Dirk van Oosterhout 21.06.2016 Use of MWD data for ... - Unit

Dirk van Oosterhout 21.06.2016

Use of MWD data for detecting discontinuities

Use of MWD data for detecting discontinuities By

D. van Oosterhout

in partial fulfilment of the requirements for the degree of

Master of Science in Geo-Engineering

at the Delft University of Technology,

to be defended publicly on Thursday June 30, 2016 at 12:00.

Supervisor: Dr. Ir. D.J.M. Ngan-Tillard TU Delft Thesis committee: Prof. Dr. G. Bertotti TU Delft

Dr. J. Benndorf, TU Delft Prof. Dr. A. Bruland NTNU Dr. Ir. P.D. Jakobsen NTNU Ir. M.L Arntsen, Norwegian Public Roads Administration

An electronic version of this thesis will available at http://repository.tudelft.nl/.

http://repository.tudelft.nl/

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Abstract

In the last decades, measurement while drilling, or MWD, technology has set foot in the drill and blast tunnelling industry. Penetration rate, thrust, torque pressure, percussive pressure, rotation speed, water flow and water pressure are registered on a centimetre scale and processed to parameters more dependent on geology. These parameters could ideally be used to adapt the blast design for a more efficient blast and to predict the amount of rock support needed. In reality, MWD technology is often only used to document geology. The main reasons for this could be that the workflow is not fully adapted to the MWD technology, the knowledge about MWD is not sufficient or the ability of MWD to represent the geology has not been extensively investigated and verified for drill and blast tunnelling. MWD has great potential since it is a relatively cheap and simple method of collecting a larger amount of data, which does not interfere with the workflow in drill and blast tunnelling. The aim is to investigate the ability to detect discontinuities and their geometrical properties in MWD data and to evaluate the usability of MWD data in terms of detecting discontinuities. The first of five objectives is a literature review on the subjects of MWD technology, discontinuities in rock masses, previous research and statistical data analysis methods. Raw MWD parameters are processed and filtered by software, which can somewhat be seen as eliminating influences such as depth dependency and percussive pressure. This results in modified penetration rate, torque or water pressure which represents rock mass properties. Previous research showed the inability to compare MWD data and available geological reporting about fractures was due to a large difference in scale. For statistical analysis of MWD data in this study 4 methods are considered. Principle component analysis and k-means cluster analyses are forms of unsupervised learning and can potentially help to understand and reduce complexity of multivariate datasets. Linear and logistic regressions are forms of supervised learning that can give insight into predictability and potentially predict the presence discontinuities. The second objective is to gather data, which is appropriate and detailed enough to study relations between MWD data and discontinuities. Two tunnels under construction are visited where limestone and highly foliated phyllite are the dominant rock types. These are the Solbakktunnelen and Bjørnegårdtunnelen. While not disturbing the construction work, different methods of detailed geological mapping are used. Blasthole remains used as scanlines for mapping discontinuities and mapping discontinuities in the contour and face led to a dataset reflecting the geological situation in tunnels. Another method for mapping discontinuities, which is not influenced as much by blasting, is borehole inspection. The use of an inspection camera and an optical televiewer resulted in 11 video footages and 25 detailed recordings of 5 meter long boreholes. The third objective is to evaluate the data by a visual comparisons of geological data and MWD data. Comparing the mapped geology and 3D images of MWD data showed that fractures with a certain infill or aperture are visible in MWD data. The more detailed geological data showed that an open fracture or a fracture with soft infill and an aperture wider than 1cm often leads to a peak in penetration rate, rotation pressure, processed penetration and processed rotation pressure. The fourth objective is to apply statistical methods to come to a more in depth understanding of the relation between MWD data and discontinuities and confirm findings in objective 3. Since it is suspected that only the actual location and aperture can be predicted, a vector is made by assigning the number 1 to each MWD sampling depth between the upper and lower boundary of a discontinuity. For intact rock, a 0 is assigned to each MWD sampling depth. The principle component analyses showed that the dataset could be reduced to a manageable number of 5 to 7 components and that around 80% of the variability was retained. K-means cluster analyses is found to be an appropriate analysis for 2 out of 4 datasets. It led to the understanding that responses in MWD data due to lithological discontinuities without an aperture cannot be separated from intact rock. Training the data with logistic regression

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analyses confirmed this finding. Logistic regression did however differentiate MWD data of open fractures from other MWD data in half of the collected data. 2 out of 3 fractures were predicted, but the exact location and size of predicted fractures differ slightly from the real location. The regression for the fractures with soft infill was successful for a quarter of the data. 6 out of 9 fractures with clayey infill were predicted, again with a small deviation in size. Testing the regression equations on test datasets, which were not part of the input for data training, did not lead to the correct prediction of fractures. The last objective is to discuss the current and future usability of MWD data for the Norwegian Public Roads Administration. Even though the statistical analyses did not fully succeed in separating responses of fractures in MWD data, visually responses are found to be characteristic. The fact that not all discontinuities give a distinctive response, makes calculations of rock quality designation unreliable. Therefore, in terms of rock support, MWD data can only assist in decision making concerning spot bolting to secure wedges due to large fractures. This research might contribute to help understand and predict grout volumes. The use of 3D images of MWD data could give a better understanding of the in situ fracture structure. Ideally, this knowledge can be used to anticipate possible under- and overbreak due to these fractures before blasting. Considering the findings in this study, it is still presumed that MWD technology has great potential even if it might not lead to the prediction of each type of discontinuity.

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Acknowledgments

During this project I got help of many different parties and individuals. Firstly I would like to thank every

Norwegian for showing patience with my Norwegian language and not shifting to English too easily. You

have contributed a great deal to my Norwegian language skills.

I would like to thank the team from the Norwegian Public Roads Administration working on the

Bjørnegårdtunnelen and Marco Filipponi from Marti AS at the Solbakktunnelen site. Not only has

gathering of data been essential for my thesis, it was special to learn about tunnel construction outside

lecture rooms.

The help of Thorvald Wetlesen from Bever Control AS has been crucial to get to this result. Thanks for

your openness in MWD processing method, assistance with gathering MWD data and support with data

management. In addition, I would like to thank Harald Elvebakk from NGU for driving a long way to

provide the televiewer service.

Essential for me in writing this thesis have been five academics and one engineer from the Norwegian

Public Roads Administration. I would like to thank Amund Bruland, Pål Drevland Jakobsen and Mari Lie

Arntsen for providing data and contacts, taking care of finances, giving valuable feedback and much

more. It means a great deal to me to be welcomed as an outsider and be given this much trust. At TU

Delft I would like to thank Giovanni Bertotti for support in the geological field and Jörg Benndorf for

support with data analysis. I want to express special thanks to Dominique Ngan-Tillard who has been

my direct supervisor. She has supported me through the last years in my choices of academic ambitions.

Overall, the supervision has been perfect, giving me the freedom to steer my own project.

To finish I would like to express my enormous gratitude towards my family and Kari. Thanks!

Delft, University of Technology Dirk van Oosterhout

June 5th, 2016

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Table of content

Abstract .................................................................................................................................................... I

Acknowledgments .................................................................................................................................. III

Table of content ..................................................................................................................................... IV

List of figures ........................................................................................................................................ VIII

List of tables ............................................................................................................................................ X

1. Introduction ......................................................................................................................................... 1

1.1 MWD technology in drill and blast tunnelling ............................................................................... 1

1.2 Drill and blast tunnelling ................................................................................................................ 1

1.3 The Norwegian Public Roads Administration ................................................................................ 2

1.4 Problem definition, aim and objectives ......................................................................................... 3

1.5 Research questions ....................................................................................................................... 4

1.6 Limitations 5

1.7 Scope and constraints ................................................................................................................... 6

1.8 Thesis outline ................................................................................................................................ 6

2. Literature study ................................................................................................................................... 7

2.1 MWD technology........................................................................................................................... 7

2.2 Discontinuities and discontinuity mapping .................................................................................... 7

2.3 The drilling process ........................................................................................................................ 8

2.4 MWD responses to discontinuities ................................................................................................ 9

2.5 Common practise of geological mapping in Norwegian tunnels ................................................... 9

2.6 Data processing ........................................................................................................................... 10

2.7 Multivariate statistics .................................................................................................................. 13

2.7.1 Principle Component Analysis .............................................................................................. 13

2.7.2 K-means cluster analysis ....................................................................................................... 15

2.7.3 Multiple linear regression analysis ....................................................................................... 16

2.7.4 Multiple logistic regression................................................................................................... 17

2.7.5 Overview of statistical analyses ............................................................................................ 20

2.8 Correlation between MWD and geology ..................................................................................... 20

2.8.1 Grouting volumes and MWD data ........................................................................................ 20

2.8.2 Geological boundaries and zones of weaknesses ................................................................. 21

2.8.3 Mechanical properties of rock and MWD data..................................................................... 22

2.8.4 Mapped geology and MWD data .......................................................................................... 22

2.8.5 Findings in research in the field of MWD technology ........................................................... 22

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3. Data gathering and collected data ................................................................................................ 23

3.1 Preparation of fieldwork at Bjørnegårdtunnelen ........................................................................ 23

3.1.1 Project .................................................................................................................................. 23

3.1.2 Geology ................................................................................................................................ 23

3.2 Preparation of fieldwork at Solbakktunnelen .............................................................................. 24

3.2.1 Project .................................................................................................................................. 24

3.2.2 Geology ................................................................................................................................ 24

3.3 Geological mapping ..................................................................................................................... 25

3.3.1 Tunnel wall mapping ............................................................................................................ 25

3.3.2 Borehole inspection .............................................................................................................. 26

3.4 Fieldwork at Bjørnegardtunnelen ................................................................................................ 26

3.4.1 Tunnel wall mapping and MWD data ................................................................................... 27

3.4.2 Borehole inspection and MWD data .................................................................................... 30

3.4.3 Overview of collected data ................................................................................................... 34

3.5 Fieldwork at Solbakktunnelen ..................................................................................................... 34

3.5.1 Tunnel wall mapping and MWD data ................................................................................... 34

3.5.2 Borehole inspection and MWD data .................................................................................... 35

3.5.3 Overview of collected data ................................................................................................... 36

4. Visual validity assessment of MWD data ........................................................................................... 37

4.1 Tunnel wall mapping data ........................................................................................................... 37

4.1.1 Overview of observations in MWD data and mapped geology ............................................ 39

4.2 Televiewer data ........................................................................................................................... 39

4.2.1 Data preparation .................................................................................................................. 39

4.2.2 Borehole 1 ............................................................................................................................ 40

4.2.3 Borehole 2 ............................................................................................................................ 40

4.2.4 Borehole 3 ............................................................................................................................ 41

4.2.5 Borehole 4 ............................................................................................................................ 41

4.2.6 Borehole 5 ............................................................................................................................ 42

4.2.7 Borehole 6 ............................................................................................................................ 42

4.2.8 Borehole 7 ............................................................................................................................ 42

4.2.9 Borehole 8 ............................................................................................................................ 43

4.2.9 Borehole 9 and 10 ................................................................................................................ 43

4.2.10 Borehole 11 to 15 ............................................................................................................... 43

4.2.11 Borehole 16 to 20 ............................................................................................................... 44

4.2.12 Borehole 21 to 25 ............................................................................................................... 45

4.2.13 Overview of observations in MWD data and mapped geology with televiewer ................. 46

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4.3 Borehole camera data ................................................................................................................. 46

5. Statistical validity assessment of MWD data ..................................................................................... 48

5.1 Data sets 48

5.2 Principle component analysis ...................................................................................................... 50

5.2.1 Determining the number of principle components .............................................................. 50

5.2.2 Orthogonal and oblique rotation .......................................................................................... 51

5.2.3 PCA outcome dataset 1 ........................................................................................................ 52




5.2.7 Overview of PCA analyses ..................................................................................................... 59

5.3 Cluster analysis ............................................................................................................................ 59

5.3.1 K-means cluster analysis ....................................................................................................... 59

5.3.2 Cross tabulation clusters and discontinuity groups .............................................................. 60

5.3.2.1 Dataset 1 ....................................................................................................................... 60

5.3.2.2 Dataset 2 ....................................................................................................................... 62

5.3.2.2 Dataset 3 and 4.............................................................................................................. 62

5.3.3 Overview of cluster analysis outcome .................................................................................. 63

5.4 Logistic regression ....................................................................................................................... 63

5.4.1 Training dataset 1 ................................................................................................................. 63

5.4.1.1 All discontinuities .......................................................................................................... 63

5.4.1.2 Discontinuities with an aperture>0 ............................................................................... 64

5.4.1.3 Discontinuities with an aperture>0 and no infill ............................................................ 64










5.4.4.1 Discontinuities and with an aperture>0 ........................................................................ 67

5.4.4.2 Discontinuities with an aperture>0 and soft or no infill ................................................ 67


5.4.5 Summary of logistic regression analyses .............................................................................. 68

VII

5.5 Regression equation .................................................................................................................... 69

5.5.1 Testing regression outcome from dataset 1 ......................................................................... 69




5.5.5 Overview of predicted discontinuities .................................................................................. 70

6. Discussion .......................................................................................................................................... 71

6.1 Literature study ........................................................................................................................... 71

6.2 Data gathering and collected data .............................................................................................. 71

6.3 Visual validity assessment of MWD data ..................................................................................... 71

6.4 Statistical validity assessment of MWD data ............................................................................... 72

6.5 Current usability of MWD ............................................................................................................ 74

7. Conclusion and recommendations .................................................................................................... 75

Research questions ........................................................................................................................... 75

Recommendations ............................................................................................................................ 77

Bibliography .......................................................................................................................................... 79

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List of figures

Figure 1. Illustration of the drill and blast cycle, starting in the top with drilling and loading and

continuing with blasting, ventilating, mucking, scaling, installing rock support and surveying (Heiniö,

1999). ...................................................................................................................................................... 2

Figure 2. Top view of tunnelling sites and tunnel section with in the top Solbakktunnelen and below the

Bjørnegårdtunnelen (The Norwegian Public Roads Administration, 2014). ............................................ 4

Figure 3. Illustration of primary geometrical properties of discontinuities in rock (Hudson & Harrison,

1997). ...................................................................................................................................................... 8

Figure 4. Illustration of feed, rotation and percussion impact for the top hammer drilling method (Olsen

V. , 2009). ................................................................................................................................................ 9

Figure 5. 2D documentation of discontinuities by the Norwegian Public Roads Administration (Lillevik,

2011). .................................................................................................................................................... 10

Figure 6. Two dimensional illustration of Principle Component Analysis (Powell & Lehe, u.d.). ........... 14

Figure 7. Illustration of binary outcome in a linear regression scatter plot. .......................................... 17

Figure 8. The logit function of logistic regression. ................................................................................. 17

Figure 9. Illustration of how Hjelme placed boreholes in a spread out tunnel contour sheet for

comparing MWD data to mapped geology. .......................................................................................... 21

Figure 10. Map of southern Norway with tunnel site locations. ........................................................... 23

Figure 11. Cross sections of geology encountered with constructing the two tunnel profiles (GEOVITA,

2014). .................................................................................................................................................... 24

Figure 12. Geological map with Solbakktunnelen section, the yellow line is approximately 2km

(Multiconsult, 2009). ............................................................................................................................. 25

Figure 13. Illustration of an optical televiewer (NGU, 2015). ................................................................ 26

Figure 14. Mapping at tunnel wall in tunnel A at profile 955 of an injection borehole remain. Person

length is 1.75m. ..................................................................................................................................... 27

Figure 15. Graphical representation of calcite infilled discontinuity in tunnel A at profile 505. ........... 28

Figure 16. Graphical approximation of positions of discontinuities seen along tunnel face 975 in tunnel

tube A. ................................................................................................................................................... 28

Figure 17. View along the tunnel face 975 tunnel A. The packers are separated by approximately 1.2m

(top left), the packers stick approximately 30 to 40 cm out of the wall (top right), the rock bolts in the

shotcrete have a diameter of 20cm (lower right).................................................................................. 29

Figure 18. Approximate location holes 1 and 2 drilled with angles of 20° to 30° from the tunnel axis. 30

Figure 19. Approximate location holes 3 to 10 drilled with angles of 55° and 90° from the tunnel axis.

.............................................................................................................................................................. 31

Figure 20. Approximate location holes 11 to 15 close to 975 and holes 16 to 20 close to 960, drilled with

angles of 40° to 60° from the tunnel axis. ............................................................................................. 31

Figure 21. Approximate location holes 21 to 25 drilled with angles of 20° to 30° from the tunnel axis.

.............................................................................................................................................................. 32

Figure 22. Graph showing drops in feeder pressure due to jamming of the drill bit caused by difficulties

with aligning drilling direction of the large hole. ................................................................................... 33

Figure 23. The inspection camera and portable computer. .................................................................. 35

Figure 24. Screenshots of typical video footages. ................................................................................. 36

Figure 25. View from underneath the tunnel, or the tunnel floor, of RMS normalised penetration with

a moving average of 4 values showing possible locations of mapped discontinuities. ......................... 38

Figure 26. Top view of RMS normalised penetration with a moving average of 4 values of the contour

showing possible locations of mapped discontinuities. ........................................................................ 38

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Figure 27. Illustration of mismatch between televiewer depth and MWD data. .................................. 46

Figure 28. Zones with light coloured minerals which might give a response in MWD data. Left in

borehole 1 at a depth of 4.8m and right in borehole 4 at a depth of 3.5m. .......................................... 47

Figure 29. Scree plot for dataset 1 to determine the number of statistically significant components. . 51

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List of tables

Table 1. Over view of statistical analyses. ............................................................................................. 20

Table 2. Geometrical properties of discontinuities crossing an injection borehole remain close to profile

955 in tunnel A. ..................................................................................................................................... 27

Table 3. Overview of collected data at the Bjørnegårdtunnelen. .......................................................... 34

Table 4. Overview of collected data at the Solbakktunnelen. ............................................................... 36

Table 5. Overview of observations in MWD data and mapped geology. ............................................... 39

Table 6. Overview of observation in MWD data and geological data from the televiewer inspections.47

Table 7. Table showing how depth sampling of discontinuity location vector is done. ......................... 49

Table 8. Component correlation matrices with an oblique rotation for all datasets. Highlighted in green

are the highest Pearson correlations..................................................................................................... 51

Table 9. Total variance explained by principle components. ................................................................. 53

Table 10. Pattern matrix giving the correlation between parameters and principle components for

dataset 1. ............................................................................................................................................... 54


dataset 2. ............................................................................................................................................... 56


dataset 3. ............................................................................................................................................... 57


dataset 4. ............................................................................................................................................... 58

Table 14. Overview of PCA analyses. ..................................................................................................... 59

Table 15. The outcome of Variance Ration Criterion. ........................................................................... 59

Table 16. Cross tabulation of MWD clusters and discontinuity groups for dataset 1. ........................... 61

Table 17. Cross tabulation of principle component clusters and discontinuity groups for dataset 1. ... 61

Table 18. Cross tabulation of MWD clusters and discontinuity groups for dataset 2. ........................... 62

Table 19. Cross tabulation of PC clusters and discontinuity groups for dataset 2. ................................ 63

Table 20. Overview if cluster analysis outcome. .................................................................................... 63

Table 21. Outcome of sensitivity analysis of regression analysis with discontinuities with an aperture >0

and no infill. ........................................................................................................................................... 65

Table 22. Outcome of sensitivity analysis of regression analysis with discontinuities with an aperture >0.

.............................................................................................................................................................. 65


and no infill. ........................................................................................................................................... 66


and no infill. ........................................................................................................................................... 67


and soft or no infill. ............................................................................................................................... 68


and no infill. ........................................................................................................................................... 68

Table 27. Overview of the best predicting models which meet the Wald statistics and Hosmer and

Lemeshow test. ..................................................................................................................................... 69

Table 28. Overview of predictions in the test datasets. ........................................................................ 70

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1. Introduction

In this first chapter readers will be introduced to MWD technology, tunnelling methods and the

company which assigned this thesis. Next, the problem statement and objectives will be given followed

by research questions and limitations.

1.1 MWD technology in drill and blast tunnelling

In the beginning of the 20th century, Schlumberger pioneered with downhole logging for the oil industry

with the aim of increasing the understanding of geology in oil fields. In the 1970’s the technology was

introduced in the mining industry to detect ore bodies (Valli, 2010). In the last decades MWD has also

set foot in the tunnelling industry and since 1997 MWD tools are equipped standardly on drilling rigs.

MWD stands for measurement while drilling and it is in principle the registration of drilling parameters.

These drilling parameters are processed. The raw and processed drilling parameters are interpreted to

predict geology and improve drilling efficiency, this is often called Drill Parameter Interpretation. MWD

technology in tunnelling leads to large amounts of data. Nowadays computers and software packages

make it possible to process this data and visualize it in 3D. Ideally, these outcomes are used to adapt

the blast design for a more efficient blast and to predict the amount of rock support needed (Nilsen &

Palmstrom, 2013). In reality, it is often only used to document geology.

Nevertheless, MWD technology has great potential as it is a relatively cheap and simple method of

collecting a larger amount of data, which does not intervene with the workflow in drill and blast

tunnelling. In the next section the use of MWD technology in drill and blast tunnelling is introduced.

1.2 Drill and blast tunnelling

Two commonly used tunnelling techniques are tunnelling with a tunnel boring machine and drill and

blast tunnelling. Both involve measurement while drilling, but this thesis is merely focussed on drill and

blast tunnelling. Drill and blast tunnelling consists of a series of actions, where each cycle typically ends

with a 5 meter tunnelling advance depending on rock mass quality and blast vibrations. It starts with

drilling blast holes, usually of 5m long. Several tens of holes are drilled, but an average number of holes

is hard to give since the number of holes depend on the rock, wanted perfection of tunnel contour and

tunnel dimensions. MWD data is collected for each blast hole. The next step is charging and blasting.

After ventilation to reduce dust and dangerous gas concentrations, the blasted rock is transported out

of the tunnel by trucks or a conveyor belt. At this stage the rock mass might still be unstable so the rock

surfaces are scaled by machine and hand to remove loose hanging blocks. After this, it is up to the

geologist or engineering geologist and the contractor to decide what kind of rock support will be used

to support the rock on the long term. In Norway this is often called ‘byggherrens halvtime’, which

directly translated means ‘the client’s half hour’. It is a contracted activity where no other activities

should take place than geological mapping and deciding on rock support. The next step in this process

is the installation of the rock support, which can be bolts, reinforced ribs of sprayed concrete, spiling

bolts, reinforced shotcrete or a combination of these support types. Often these are installed directly

after removal of blasted rock. In some cases these types of rock support are temporary. Permanent

rock support might be the installation of additional rock support or cast in place concrete lining. The

final step is a survey to prepare the next cycle. The cycle is illustrated in Figure 1. Tunnelling is an

expensive activity, time efficiency is therefor of the essence.

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As will be discussed later, this thesis will be mainly focused on the detection of discontinuities. If

discontinuities can be identified during probe or blast hole drilling this could help in different fields.

Fracturing has great influence on the rock mass quality and therefore the amount of rock support

needed. For example, a fractured rock with small discontinuity spacing can require multiple forms of

rock support like shotcrete, bolts, spiling bolts etc. A rock mass with multiple discontinuity sets could

lead to the formation of wedges, which can collapse. Large wedges can be supported by spot bolting.

Another form of rock support is injection of grout. Grout injection is done to increase the water

tightness. Large volumes of grout are injected in fractures in rock masses. The understanding of where

and how rocks are fractured could potentially help to predict the volume of grout that is needed.

Another example when detection of large discontinuities can be useful is to prevent under- and

overbreak. Excavating too much or too little rock would require additional shotcrete or reblasts to get

the desired tunnel contour. Fracturing in the rock mass is one of the causes of under- and overbreak.

To summarize, detection of discontinuities before excavation would help to characterize rock masses

early and to anticipate to these rock mass conditions before excavation.

1.3 The Norwegian Public Roads Administration

The Norwegian Public Roads Administration, or NPRA, is responsible for planning, construction and

operation of the national and county road network. This road network also includes about 1000 road

tunnels with an added length of about 800km. Two tunnels which are assigned by the NPRA have been

visited to collect data for this thesis: Solbakktunnelen and Bjørnegårdtunnelen. In Figure 2 illustrations

of both tunnels are given. The Solbakktunnel will be a 14.3km tunnel, which will make it world-longest

subsea road tunnel with the deepest point at 290m below sea level. The Bjørnegårdtunnel will be a

2.3km tunnel and it is built to reduce traffic in Sandvika. This thesis is written in collaboration with the

Norwegian Public Roads Administration.

Figure 1. Illustration of the drill and blast cycle, starting in the top with drilling and loading and continuing with blasting, ventilating, mucking, scaling, installing rock support and surveying (Heiniö, 1999).

3

1.4 Problem definition, aim and objectives

MWD technology in drill and blast tunnelling is rather new. Like mentioned before, it is often only used

to document geology while it has the potential to improve the drill and blast tunnelling process. The

main reasons for this could be that MWD technology is not fully adapted to the drill and blast workflow,

the knowledge about MWD is not sufficient or the ability of MWD to represent the geology has not

been extensively investigated and verified. Apart from rock hardness, the little amount of MWD

research in fracturing and water conditions have not shown overwhelming results. Concerning

fracturing, one main obstacle, which has been encountered in previous research, is the difference in

scale between MWD data and available geological information. MWD data has a typical sampling

interval of 2 to 5cm. The most detailed geological information available from tunnel sites is often

mapped geology by engineering geologist. Since the objective of this geological mapping is to document

geology and agree on rock support types, a cm scale accuracy is not relevant and far too time consuming

in the drill and blast tunnelling. Only large structures like intrusions, rock type changes and large

discontinuities are mapped. This is often done by different engineers and locations of structures are

estimated. This might be sufficient for the objective of the geological mapping, but for comparing

geology and MWD data, this is too inaccurate and unprecise. The Norwegian Public Roads

Administration spends money on facilitating MWD software and MWD services, while the ability of

some parts of MWD technology has not been verified. The Norwegian Public Roads Administration

defines the use of MWD for their projects in their process code (The Norwegian Public Roads

Administration, Prosesskode 1 - Standard beskrivelsetekster for vegkontrakter, hovedprosess 1-7.,

2012). It is stated that all drilling rigs must be equipped with logging equipment and used for at least

90% of the borehole length.

The aim of this thesis is to investigate the ability to detect discontinuities and their geometrical

properties in MWD data and to evaluate the usability of MWD data in terms of detecting discontinuities

for the NPRA as the client in future tunnelling projects.

Five objectives are established for this project.

1. Firstly, the subjects of MWD technology, discontinuities in rock masses and previous research

need to be studied. In addition, the literature study should be focussed on possible statistical

data analysis methods, which can be used to evaluate MWD data.

2. The second objective is to gather data, which is appropriate to study relations between MWD

data and discontinuities. This objective will include preparation and execution of fieldwork.

3. The third objective is to evaluate the data by visual comparisons of geological data collected

during fieldwork and MWD data.

4. The fourth objective will be to apply statistical methods to come to a more in depth

understanding of the relation between MWD data and discontinuities and confirm findings in

objective 3.

5. The last objective will be to discuss the current and future usability of MWD data for the NPRA.

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1.5 Research questions

The main research question is: To what extent can discontinuities be detected by MWD data and used for reporting geology in a tunnel, predicting geology ahead of the tunnel face and predicting the amount of rock support needed? This will be answered by achieving the listed objectives and answering the following research questions.

1. Conduct a literature study of the subject MWD technology, discontinuities in rock masses, statistical analysis that can be used to evaluate MWD data and previous research in the subject of MWD validity. - What is MWD technology in drill and blast tunnelling? - What kind of discontinuities can be encountered? - How is geology mapped in tunnels by the Norwegian Public Roads Administration? - How is MWD data filtered and processed? - What statistical analyses are appropriate for analysing MWD data to enhance and

understand the predictability of discontinuities? - What is the result of previous research on the validity and usability of MWD?

Figure 2. Top view of tunnelling sites and tunnel section with in the top Solbakktunnelen and below the Bjørnegårdtunnelen (The Norwegian Public Roads Administration, 2014).

5

2. Prepare site visits and conduct fieldwork. - What is the local geology of the tunnel sites? - What methods can be used to map discontinuities in tunnels? - What are the parameters to obtain during fieldwork? - To what extent is it possible to work on sites where the degree of fracturing is the only

variable parameter? - What is the quality of the collected data?

3. Assess the validity of MWD data by visually comparing the geological data and MWD data. - What kind of discontinuities are visible in MWD data or what kind of geometrical properties

make discontinuities visible in MWD data? - Is there a characteristic response of MWD data to discontinuities?

4. Assess the validity of MWD data through statistical analyses. - What geological data can be used for statistical analyses? - How should geological data be translated to statistical input? - Are the statistical analyses appropriate for the MWD data collected? - Can discontinuities and/or certain geometrical properties be predicted by use of statistical

methods? 5. Discuss the current and future usability of MWD data for the Norwegian Public Roads

Administration and contractors - To what extent are discontinuities visible in MWD data? - In which way could the outcome of this study be used to improve the process of drill and

blast tunnelling ? - What significance do detected discontinuities have for rock support determination?

1.6 Limitations

In advance it can be stated that performing fieldwork in tunnels under construction does not allow

much freedom. An outsider is limited in where he or she can be mapping geology and how much time

he or she may get. Both client and contractor are bound to contracts, which involve large amounts of

money. It is therefore unwanted to disturb the tunnelling processes. By good communication and

planning, these limitation will be minimized.

Next to that the conditions under which geological mapping is done in a tunnel are different from above

ground. Artificial light, accessibility and noise could influence the ability of gathering data. As much as

possible, these limitations should be taken into account when designing data gathering methods and

preparing fieldwork. Moreover, there is always a possibility that rock masses do not show a wide variety

of discontinuities. This limitation cannot be foreseen.

The most important limitation is that no matter how interesting a fresh rock contour or face might be,

it cannot be studied in an unsafe situation, both in terms of rock stability and construction activities.

This will always be discussed before entering tunnels with client and contractor and during data

gathering with staff.

A limitation with the use of MWD technology is that a misconception might rise that MWD data gives

all the information you need to know about rock mass conditions. This is incorrect. Geological mapping

gives a much better idea of the variability of the geology than MWD technology can. As a result, the

aim of this study is not to replace geological mapping, but to support the geologist and improve drill

and blast tunnelling.

6

1.7 Scope and constraints

This thesis is mainly focused on the detection and prediction of discontinuities from MWD data.

Objective 5, the actual usefulness of the results of this study, is covered in much less detail. Although it

would be interesting, the scope of this study does not cover the implications of discontinuity detection

on the drill and blast workflow and costs. It also does not aim to predict discontinuities based on MWD

data for an entire tunnelling project. The main reason that this study does not aim to cover the two

mentioned points is that these require extensive knowledge about the extent to which discontinuities

can be detected. The available sources of information do not give this knowledge. In fact, none of the

available resources attempted to study discontinuities and MWD data as detailed as is attempted with

this study.

1.8 Thesis outline

This thesis starts with a general introduction to MWD technology, including problem definition, aim and

objectives, research questions and limitations. Chapter 2 provides background information of all the

different aspects involved in this project. MWD technology, discontinuities and discontinuity mapping,

the drilling process, common practise of geological mapping in Norwegian tunnels, data analysis,

multivariate statistics and previous research in the correlation between MWD technology and geology

is summarized in the literature study.

Chapter 3 includes introductions to the tunnel sites visited for data gathering: Solbakktunnelen and Bjørnegårdtunnelen. This is followed by descriptions of the preparations for data gathering, a short summary of the fieldwork and the results of the fieldwork. Next, the data is analysed. In chapter 4 this is done visually. Here, a mismatch between scales of geological mapping and MWD data is still allowed. In chapter 5, only the detailed geological records are used for a statistical analysis. In the discussion the potential of MWD technology in the field of fracturing is discussed. In the conclusion the findings and answers to the research questions will be summarized. Furthermore, the main limitations and their possible solutions will be discussed and recommended.

7

2. Literature study

Here, the literature study on the subjects MWD technology, discontinuities in rock, the drilling process,

geological mapping in Norwegian tunnels, data processing, statistical analyses and previous research in

the field of MWD is summarized. The 4 main sources of literature are the library at NTNU, the library at

TU Delft, a database of the Norwegian Public Roads Administration and the internet.

2.1 MWD technology

Like stated, MWD technology deals with recording drilling parameters and processing these

parameters. In the past it used to be only penetration rate, which was measured, whereas today the

systems installed on the drilling rigs register 9 parameters: Penetration rate, thrust, torque pressure,

percussive pressure, rotation speed, water flow, water pressure, depth and sampling time. The drilling

rigs and measuring systems are calibrated either at the site or by use of previous projects. The

parameters are filtered and processed by software to visualize measures of the hardness, fracturing

and water conditions. Some software packages put more focus on defining these three outcomes, while

other software leaves it to the user to interpret the different outcomes.

The amount of data collected with one advance round can be enormous. For example, if one advance

round consists of 80 blast holes, all 9 parameters plus 6 processed parameters are stored. With a

common sampling interval of 2cm, this gives 300000 data points.

2.2 Discontinuities and discontinuity mapping

A discontinuity is defined by Hudson and Harrison as ‘’any separation in the rock continuum having

effectively zero tensile strength.’’ (Hudson & Harrison, 1997). Hudson and Harrison state that

discontinuities can be the single most important factor governing the deformability, strength and

permeability. Therefore, discontinuities can critically effect the stability of a tunnel. There are different

type of discontinuities. In this thesis, four different types of discontinuities are considered (Shanghal &

Gupta, 2010).

1. Lithological discontinuities, the layering in sedimentary rock.

2. Foliation, discontinuous planes caused by parallel alignment of minerals due to stress.

3. Fracture, discontinuous planes where stress caused partial loss of cohesion.

4. Faults and shear zones, discontinuities caused by movement due to shear stress.

5. Other geological discontinuities, for example intrusive contact and veins.

In addition, there can be blast induced fractures present.

Several geometrical properties are defined. These include spacing, orientation, persistence,

termination, roughness, aperture, discontinuity sets and presence of water (see Figure 3).

Spacing is the distance between discontinuity intersections with a scanline. Orientation is defined as

the dip direction, the horizontal angle between the normal of the discontinuity plane and north, and

dip angle of the plane. Persistence is the length or size of a discontinuity plane. Termination is the

ending of a fracture due to another fracture. Aperture is the perpendicular distance between two

adjacent discontinuities.

In practise, it would be too time consuming and costly to map these properties for all discontinuities in

a tunnel under construction. Several classification systems are designed to classify the quality of the

rock. In the Norwegian tunnelling industry, the Q-system is used. The Q-systems gives a value between

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0.001 and 1000, which indicates the quality of the rock (NGI, 2013). This value can also be used to

estimate the amount of rock support needed. The Q-value is a computation of six different parameters:

𝑄 =𝑅𝑄𝐷

𝐽𝑛∗

𝐽𝑟

𝐽𝑎∗

𝐽𝑤

𝑆𝑅𝐹

Rock Quality Designation, or 𝑅𝑄𝐷, is the sum of the length of all core pieces longer than 10 cm as a

percentage of the total length of the core. It can also be estimated by counting the number of fractures

per m3. 𝐽𝑛 is the joint set number, which represents the number of joint sets and random joints.

Together the first term represents the relative block size. 𝐽𝑟 is the joint roughness number representing

both small scale roughness (rough, smooth and slickenside) and large scale roughness (stepped,

undulating and planar). 𝐽𝑎 represents the joint friction or shear strength. The second term is an

approximation of the actual friction angle. 𝐽𝑤 is the joint water reduction factor which represents the

presence of water and 𝑆𝑅𝐹 is the stress reduction factor which represents the relation between rock

strength and stress.

2.3 The drilling process

In the drill and blast tunnelling industry the drilling method used is top hammer drilling. It is a

combination of applying feed, rotation and percussion impact transmitted through the drilling steel

(Figure 4) (Olsen V. , 2009). All the energy is transferred through the drilling string. The drill bit is

covered with several indenters, which are either small hemispheres or cones. If an indenter is forced in

to the rock, the stress in the rock increases and a zone around the indenter is deformed plastically. A

small zone directly under the indenter is crushed to rock powder. Just outside the zone where rock is

pulverized the stress can reach the peak strength and the rock can break. In a larger hemisphere around

the indenter, cracks are induced and some of these cracks can find a path out to the free surface. This

is called chip formation (Olsen V. , 2009). The indenters on a drilling bit are forced in the rock by the

percussion pressure. The position of the indenters is changed continuously by the rotation and the

drilling bit is kept in contact with the rock by the feed pressure.

Thrust, rotation speed and percussion pressure are parameters which are controlled by the operator

or automatic drilling system. Therefore, these parameters are independent. The drilling system is

programmed to drill as fast as possible without jamming the drill bit. Jamming can for example be

Figure 3. Illustration of primary geometrical properties of discontinuities in rock (Hudson & Harrison, 1997).

Termination

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caused by a too high thrust. In that case, the contact of the drill bit with the rock is continuous and

rotation and indentation are obstructed. The dependent parameters are penetration rate and torque.

These are dependent on the independent parameters and are the possible cause of variation.

2.4 MWD responses to discontinuities

The drilling behaviour when crossing an open discontinuity has been researched. Schunnesson

summarises the findings of Barr in 1984 in his PhD thesis (Schunnesson H. , 1996). Most or all of the

following events happened in an artificial set-up of rock blocks including discontinuities with known

orientation aperture and infilling.

When a discontinuity was met, the following was observed:

- A short increase in penetration rate.

- A small increase in rotational speed followed by a sharp decrease when re-establishing rock

contact.

- A drop in torque pressure followed by sharp increase on re-establishing rock contact.

- A drop in water pressure until equal pressure in the void or rock is established.

- A drop in feed pressure followed by a sharp increase on re-establishing rock contact. This might

only be visible for discontinuities with a large aperture.

An infilled fracture can dampen the events. The most reliable event is the increase in penetration rate.

Schunnesson found in his own research that the intensity of fracturing can cause the penetration rate,

torque pressure and rotational speed to increase.

2.5 Common practise of geological mapping in Norwegian tunnels

In the Norwegian tunnelling industry, it is common that geological inspection and documentation is

done by a geologist or an engineering geologist employed by the client. After the removal of blasted

rock and the cleaning of the surfaces for the application of shotcrete, the client has a 30 to 45 minutes

window, in which there is no other activity in the tunnel, to document the rock mass properties

(Rongved, u.d.). In common practise this means the documentation of the Q-values, classification of

rock types, mapping of fractures. This is an interactive process between client and contractor. When

rock mass quality is high, the geological mapping is often done during operation and the drill and blast

activities are not put on a hold. Afterwards the client stores the information digitally. The Norwegian

Public Roads Administration uses the program Novapoint from Vianova Systems AS which gives the

options to document foliation, discontinuities, joint sets, joints with infill, weakness zones < 1m,

weakness zones > 1m, rock type, Q values and water leakage (Vianova Systems AS, 2011). Novapoint

gives a 2 dimensional representation of the exposed tunnel contour, meaning that the (engineering)

geologist has to document in 2 dimensions. The tunnel contour is split in four parts, shown as dashed

Figure 4. Illustration of feed, rotation and percussion impact for the top hammer drilling method (Olsen V. , 2009).

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lines parallel to the tunnel axis in Figure 5. The tunnel floor is not included in mapping. The geological

features are transformed from the 3 dimensional space to the 2 dimensional as illustrated in Figure 5.

The Norwegian Public Roads Administration defines this work facet in their process code (The

Norwegian Public Roads Administration, Prosesskode 1 - Standard beskrivelsetekster for vegkontrakter,

hovedprosess 1-7., 2012). Documentation and mapping of the geology is done to help planning rock

support installation, to have documentation of geology and to see the link between geology and

installed rock support.

2.6 Data processing

The common practise of processing MWD data is based on a doctoral thesis written by Schunnesson.

Schunnesson aimed to develop a method for analysing MWD data and reducing the need for calibration

of rock properties against MWD data (Schunnesson H. , 1996) (Schunnesson H. , 1997) (Schunnesson &

Holme, 1997) (Schunnesson & Sturk, 1997). The data used in his thesis came from the Viscaria mine,

the Zinkgruvan mine, the Glodberget tunnel and the Hallandsåsen tunnel. Schunnesson proposed a

method to separate the two main groups of parameters, namely the independent parameters, which

are feeder pressure, rotation speed and percussion pressure, and dependent parameters, which are

penetration rate and torque pressure. First, the length dependency along the boreholes should be

taken into account for all parameters. Length dependent behaviour is caused by for example the

reflection of energy with the addition of each drilling rod, increase in friction with the rock and the

decrease of flushing efficiency. Next, the applied differences in thrust should be normalized for

penetration rate and torque pressure. Finally, the influence of penetration rate on torque should be

normalized. The resulting data consists of systematic variations caused by the drill rig itself. This should

be subtracted from the original data set to remain with the unsystematic variations or rock mass

variation. A straight forward way to do this is to withdraw the gradient of the trend line of every dataset

from every dataset.

Figure 5. 2D documentation of discontinuities by the Norwegian Public Roads Administration (Lillevik, 2011).

11

Schunnesson stated that after applying this method, only the rock dependent variations are left in the

data. He found that the magnitude of penetration rate and torque pressure generally are good

indicators for rock hardness and that both increase in fractured rock. However, responses based on the

magnitude of penetration rate and torque pressure differ from site to site. The variability of penetration

rate and torque pressure usually has a more general relation to fracturing. Schunnesson gave several

examples from his data that showed this. He used principle component analyses (PCA) to investigate

the correlation between penetration rate and torque pressure. For the Glodberget tunnel,

Schunnesson showed that less fractures gave a smoother curve.

There are a number of software packages which process the raw MWD data. Both normalisation and

root mean square calculations, abbreviated as RMS, are used to separate dependent and independent

variables and to amplify responses. In contrast with most software developers, Bever Control AS is open

about how raw MWD data is used to get to the three standard evaluations (hardness, fracturing and

water conditions (Håkonsen & Wetlesen, 2008) (Bever Team as, 2009)). Developers sometimes

explicitly define hardness based on a modified penetration rate, fracturing based on a modified torque

pressure and water conditions based on both modified water pressure and water flow. The developers

of Bever Team 3 leave interpretation up to the user and they do not assign the terms hardness,

fracturing or water conditions. Instead, the outcome is called normalized penetration, normalized

rotation pressure, normalized water, RMS normalized penetration, RMS normalized rotation pressure

and RMS normalized water. All outcome is expressed as %. These parameters are influenced by:

1. External influences like drill bit wear and the number of drilling rods.

2. The dependent parameter percussive pressure

3. Hole depth

4. Rock mass properties

In a way the steps explained below can be seen as eliminating influences 1, 2 and 3 to end up with a

modified penetration rate, torque or water pressure which represents rock mass properties in a better

way. The steps are generally the same for penetration rate, torque and water pressure and for

simplicity, these will be called parameter, or P, in the following steps.

Step 1. Influences which do not contain relevant information about rock properties like adding of drilling

rods are removed from the data. Generally, penetration rates lower than 1 m/min, unrealistic high

values for penetration rate and unrealistic low values for percussion pressures.

Step 2. An attempt is made to normalize the wear of the drill bit and the drilling settings. It is noted in

literature that this cannot be fully successful. The formula used to normalize hammer pressure,

penetration rate, rotation pressure, water pressure and water flow in Bever Team 3 is:

𝑃𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 =(𝑃𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑 − 𝑃𝑚𝑒𝑎𝑛)

𝑃𝑚𝑒𝑎𝑛⁄

In which the mean parameter 𝑃𝑚𝑒𝑎𝑛 is calculated over one single whole borehole length.

Step 3. After this normalization the most significant influencing factors on the penetration rate and

rotation pressure are believed to be percussion pressure, friction between drilling rods and borehole

(or depth dependency) and the rock mass property. Thrust and rotation speed mentioned as dependent

parameters by Schunnesson, are not considered by Bever Team 3. It is assumed that the relation

between the parameter, its rock mass property (𝑅𝑀𝑃) and percussive pressure (PP) is linear.

𝑃𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 = 𝑅𝑀𝑃 + 𝑘 ∗ 𝑃𝑃

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The constant 𝑘 is found by plotting the raw parameter, without the peaks of adding a rod, against the

percussion pressure and obtaining the linear trend.

Step 4. With this known constant 𝑘 the rock mass properties, or 𝑅𝑀𝑃, can be calculated by filtering out

the influence of the hammer pressure and the drilling depth from the normalized penetration. The

equation below is the result of rearranging the previous equation plus a correction for the drilling depth.

𝑅𝑀𝑃 = 𝑃𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 − 𝑘 ∗ 𝑃𝑃 +𝐶𝐻𝐿

100∗ 𝑠

Where 𝐶𝐻𝐿 is a predefined value for the correction of hole length and 𝑠 the sampling depth. It should

be noted that for an unknown reason this is not done for the normalized rotation pressure.

Step 5. The water parameter 𝑃𝑤𝑎𝑡𝑒𝑟 is calculated with the formula below.

𝑃𝑤𝑎𝑡𝑒𝑟 = 𝑃𝑤𝑎𝑡𝑒𝑟𝑓𝑙𝑜𝑤 ∗ (1 − 𝑃𝑤𝑎𝑡𝑒𝑟𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒2) ∗ 𝑊𝑃𝐹

Where 𝑃𝑤𝑎𝑡𝑒𝑟𝑓𝑙𝑜𝑤 is the water flow in l/min, 𝑃𝑤𝑎𝑡𝑒𝑟𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒 the water pressure in bar, and 𝑊𝑃𝐹 is the

water pressure factor which is a predefined value. 𝑃𝑤𝑎𝑡𝑒𝑟𝑓𝑙𝑜𝑤 is not corrected for the depth but for the

number of drilling rods since the water is pumped through all drilling rods which are in use. For example,

drilling at a depth of 7m requires 2 drilling rods of 5 meter. Water is then pumped through 10m.

Step 6. The output given as normalized penetration, normalized rotation pressure and normalized

water, or 𝑃𝑜𝑢𝑡𝑝𝑢𝑡 1,𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑, is calculated with a moving average of 𝑁 values

𝑃𝑜𝑢𝑡𝑝𝑢𝑡 1,𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 = 𝐴 − ∑ 𝑃𝑖

𝑖0+𝑁

𝑖=𝑖0

𝑁⁄

Where 𝐴 is either 𝑅𝑀𝑃, 𝑃𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 𝑟𝑜𝑡𝑎𝑡𝑖𝑜𝑛 𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒 or 𝑃𝑤𝑎𝑡𝑒𝑟, 𝑖0 is the index of the value to be

calculated, 𝑃𝑖 is the value of a parameter at index 𝑖.

Step 7. This operation influences the dynamic property of curves. A moving root mean square

calculation is done to represent the fluctuations of rock mass properties better. The formula used for

this is:

𝑃𝑜𝑢𝑡𝑝𝑢𝑡 2,𝑅𝑜𝑜𝑡𝑀𝑒𝑎𝑛𝑆𝑞𝑢𝑎𝑟𝑒

= √∑ (𝑃𝑜𝑢𝑡𝑝𝑢𝑡 1,𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 − �̅�𝑜𝑢𝑡𝑝𝑢𝑡 1,𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 𝑚𝑜𝑣𝑖𝑛𝑔 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑜𝑓 𝑁 𝑣𝑎𝑙𝑢𝑒𝑠)2𝑁

𝑁

N is the number of measurements taken for the root means square computation and it influences how

dynamic the response is to changes in the original curve. If 𝑃𝑜𝑢𝑡𝑝𝑢𝑡 2,𝑅𝑜𝑜𝑡𝑀𝑒𝑎𝑛𝑆𝑞𝑢𝑎𝑟𝑒 𝑤𝑎𝑡𝑒𝑟 is larger than

0.7 or smaller than the original water flow value, it is set to 0.

Bever Team 3 allows the user to display every individual curve of every step and 3 dimensional images

with colour scales. These colour scales can be adjusted manually.

Another method to characterize rock masses is by use of specific energy, the energy required to break

one unit volume of rock (Ghosh, 2015) (Rai, Schunnesson, Lundqvist, & Kamur, 2015) (Ghosh,

Schunnesson, & Kumar, 2014). Ghosh et all and Rai et all had the aim to determine an unique

geotechnical description of the rock mass excavated by rotary core drilling. The description of specific

energy used in their research is 𝐸 =𝐹

𝐴+

2𝜋𝑁𝑇

60𝐴𝑢, in which E is the specific energy, F is the thrust, A is the

area of the cross section of the drill bit, N is the rotary speed, T is torque and u is penetration rate. The

13

relation between specific energy and compressive strength of the rock has often been verified and is

found to be best in rock masses with changing conditions. Specific energy has not been introduced in

percussion drilling in tunnelling.

2.7 Multivariate statistics

For this thesis the Bever Team 3 software is used to extract raw and processed MWD data. It is

suspected that the moving averages used as described in section 2.6 influences the ability to detect

discontinuities. Therefore processed MWD parameters are generated with different moving averages.

This makes the number of variables even larger. To handle a large amount of variables it is chosen to

apply multivariate statistical analyses. Wuensch listed a number of available multivariate analyses

(Wuensch, 2013). In addition he stated that it is relatively easy to perform a multivariate analysis, but

not easy to interpret the outcome of such an analysis. Wuensch also warns that for each individual

method, multiple choices are to be made and therefore outcomes of analyses may differ from analyser

to analyser. The outcome of a multivariate analysis for this research should enhance the understanding

of the interdependency of MWD parameters, it should give an overview of the most important

parameters influencing fracturing and it should clarify the type of response of a parameter when

encountering a discontinuity.

There are two principle methods to approach sets of data: Supervised and unsupervised learning

(Bergen & Ioannidis, 2015). Unsupervised learning aims to understand relationships between variables.

Therefore, the input is data without an associated response. Examples of unsupervised learning

methods are dimension reduction and cluster analysis. For this study, one dimension reduction method

and one type of cluster analysis are found to be useful, namely Principle Component Analysis (PCA) and

k-means cluster analysis. The input data for these methods will merely be MWD data. Supervised

learning includes the associated response variable, which for this study are the presence and

geometrical properties of discontinuities. The goal of supervised learning is to generalize to new data.

One type of supervised learning is of special interest for this study, namely regression. A regression

could help to predict responses of new data. More specific for this study, regression could help to

predict discontinuities ahead of the tunnel face. In the following sections, two types of regressions are

discussed. They are multiple linear regression analysis and multiple logistic regression.

At the end, an overview of the advantages, disadvantages and data requirements for this thesis is given.

2.7.1 Principle Component Analysis

Principle component analysis, or PCA, is a form of unsupervised learning. PCA is a widely used and well-

developed statistical method. Schunnesson used PCA to investigate the interdependency of MWD

parameters. Jolliffe describes PCA as: ‘The reduction of the dimensionality of a data set consisting of a

large number of interrelated variables, while retaining as much as possible of the variation present in

the data set’ (Jolliffe, 2002). Wackernagel states that PCA can be used for data compression,

multivariate outlier detection, deciphering a correlation matrix, identifying underlying factors and

detecting intrinsic correlation (Wackernagel, 2003). The interdependent variables are transformed to a

new, uncorrelated set of variables, the principle components. The first few of these principle

components contain most of the variation present in all of the original variables. Schunnesson observed

that the MWD parameters penetration rate and torque pressure preserve most of the information

captured by all the MWD parameters.

Below an image of a hypothetical dataset with 2 parameters is given. In the left graphs shows the

original dataset with parameters x and y. If one would try to compress this dataset by deleting 1

14

parameter, the choice of deleting either x or y is not simple. By deleting either of them, a significant

amount of variability is lost as can be seen in the image below the graph.

PCA finds a coordinate system that preserves more of the variation (Powell & Lehe, u.d.). The rotated

axes are called the principle components. In Figure 6, the right graph visualizes the new coordinate

system for the hypothetical dataset. As can be seen below the right graph, the first principle component

contributes most to the variation in the dataset. The second principle component contributes much

less to the variation compared to principle component 1 or x and y. By dropping the second principle

component, the data is compressed and still a fair part of the variability is maintained.

For a higher dimensional dataset this is hard to visualize, but the principle is the same. The first principle

component represents most of the variation. The aim is to choose enough principle components so

that a significant part of the variation is preserved and at the same time to reduce number of variables.

A normal approach to investigating the number of principle components required is to look at the scree

plot. This is a plot of eigenvalues versus the component number. The eigenvalues decrease for an

increasing component number. In the scree plot one would look for the transition where the

eigenvalues are decreasing rapidly to where the eigenvalues are relatively small and of the same size.

This is not always an obvious point. A parallel analysis is a more objective way for determining the

number of components needed (O'Connor, 2000). A common parallel analysis is the Monte Carlo

analysis, where data is generated either randomly based or generated based on permutations of the

raw data. The latter option is more suitable to data which is not normally distributed. If the eigenvalue

of the nth component of the parallel analysis is smaller than the eigenvalue of the nth component of the

original dimension reduction analysis, this nth component is to be considered as statistically significant.

Other input which needs to be defined for a parallel analysis is the variables for the PCA, a desired

number of data parallel sets and a desired percentile for statistical significance testing.

Another important aspect of PCA is the type choice of rotation used to convert the variables in principle

components. There are two type of rotations oblique and orthogonal. Like stated before, PCA creates

an uncorrelated sets of variables, i.e. the principle components. It is therefore straightforward to

choose an orthogonal rotation, which forces the principle components to be uncorrelated and makes

the large loadings larger and the small loadings smaller, either within a component or with a variable.

It is also possible to use an oblique rotation, where the principle components are allowed to be

Figure 6. Two dimensional illustration of Principle Component Analysis (Powell & Lehe, u.d.).

15

correlated with each other. The thought behind it is that an oblique rotation could be orthogonal if that

is the most appropriate solution for the data. If the most appropriate solution is a non-orthogonal

relation between principle components and principle components are slightly correlated, it is also

allowed by an oblique rotation. In most cases there will be some correlation between factors or

components. If the correlation coefficient between principle components is 0.32 or higher, it means

there is an overlap of 10% or more in variance among principle components. If there is no theoretical

reason for using an orthogonal rotation, an oblique rotation is recommended (Brown, 2009). When

using a regression analysis an important reason to not use an oblique rotation is the risk of

(multi)collinearity. This will be explained later in this chapter.

The question of whether a principle component analysis should be used or not is answered by a test

called Kaiser-Meyer-Oklin measure of sampling adequacy, or KMO. This is an evaluation including the

off-diagonal elements of the original correlations and rotated correlations. As a rule of thumb, an

outcome below 0.5 indicates no reason to perform a PCA. The higher above 0.5 the more appropriate

a PCA becomes (Dziuban & Shirkey, 1974). Another method of justifying the use of a PCA is by looking

at the percentage of variance that is being accounted for by the PCA, or communalities. The higher

these percentages, the better.

2.7.2 K-means cluster analysis

A cluster analysis is a type of unsupervised learning, where the goal is to identify and understand

possible clusters in the data. A cluster analysis is designed to partition data into subsets with common

characteristics. There are different types of clustering analysis. A good analysis to assess large amounts

of data is the k-means cluster analysis. With k-means clustering, a data point can only be in one cluster,

whereas with other methods, data points can be in multiple clusters. This is also called hard and soft

boundaries. One important aspect is that the number of clusters in the data needs to be predefined.

Unfortunately there is no good way to decide on the correct number of clusters. Only in special cases

one could decide the correct number. For example if the data exist out of grading digits 0 to 9, the

obvious chose is 10 clusters.

The k-means cluster analysis starts with defining the number of clusters k. Next k random initial cluster

centres are generated in the dataspace. Boundaries of equal distance between cluster centres are

computed and every data point within a boundary of a cluster centre is assigned to the cluster with that

random initial centre point. The next step is that new cluster centres are calculated for each cluster

based on the average of the data points in a cluster. New boundaries of equal distance between the

new cluster centres are computed and all data points are redistributed to the new cluster centre. This

is repeated until the cluster centres are not changing.

In this thesis the following considerations are made to assess the number of clusters.

1. Number of iterations: If it takes a high number of iterations to find stable cluster centres, it

indicates that software has to readjust centres often due to a lack of clustering in the data. A

point of attention is that the higher k is defined, the more iterations are needed.

2. Variance Ratio Criterion (VRC). The criterion is given by

VRC𝑘 = (SS𝑏

K − 1)/(

SS𝑤

𝑁 − 𝐾)

Where SS𝑏 is the between cluster variation, SS𝑤 the within cluster variation, K the number of

clusters and 𝑁 the number of objects. VRC𝑘 is in fact the F-value of an one-way ANOVA. To

determine the correct number of clusters, 𝜔𝑘 should be calculated for every cluster. The

smallest 𝜔𝑘 indicates the best number of clusters. 𝜔𝑘 = (VRC 𝑘+1 − VRC𝑘) − (VRC𝑘 − VRC𝑘−1)

16

The VRC has been proven to work well. The down side is that due to the term VRC𝑘−1 the

minimum number clusters that can be evaluated is 3.

After the evaluation of the best number of clusters, the outcome can be cross tabulated with the

geological information. This might give inside in if a cluster could represent a certain group of input

geological variables.

2.7.3 Multiple linear regression analysis

A multiple linear regression analysis is aimed to create a predictive equation of independent variables

that correlate with a dependent variable with a minimum error in predicting the dependent variable. A

commonly used regression analysis is the linear regression analysis. Let 𝛽𝑛 represents the weights of

the dependent variables 𝑋𝑛 contributing to the output variable �̂�. 𝜀 is the error which is assumed to be

0 for multivariate regression. The end result of a linear regression is given below

�̂� = 𝛽1𝑋1 + 𝛽2𝑋2 + ⋯ + 𝛽𝑛𝑋𝑛 + 𝜀

In principle the method starts with looking for the variable that explains most of the variance in the

dependent variable, 𝑋1. Once found, it moves on to find the variable that explains the second most of

the variance of the dependent variable and so on. In order to construct the linear regression an

observation set of the independent variable 𝑌 is needed. It is not possible to explain 100% of the

variance and a challenge is to decide which variables to include.

For this research 𝑌 could be the presence or geometrical property of discontinuities. This information

needs to be transformed to a vector with the same dimensions and interval as the sampling depth. For

the simplest case of detecting the discontinuities’ positions, this vector needs to be filled with the

information ‘no discontinuity present’ and ‘discontinuity present’ on every sampling interval. A multiple

linear regression is not suited to predict a binary outcome. Like described in literature the method to

predict a binary outcome out of multiple input predictor variables is called a multiple logistic regression

model (Hosmer, Sturduvant, & Lemeshow, 2013). The main difference and reason for using a logistic

regression instead of a linear regression model can be illustrated with the simple linear regression

scatter plot of a binary outcome. If a binary outcome is plotted in a linear model plot, the regression

line is described like:

𝑦 = 𝛽0 + 𝛽1𝑥1 + 𝜀

For a logistic regression the binary outcome will be 0 or 1, illustrated with the points in Figure 7. The

binary outcome is closely related to the probability of the outcome to be between 0 and 1. The formula

can be written as:

𝑃(𝑦 = 1) = 𝑝 = 𝛽0 + 𝛽1𝑥1 + 𝜀

When plotting this regression line, it can be seen that for a low 𝑥1 or a high 𝑥1 there is a chances that

𝑝 can become negative or higher than 1. This is not possible. In addition, the error should be scattered

evenly around the regression line. As illustrated with blue lines in Figure 7, the error is not distributed

evenly around the regression line, namely the error is positive on one side of the regression line and

negative on the other side of the regression line or vice versa. This implicates that there is definitely a

pattern in error distribution. A solution would be to cap the linear model, where values 𝑝 < 0 equal 0

17

and the values 𝑝 > 1 equal 1, and represent the regression with a spline function Figure 7. In the next

part another solution to this problem will be explained in more detail, namely the logistic regression.

2.7.4 Multiple logistic regression

A multiple logistic regression model has 𝑛 independent variables 𝒙 = (𝑥1, 𝑥2, … , 𝑥𝑛) with beta-weights

𝜷 = (𝛽0,𝛽1, … , 𝛽𝑛) and the predicted dependent variable is 𝑔. To solve the problem of negative

probabilities and probabilities larger than one, the next equation is used:

𝑝 =exp (𝛽0 + 𝛽1𝑥1 + 𝛽2𝑥2 + ⋯ + 𝛽𝑛𝑥𝑛)

exp(𝛽0 + 𝛽1𝑥1 + 𝛽2𝑥2 + ⋯ + 𝛽𝑛𝑥𝑛) + 1

The equation satisfies the condition of 0 < 𝑝 < 1. This equation can be rewritten to the logistic

regression equation:

𝑔(𝑥) = ln(𝑝

1 − 𝑝) = 𝛽0 + 𝛽1𝑥1 + 𝛽2𝑥2 + ⋯ + 𝛽𝑛𝑛

This equation is also called the logit. Even though the probability function is not a linear function of 𝒙,

the transformation is a linear function of 𝒙. For simple logistic regression the regression line would look

like illustrated in Figure 8.

Note that for a simple linear regression the certain beta-value can easily be interpreted. For example

for every unit of 𝑥1 the probability increases with 𝛽1. For a simple logistic regression this evaluation is

less straight forward. In this case for every unit of 𝑥1, the probability increases with 𝑝 = ln(𝑔(𝑥)

1−𝑔(𝑥)),

where most change happens close to the inclination of the regression line.

x

0

p

1

x

0

p

1

Figure 7. Illustration of binary outcome in a linear regression scatter plot.

x

0

p

1

Figure 8. The logit function of logistic regression.

18

The vector 𝜷 is estimated with the method of maximum likelihood, a method that ‘yields values for the

unknown parameters that maximize the probability of obtaining the observed set of data.’

The assumptions or conditions for the multiple logistic regression analysis are:

1. The explanatory variable can be both continuous, ordinal and nominal independent variables

(Hosmer, Sturduvant, & Lemeshow, 2013).

2. The output variable must be a nominal dependent variable (Hosmer, Sturduvant, & Lemeshow,

2013).

3. The sample size needs to be significant (Hosmer, Sturduvant, & Lemeshow, 2013).

4. There cannot be outliers (Hosmer, Sturduvant, & Lemeshow, 2013).

5. There should be a linear relationship between any continuous independent variable and the

logit transformation of dependent variable (Lund Research Ltd, 2013), i.e. the data should fit

the model.

6. There should be no multicollinearity, i.e. correlations or multiple correlations of sufficient

magnitude which have the adversely affect regression estimates (Lund Research Ltd, 2013).

For the previously discussed input and output, the first 2 conditions are fulfilled. For a single 5 meter

long hole and a sampling interval of 2cm, there will be 250 samples. This is significant and MWD data

of multiple holes might be merged. The last three conditions need to be checked while doing the

analysis. Most likely there will be multicollinearity between the processed MWD data and possibly

between the raw MWD data. PCA might decrease the risk of multicollinearity. Collinearity can lead to

high r2 values. At the same time, if the independent variables are not statistically significant, it can lead

to unrealistic beta-weights with the opposite signs or in the worst case the analysis will not find a

solution. Four checks for multicollinearity will be considered in this thesis. The first check is looking for

extremely high Pearson’s correlation coefficients between the input variables. In literature it is stated

that if a Pearson correlation is close to 0.8, collinearity is likely to exist (Unknown, 2007). The second

check is to see if plus and minus signs of Pearson’s correlation coefficients are the same for beta-

weights. The third check is looking at the magnitude of the standard errors, which in the case of

multicollinearity might be double the size of the other standard errors. The fourth check is called the

tolerance. It is a multiple regression analysis where the input variable is regressed on to the other input

variables. Tolerance is defined as 1 minus the r2 value. Tolerance values less than 0.2 most likely indicate

multicollinearity, tolerance values of less than 0.1 indicate significant multicollinearity. To solve

multicollinearity the variable simply has to be left out or sample size can be increased. The last step

before running the analysis is to decide which parameters to include in the analysis. The final step is to

look at the Pearson’s correlations. Software indicates if a correlation is significant at the 0.01 and 0.05

level.

After establishing the beta-coefficients the model needs to be studied for its predictive capabilities. 7

check will be considered.

1. The Omnibus Tests of Model Coefficients. This is a test to check if the model with predictors

has improved compared to the model without predictors, i.e. rejecting or accepting the null

hypothesis. The model without predictors is basically the ratio between the two binary options.

The chi-square value 𝑋2 should be calculated (see equation) and compared to the critical value,

or alternatively the p-value should be computed from the chi-square value. The p-value is the

area under the chi-squared distribution of the domain from 𝑋2 to ∞. This distribution is a

function of the degrees of freedom. If the p-value is smaller than 0.05, the null hypothesis can

be rejected and the model with predictors is better than the model without. A disadvantage

with this check is that it does not tell how strong the model is.

19

𝑋2 = ∑(𝑂 − 𝐸)2

𝐸

Where 𝑋2 is the chi-square value, 𝑂 is the observed frequency and 𝐸 is the is the expected

frequency.

2. The next check will be looking at the 𝑟2 values. A 𝑟2 squared value indicates the variability of

the dependent variable, which is explained by the linear regression model. The 𝑟2 in classical

regression analysis is well known and often used as a measure of success of predictive

equations. For a logistic regression it is not possible to calculate the normal 𝑟2 value. Instead,

the pseudo 𝑟2 values can be calculated based on likelihoods of the regressed model and the

model without explanatory variables. Pseudo 𝑟2 values have some of the same properties as

classical 𝑟2 values, however the downside of using pseudo 𝑟2 values is that they are harder to

interpret. Nagelkerke came up with a 𝑟2 value which has the property that 0 < 𝑟2 < 1

(Nagelkerke, 1991).

3. Hosmer and Lemeshow Test for goodness of fit. This is another test to check how good the

model fits the data, where if the p-value is smaller than 0.05 it is defined to be not a good fit.

It should be noted that this p-value is different from the one described in point 1. This test is

very dependent on sample size and should not be interpreted without considering the sample

size. Hosmer and Lemeshow state that minimum sample size should be over 400 and is

especially powerful when the ratio of binary outcome is divided to a certain extent, for example

a 0.25/0.75 (Hosmer, Sturduvant, & Lemeshow, 2013).

4. It is possible to check to what extent the predictive capacity increased from the model without

predictors to the model with predictors.

If the model is found to fit the data and is of significant quality, the outcome can be interpreted.

5. Like stated before, the interpretation of the beta-coefficient or their odd ratios for a logistic

regression does not always give a correct insight into the relative importance of one predicting

variable. Usually only unstandardized beta-coefficients are calculated by statistical software. It

is possible to calculate the standardized beta-coefficients, so that the beta-weights can be

compared for importance in the model. The equation used for calculating standardized beta-

coefficients is:

𝛽𝑛,𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑 = 1

(1 + 𝐸𝑋𝑃 (− (𝐿𝑁 (𝐴

1 − 𝐴) + 0.5 ∗ 𝐵 ∗ 𝐶)))⁄

− 1

(1 + 𝐸𝑋𝑃 (− (𝐿𝑁 (𝐴

1 − 𝐴) − 0.5 ∗ 𝐵 ∗ 𝐶)))⁄

Where 𝛽𝑛,𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑 is the standardized beta-coefficients, 𝐴 is the mean predicted

probability for the dataset, 𝐵 is the unstandardized beta weight and 𝐶 is the the sample

standard deviation (King, 2007).

6. Each beta weight is tested by Wald statistics, which leads to an individual p level for each beta

weight. The Wald value is the beta-coefficient divided by the standard error to the power of 2.

The p-value is determined by use of the Wald distribution. Wald statistics are sensitive to

sample size.

7. The predicted discontinuity location vector can be compared to the original discontinuity

location vector. Possible mismatches between the two can be understood by looking back at

geological records and MWD graphs.

20

A final test can be done by using the regression equation to predict discontinuities in a dataset which is

not used as input to the regression.

2.7.5 Overview of statistical analyses

In Table 1, an overview of the advantages, disadvantages and data requirements for this thesis is given.

Table 1. Over view of statistical analyses.

Learning type Method Advantages Disadvantages Data requirement

Unsupervised PCA - Low noise sensitive - Reduction in data - Reduction of noise

- Restricted to linear correlations

- Sample adequacy, i.e. large enough sample size

K-means cluster analysis

- Fast for large datasets compared to other clustering methods - Simple and understandable use

- Different initial cluster positions could lead to different outcomes - Hard to determine the number of clusters - No appurtenance to several clusters

- An idea of how many clusters are could be present in the data

Supervised Multiple linear regression analysis

- Accounts for multiple predictive variables simultaneously

- Cannot tackle a categorical dependent variable - Sensitive to collinearity - Assumes linear relation ships

- Continuous or categorical independent variables - A dependent variable on continuous scale

Multiple logistic regression

- Accounts for multiple predictive variables simultaneously - Can tackle a categorical dependent variable, essential for this study - Does not assume linear relationships between dependent and independent variable

- Sensitive to collinearity

- Continuous or categorical independent variables - A dichotomous (binary) dependent variable

2.8 Correlation between MWD and geology

Several case studies have been done in Norway on the subject of MWD. In this section, the studies and

findings are summarized. At the end, the findings are listed.

2.8.1 Grouting volumes and MWD data

Høien and Nilsen have been looking at grouting volumes and MWD data of fracturing and water

conditions (Høien & Nilsen, 2014). The Løren tunnel is a 915 meter rock cover tunnel in an urban setting.

The main hazard was the lowering of the groundwater table which could cause settlements. Systematic

grouting and monitoring gave a good possibility to execute this research. The MWD-software used by

21

Høien and Nilsen is GPM+, delivered by Rockma AB, and data processing is based on Schunnesson’s

research. The water leakage mapping is done after the injection of grout, hence a quantitative analysis

of the relation between water leakage and MWD data is not possible. In this tunnel, all the grout holes

were first drilled and subsequently the grout was injected. In other cases, the grouting starts directly

after drilling any single grout hole. In this situation, the injected grout might influence the MWD data.

Høien and Nilsen found several sections that showed a good match or a visible relation between MWD

water conditions and fracturing with respect to the grouting volumes. For future projects, they

recommend separating the study in areas with and without groundwater.

2.8.2 Geological boundaries and zones of weaknesses

Hjelme worked on the same tunnel for his MSc thesis (Hjelme, 2010). The aim of his research was to

analyse the sensitivity of normalized MWD data, to identify geological boundaries and zones of

weakness of software in verifying the geology mapped in the tunnel and to investigate the correlation

between mechanical rock properties and MWD data. He used the software Tunnel Manager by Atlas

Copco AB which is based on Schunnesson’s research. Calibration of the drilling rigs is done by use of

data from other projects. Hjelme zoomed in on 5 chainages. He concluded that the validity of

normalization depends on the amount of data. The significant difference between the number of

injection holes and blast holes is reflected in the results. The higher number of blast holes resulted in

smoother lines after normalization than for injection holes, where the data set was 1/3 the size of the

blast holes. Another important point is the fact that at the beginning of each hole, drilling is executed

with a soft start, hence the first part of the data is deleted. The feed thrust highly affects the penetration

rate and torque, therefore normalization of penetration rate and torque pressure for feed is essential.

Hjelme found that there often is a mismatch between MWD data and geological mapping. Weakness

zones and transitions to other rock types are often found in both the geological records and MWD data

with a spatial difference of up to 2 meters. This occasionally high difference might be caused by use of

geological mapping done by engineering geologist on site. Occasionally features were only visible in the

MWD data or vice versa. It should be noted that the location of fractures and transitions in the

geological mapping are visualized in a spread out tunnel contour and that finding the exact locations of

features on this spread out contour is hard. Figure 9 demonstrates how Hjelme extracted the locations

of geological features from the geological records. He illustrated the investigated boreholes on the

geological mapping.

Figure 9. Illustration of how Hjelme placed boreholes in a spread out tunnel contour sheet for comparing MWD data to mapped geology.

22

2.8.3 Mechanical properties of rock and MWD data

Rødseth focused her research on the comparison between rock mechanical properties and MWD data

of the Eikremtunnelen, the Lørentunnelen and the Oppdølstranda tunnel (Rødseth, 2013). The

geological mapping is done by the geologist and engineering geologist of the Norwegian Public Roads

Administration. Rockma AB and SPSS are used to process the MWD data. The comparison of MWD data

and geological data is done in Microsoft Excel, where Rødseth made XY-plots to determine the

correlation coefficients (r2) between the MWD data and the fracturing number produced by Rockma,

RQD and RQD/Jn. In total Rødseth used about 4700 meters of tunnel length. Based on research, an

absolute correlation coefficient, I(r)I, higher than 0.5 represents a good correlation and an absolute

correlation coefficient lower than 0.3 represent a low correlation (Holmøy, 2008). Close to 2100 meters,

or 45 %, of tunnel MWD data showed a good correlation with the RQD, while only 16 % of the MWD

data showed a good correlation with RQD/Jn. Rødseth discussed the limitations of this research. The

geological records are often questionable. For example joint sets are documented as single joints with

no frequency. Further, Rødseth mentioned blast induced fractures and the difference between

resolution in geological data, RQD and MWD data.

Valli aimed his research on the hardness parameter at the ONKALO project in Finland [37]. He aimed to

correlate MWD hardness evaluations with the Schmidt hammer rebound number and point load tests.

Valli used the software Tunnel Manager MWD version 1.5 developed by Atlas Copco. This method of

processing MWD data has not been made public. The Schmidt hammer was used for approximately 126

meter of tunnel wall where every meter, about 8 measurements were made. 1385 point load tests

were used in the study. He found the correlation between MWD hardness and Schmidt rebound

number unsatisfactory. Changing Schmidt rebound numbers are found for not changing MWD

hardness. The results of the point load tests show a too small variation and is not usable for a

comparison with MWD data.

2.8.4 Mapped geology and MWD data

Herfindal based her research on the Skillingsmyrtunnelen (Herfindal, 2013). She investigated whether

there exists a correlation between MWD data, mapped geology, Q values and registered water inflow

by using the program GPM+ by Rockma. She found that the defined fracture outcome showed an as

high intensity in areas with both high and low RQD values. Hardness is found to correlate well with the

mapped geology since most of the dikes can be found back in the processed MWD data. Herfindal

recommends for future research to perform a more thorough geological field study or 3D surface

scanning in order to eliminate the difference in detail of geological documentation and MWD data.

2.8.5 Findings in research in the field of MWD technology

Based on the studied research, the following can be stated.

- The hardness parameter is found to correlate well with geological boundaries like dikes, zones

of weakness and rock type transitions. The maximum difference between location in MWD data

and geological record is found to be 2m.

- Several times, a good match or a visible relation between MWD water conditions and fracturing

with respect to the grouting volumes is found. The data is collected at a tunnel site with various

ground types like limestone, shale, sandstone, volcanic rocks and soil.

- RQD is found to correlate well with MWD fracturing number for 46% of studied tunnel sections

from 3 different tunnels. The relative block size, or RQD/Jn, did not show a convincing

correlation.

- Schmidt hammer hardness has not been shown to correlate with the MWD hardness number.

23

3. Data gathering and collected data

For this thesis two tunnel sites are visited (Figure 10) where different methods of gathering detailed

geological data are performed. All these methods have in common that the gathered data can be

compared to the MWD data of one or multiple holes. In this chapter the geology of the sites,

methodology and resulting datasets are discussed.

3.1 Preparation of fieldwork at Bjørnegårdtunnelen

3.1.1 Project

The Bjørnegårdtunnel is part of the Sandvika-Wøyen project for which the Norwegian Public Roads

Administration is the client. Traffic going through the city of Sandvika will be deviated by two 2-lane

tunnel tubes of 3.4 km long. Two thirds of the tunnel is constructed with drill and blast tunnels and one

third is constructed with the cut and cover method. Construction started in February 2015, drill and

blasting activities are planned to be finished in the autumn of 2016 and the project is planned to be

open for traffic in 2020.

3.1.2 Geology

The project is located in what is called the Oslo field. The rocks encountered in this project area are

deposited in the middle and late Ordovician in a sedimentary environment. The collision of Laurentia

and Baltica caused the rocks to fold on the outer borders of this collision zone, where the Oslo field was

located. In this period the Caledonian orogeny shaped the mountainous landscape in middle and west

Norway. In the late Carboniferous partial continental rifting opened a new sea which today is the Oslo

fjord. The sedimentary rocks are tilted towards the Oslo fjord. As the rifting continued batholith, dikes

and lava originated in the Oslo field (Ramberg, Bryhni, & Nøttvedt , 2013).

In Figure 11 two geological cross sections are given of the trajectory of both tunnel tubes. Drill and blast

is executed on the section from 300m to approximately 1650m northwest of the hill Hamang. A local

Figure 10. Map of southern Norway with tunnel site locations.

24

geological map is attached in appendix 1 (GEOVITA, 2014). The rocks encountered are limestone, shale

and intrusive rocks (see legend). One limestone formation consists of alternating layers with nodules.

In the engineering geological report the discontinuities predicted in the sedimentary rocks are mainly

bedding discontinuities with a steep dip and irregular oriented discontinuities. The spacing of bedding

discontinuities is predicted on a dm-scale.

Figure 11. Cross sections of geology encountered with constructing the two tunnel profiles (GEOVITA, 2014).

3.2 Preparation of fieldwork at Solbakktunnelen

3.2.1 Project

The Solbakktunnel is part of a project named Ryfast, which connects the inland and Stavanger and

improves the infrastructure on the east site of Stavanger. It consists of three tunnels. Solbakktunnelen

will be 14.3km long and 290m below sea level at its deepest.

3.2.2 Geology

The tunnel site is located in an area with Precambrian autochthonous basement, gneiss, and phyllite

form the Cambrian to Ordovician age (Multiconsult, 2009). The gneiss is found in two different groups,

Boknafjorddekket and Storheidekket (see Figure 12). The first group contains quartz and feldspar rich

gneiss which is locally banded. The second group contains mica rich gneiss, which is described as gneiss

with a grainsize ranging from fine to medium. The foliation found in outcrops has a spacing ranging

from 2 to 50cm. In addition there is a discontinuity set with a spacing varying between 0.5 and 1m. The

phyllite has been found in different intensities of metamorphism ranging from intense small scale

folding to foliation with a spacing of less than 10cm. As the contractor starts excavation at the east end

of the tunnel, they will first encounter gneiss from Storheidekket, then gneiss from Baknafjorddekket

and finally the phyllite. The exact location of the transition from gneiss to phyllite at tunnelling depth

cannot be predicted with high accuracy.

Malmøya Formation - Limestone, massive bedded and as lenses and nodules alternating with shales (80)

Skinnerbukta Formation - Shale, silty with thin calcareous zones (81)

Vik Formation and Rytteråker Formation - Limestone and shale, predominant limestone with nodules (82)

Solvik Formation - Shale with thin layers of siltstone and limestone (83)

Langøyene Formation - Massive limestone and shale layers (84)

Different formation - Shale with limestone nodules (85)

Fault

Dike

Measured dip

25

Figure 12. Geological map with Solbakktunnelen section, the yellow line is approximately 2km (Multiconsult, 2009).

3.3 Geological mapping

In this part, the possible geological mapping methods for this projects are discussed. Central with these

methods is the need for information of discontinuity type, discontinuity properties and location of the

discontinuity with an accuracy such that it can be compared to MWD data.

3.3.1 Tunnel wall mapping

A perfect blast has no overbreak and it creates straight planes between every contour hole. In such a

case, the borehole remains (half holes) can be used as a scanline where the location of a discontinuity

and corresponding geometrical properties of discontinuity are documented. In reality, a blast is never

perfect and borehole remains might be absent. The information wanted with this method are location

of the hole, overall RQD, Schmidt hammer strength of discontinuity planes, joint set, orientation,

aperture, persistence, infill, weathering state of discontinuity plane, joint roughness and joint alteration

number. In addition, the intact rock strength is wanted to evaluate a possible different between intact

rock strength and discontinuity plane strength, which might influence drilling parameters. Attention

should be payed to the difference between blast induced and natural fractures.

In case borehole remains are not present or out of reach, an area within reach can be mapped. The

same information should be gathered as with mapping borehole remains. The only difference is that

mapping becomes two dimensional which makes it harder to compare it with one dimensional MWD

data of a single borehole. A terrestrial laser scanner or photography would be useful to map

discontinuities, but were not used in this study.

In case large discontinuities are present in the face, these can be mapped with an estimation in a rolled

out tunnel profile, as described in section 2.5.

It should be emphasized that without the possibility to place discontinuities accurate in a 3D image or

along MWD data of a borehole remain, the data is not relevant for this study. This is expected to be the

main challenge during the fieldworks.

26

3.3.2 Borehole inspection

A method of capturing rock which is much less influenced by blasting is inspecting boreholes. This can

be done with different types of devices. The cheapest device to use is a regular inspection camera. It

should at least have proper lighting and depth indication. In addition, an indicator of the orientation of

the camera can be useful in case the camera might rotate. A disadvantage is that most inspection

cameras are aimed forward and the borehole wall is recorded with an angle less than 90°. An acoustic

or optical televiewer is a better alternative. An acoustic televiewer measures the change in wavelength

and amplitude of reflected soundwaves send towards the borehole wall. The borehole has to be filled

with water. The optical televiewer is a camera that records the reflection of the borehole wall a convex

mirror inside a tube. This gives a recording of the borehole wall with a 90° angle. The cylinder is pulled

out of the borehole with a speed of about 1m/min with a windlass, which registers the depth of the

device. Bright light is generated in the cylinder, which is kept central in the borehole. The optical

televiewer can be used in both dry and water filled holes, but the water needs to be clear. In Figure 13

an illustration is given (NGU, 2015). During the recording, the orientation of the televiewer is measured.

The outcome of a survey with a televiewer is a detailed digital image with a pixel size going down to

1mm2 of the borehole wall. This image can be processed with a simple program where three clicks on

a discontinuity lead to a sinusoidal approximation. The depth, orientation, self-given description of

discontinuity type, possible aperture and orientation statistics are the output.

3.4 Fieldwork at Bjørnegardtunnelen

During the first visit, 4 faces were under excavation. The engineering geologist was called to inspect the

geology of recent excavated rock several times a day. During these inspections, it was planned for this

thesis to document geology with more detail by mapping the tunnel wall. In addition, the Norwegian

Public Roads Administration and the contractor NCC agreed to drill 10 holes for borehole inspection.

During a second visit, an additional 15 holes were drilled. At the time of both fieldworks, limestone was

being excavated. An alternation of zones and layers with and without nodules caused that degree of

fracturing is not the only variable influencing MWD data. In this subchapter the fieldwork and resulting

data will be discussed.

Figure 13. Illustration of an optical televiewer (NGU, 2015).

27

3.4.1 Tunnel wall mapping and MWD data

In practise, mapping of the tunnel wall is a challenging task. The tunnel contour at the site was

supported with shotcrete almost immediately after the scaling and mapping. Stopping the drill and blast

cycle was not preferred. Issues like poor visibility, noise, reachability and most importantly, safety highly

influenced the ability to work. Several times the geological mapping was attempted without successful

mapping results. For constructional reasons a part of the northern wall of tunnel A was not covered

with shotcrete. There it was possible to spend more time and realize better conditions. In Figure 14, a

remain of an injection borehole is displayed. This borehole crossed 7 discontinuities. The white colour

is the injected grout. The exact location of the end or beginning of the borehole is unknown, but the

fracture studied by the person in the picture is a fracture with an aperture of 3 to 4 cm filled with clay.

This fracture and its approximate location, 0.3m before profile 955, will most likely be visible in MWD

data with mapped data and is therefore chosen as the start point of observations. In Table 2 the

observed discontinuity and geometrical properties are given.

Table 2. Geometrical properties of discontinuities crossing an injection borehole remain close to profile 955 in tunnel A.

Fracture number 1 2 3 4 5 6 7

Distance (m) 0 -0.45 -1.95 -2.60 -3.25 -3.34 -4.41

Location (m) 954.7 954.25 952.75 952.1 951.45 951.36 950.29

Dip direction (°) 160 155 160 80 150 150 152

Dip direction relative to tunnel (°)

210 205 210 130 200 200 202

Dip angle (°) 74 70 70 70 70 70 70

Aperture (mm) 30-40 10 0 0 0 0 0

Persistence (m) Whole contour

Whole contour

Whole contour

Whole contour

4 4 Whole contour

Infill Clay Calcite - - - - -

Weathering None None None None None None None

Figure 14. Mapping at tunnel wall in tunnel A at profile 955 of an injection borehole remain. Person length is 1.75m.

28

A few times, there was a possibility to have a closer look at the tunnel contour from a platform. In the

section 510 to 505 in tunnel tube A, a calcite vein persisting along the whole tunnel contour is

documented. The discontinuity has an orientation of 155/87 relative to north and an aperture of 5 to

10 mm. The bedding is approximately orientated 200/80 relative to north. In Figure 15 you see a graph

which illustrates the continuation of the discontinuity over the contour like explained in section 2.5.

Figure 15. Graphical representation of calcite infilled discontinuity in tunnel A at profile 505.

During grout injection, there was plenty of light and time to map the tunnel front. A disadvantage is

that the walls are covered with shotcrete and discontinuities visible in the face need to be extrapolated

to an estimated point of intersection with a borehole. Extrapolation of discontinuities is a large possible

error since the straightness of a fault plane is hard to predict. When discontinuities are close to parallel

to the tunnel face, extrapolating faults planes can be based on multiple orientation measurements.

Figure 17 shows tunnel face 970 in tunnel A where several large discontinuities are close to parallel

oriented with the tunnel face. Infill differs from calcite, unknown, syenite (most likely) and no infill.

Apertures vary from 0 to 30mm. In Figure 16 all the information from both contour mapping and face

mapping can be found. The locations of the discontinuities is estimated on site.

505.00

510.00

-14.00-7.000.007.0014.00

Discontinuity in tunnel A section 510-505

Fracture 1, calcite infill of 5 to 10 mm, 160/87

965.00

970.00

975.00

-12.00 -6.00 0.00 6.00 12.00

Fractures in tunnel section 965-970

Fracture 1, red infill of 30 mm or moreFracture 2, no infill observedFracture 3, red infill of 20-30 mmFracture 4, no infillFracture 6, unknown infill of 20 mm in lower 5 meters, fracture not visible higher then 5 metersFracture 7, no infill or 1 mm of calciteFracture 8, 5-22 mm calcite infillFracture 9, 10-30 mm calcite

Figure 16. Graphical approximation of positions of discontinuities seen along tunnel face 975 in tunnel tube A.

29

Figure 17. View along the tunnel face 975 tunnel A. The packers are separated by approximately 1.2m (top left), the packers stick approximately 30 to 40 cm out of the wall (top right), the rock bolts in the shotcrete have a diameter of 20cm (lower right).

30

3.4.2 Borehole inspection and MWD data

Discontinuity mapping at the tunnel face has the disadvantage that it is hard to define the exact location

of a discontinuity with regards to a borehole. A televiewer is possibly the best method to gather data

for a comparison between geology and drilling parameters. The Geological Survey of Norway provided

the televiewer service.

During the first field work, the possibility was given to drill 10 holes of 5 meters long. The holes had to

be drilled in reach of electricity and could not penetrate the grout screen. Drilling of holes also had to

fit in the drill and blast activities of the contractor NCC. Moving the drilling jumbo for each hole would

take too much time so it was chosen to park the drilling rig at three places and to drill multiple holes

with different orientations at every location. The drilling arm of the AMW drilling jumbo had a forward

reach of about 4m. The blasthole size used at the Sandvika site is 48mm. Due to the size of the

televiewer all holes had to be enlarged, once with the normal blasthole drill bit and once with a large

hole drill bit to reach a 110mm diameter. In order to receive both MWD data from the normal hole

drilling and large hole drilling, drilling settings had to be adjusted on the drilling jumbo. Sampling

interval was reduced to 10mm and the drilling parameters for torque pressure, feed pressure and

percussive pressure for the large hole drilling had to be set the same as the normal hole drilling. One

test hole was drilled with these settings during drilling of large holes in a face. During the second

fieldwork, another drilling jumbo was available. Unfortunately no other rock type was encountered in

the meantime, hence holes were drilled in limestone again.

The most important aim of this part of the fieldwork is to see how drilling parameters behave when

drilling through discontinuities. Therefore, it was essential to encounter discontinuities. To analyse the

response only of discontinuities, the other rock mass properties should be constant. At this site the

lithological discontinuities, or bedding, are dominant. Open fractures and discontinuities with infill were

less dominant. Documented geology by engineering geologist on site, like described in section 2.5, was

used to determine locations to drill holes. Since bedding would most likely be encountered in the holes,

it was chosen to focus on trying to capture other discontinuities than bedding. In addition, it is preferred

that the rock mass changes as little as possible. The locations chosen are displayed in figures given

below . The stars indicate the approximate location of drilling. The drilling direction is opposite to the

profile numbering with angles of 55° or 90° with respect to the tunnel axis. Twice, an angle of 20° has

been tried, but the operator had problems finding grip and when drilling large holes there were

problems with keeping the drilling arm aligned with the pilot hole. This lead to the loss of a large drilling

bit and a drilling string. The drilling bits which were used where all relatively new, or brand new after

losing a drilling bit.

The location of borehole 1 and 2 was chosen since the observation by the engineering geologist

described an infill of clay for all 5 discontinuities. One discontinuity was observed with an aperture of

1cm. At this location, the RQD is estimated to be 62.5 % and two joint set plus random joints were

observed. See Figure 18.

Figure 18. Approximate location holes 1 and 2 drilled with angles of 20° to 30° from the tunnel axis.

31

At the location of borehole 3 to 10, three fractures with an aperture of 1cm, 30cm and 40 cm are

documented. It could well be that the units have gotten mixed up and that the aperture is 3cm and

4cm, as the discontinuities are mapped by different engineering geologists. The discontinuity with an

aperture of 1cm has a clay infill. At this location, the RQD has been documented between 62.5 % and

85 % and two joint sets plus random joints have been observed. See Figure 19.

Boreholes 11 to 15 are located where discontinuities were observed by the author during the first

fieldwork. Even though the engineering geologist did not document all the discontinuities present,

multiple discontinuities with different types of infills are present at that location. It should be noted

that the documentation is done by a substitute for the normal engineering geologists on site. In Figure

20 in the previous section, the discontinuities are mapped. During planning of drilling, the locations are

studied for feasibility and navigation. At this location, a small part of the shotcrete collapsed and the

rock wall is visible. Even though the rock was covered in dust and dirt, closed discontinuities and

discontinuities with infills of 1cm syenite and 3cm clayey content are observed.

Boreholes 16 to 20 are chosen at a location where multiple discontinuities are observed. One

discontinuity has a 4cm infill of clay. RQD is documented to vary between 62.5 and 70 % and two joint

sets plus random joints are observed. See Figure 20.

Figure 19. Approximate location holes 3 to 10 drilled with angles of 55° and 90° from the tunnel axis.

Figure 20. Approximate location holes 11 to 15 close to 975 and holes 16 to 20 close to 960, drilled with angles of 40° to 60° from the tunnel axis.

32

The location of the remaining boreholes 21 to 25 is chosen based on the documentation of 5

discontinuities of which three have an aperture of 4cm. The documented RQD is 62.5 % and one joint

set plus random joints have been observed. See Figure 21.

The data set of the televiewer borehole inspection consist of 2 borehole recordings of 4m length and

23 borehole recordings close to 5 meter length. For an unknown reason, the adjustments in sampling

interval in the drilling system did not lead to a decrease of sampling interval in the MWD data. It could

be that the minimum sampling distance that can be registered by the drilling systems is 0.02m. The

2718 samples in normal hole drilling with the Atlas Copco jumbo have an average interval of 0.0271m

with a standard deviation of 0.010m. For the large holes these numbers are: 3249 samples, average

interval of 0.0225m and a standard deviation of 0.0046m. The 2430 samples in normal hole drilling and

2283 samples in large hole drilling with the AMW drilling jumbo have an average interval of 0.0200m

and standard deviation of 0.0005m. Another difference between the two drilling rigs observed in the

MWD data is that overall the hammer pressure of the AMW jumbo is fluctuating around 140 bar, while

for the Atlas Copco jumbo, it fluctuates around 200 bar. Also for the rotation pressure, water pressure

and water flow there are overall differences of 60 bar, 5 bar and 40l/min.

After drilling of every pilot hole, the drilling boom needed to be moved in order to save and receive

data from both the pilot hole and the large hole. This caused problems with finding the exact same

drilling orientation of the pilot holes for the large holes. When a large hole drill bit is not aligned with a

pilot hole, it causes the drill bit to jam and asks for readjustments by the operator. In Figure 22, the

difference between the drilling log of a pilot hole and the corresponding large hole is illustrated with

feed pressure from hole 9. At about 3.15m, 4.6m and 4.9m the feeder pressure from the large holes

drop due to the process described above. During drilling, it was written down when the drill bit got

jammed. This happened mainly with drilling the large holes. One additional event seen in all MWD data

is the slow start. The operator controls the moment when drilling in full speed is started. Drilling with

full speed from the beginning is never done. In Figure 22, the feeder pressure is low in the beginning

and increases after about 0.6 meter for the normal hole and after about 1.5m for the large hole. This

effect should be disregarded when analysing MWD data. In addition, hole 1 showed an unexplainable

low penetration rate.

The televiewer measurement of borehole 1 is defined for a depth from 0.0m, the bottom, to -4m. For

borehole two, the bottom is set to -1.84m. The remaining holes have a reference point of 5m. The

televiewer had to be held in place by hand the last 0.5m of each measurement. This caused poor

recordings for the last 0.5m. While drilling borehole 17, the drilling bit broke at a depth of about 4.3m

deep. Drilling the large hole did not go smooth and the drilling bit had to be pulled out of the hole half

way. At the point where the drilling bit enters the hole again, a new drilling log file is made. The starting

depth of in the new log file can be anywhere between the tunnel wall and the depth where drilling

stopped before the drilling bit was pulled out. This led to a high uncertainty in depth of the large hole

since two log files have to be merged.

Figure 21. Approximate location holes 21 to 25 drilled with angles of 20° to 30° from the tunnel axis.

33

In the recordings, lithological banding, calcite veins, fractures with soft infill and fractures without in fill

are present. Lithological banding is dominant and present in all televiewer measurements. Veins are

present in all holes except holes 10, 24 and 25. They are most likely filled with calcite, which was the

only vein infill seen in fresh rock. Borehole 1 and 2 do not show the expected fractures with a clay infill.

The clay might be flushed out during drilling. In borehole 5 and 6, the mapped fractures are visible.

Here, the black bands are zones where light is not reflected to the televiewer. At the location of

boreholes 11 to 15, a fracture with soft, fine grained, dark red infill is present but not detected in the

televiewer measurements. Other fractures are detected at this location. Boreholes 11, 12 and 13 are

drilled partly in a red sandstone. In holes 21 to 25, fractures with a softer, more clayey, green infill are

captured. A sample is taken from a fracture in crossing the first 10cm of a borehole. From studying the

tunnel contour, it is know that all discontinuities except for calcite veins have zero or nearly no tensile

strength. This means that all discontinuities except for calcite veins would contribute to the calculation

of RQD.

The depth registration of the reel, which pulls the televiewer out of the holes is accurate. There was

however a problem with controlling the movement of the cable when the reel was placed multiple

meters from the hole. When recording holes 21 to 25, the minivan containing the reel had to be parked

about 8m from the tunnel wall. This made it hard to control the movement of the cable. Wobbling

might have led to an inaccurate depth registration.

The image processing is done with the software RGLdip by Robertson Geologging. The outcome of this

is a table with the upper and lower depth of sinusoidal approximation in mm accuracy relative to the

reference point, the average depths in mm accuracy relative to the reference point, orientation relative

to north, aperture and a self-interpreted discontinuities definition for each discontinuity. In appendix

2, the 25 recordings and these outcomes are given. In appendix 3, the MWD data is given.

0

20

40

60

80

100

0 1 2 3 4 5Feed

er p

ress

ure

(b

ar)

Depth (m)

Feeder pressure

Normal

Large

Figure 22. Graph showing drops in feeder pressure due to jamming of the drill bit caused by difficulties with aligning drilling direction of the large hole.

34

3.4.3 Overview of collected data

The geological data and MWD data collected at Bjørnegårdtunnelen is summarized in Table 3. Only the

usable data for this study is discussed in the report.

Table 3. Overview of collected data at the Bjørnegårdtunnelen.

Location Geological data MWD data Correlation type

Tunnel tube A Close to profile no. 955

Borehole remain of about 5m long crossing 7 discontinuities of which one with clay infill, one with calcite infill

MWD data with a sampling interval of 2.48cm and standard deviation of 0.44cm.

Visual comparison

Tunnel tube A Profile no. 510 to 505

Position and orientation of a calcite vein persisting along whole tunnel contour

3D images of parameters Visual comparison

Tunnel tube A Tunnel face at profile no. 970

Positions and orientations of 9 fractures persisting along the whole face. Infill is varying from clayey, reddish unknown, unknown and none.

3D images of parameters Visual comparison


2 borehole images with calcite veins and lithological discontinuities

MWD data with a sampling interval for normal and large hole size of 2.00cm and standard deviation of 0.005cm. Rotation speed is missing.

Visual and statistical comparison


8 borehole images with calcite veins, lithological discontinuities and fractures without infill



MWD data with a sampling interval for normal hole data of 2.71cm and standard deviation of 1.00cm. Large hole data sampling interval average 2.25cm and standard deviation 0.46cm.

Tunnel tube A Close to profile no. 975 and 960

10 borehole images with calcite veins, lithological discontinuities, fractures with softer clayey infill and fractures without infill

3.5 Fieldwork at Solbakktunnelen

At the time of excavation, 2 faces were under excavation. At this site the work continued day and night

and several times a day there was the opportunity to map the geology of recent excavated rock. On

site, there was a borehole camera present, which could be used to record large hole drillings in the

tunnel face.

3.5.1 Tunnel wall mapping and MWD data

During the whole fieldwork, the rock excavated was highly foliated phyllite and larger discontinuities

were absent. Often rock fragments were falling after scaling and it was not safe to stand under recently

excavated rock that had not been supported. This made mapping at the tunnel face for the purposes

for this thesis impossible.

35

3.5.2 Borehole inspection and MWD data

The inspection camera was used at 3 different tunnel faces to inspect the large holes. The camera used

is an Inspector ® 48 produced by CamTronics with a resolution of 720x576 pixels. Unlike the televiewer,

this inspection camera produces a regular video of the borehole wall. The equipment includes a

portable reel and computer on which real life footages was displayed (see Figure 23. The inspection

camera and portable computer.). It was unfortunately not possible to spend more time besides making

one recording for each hole. The inspection camera viewed forward with an 84° angle and bright

artificial light. On the reel, a depth indicator with an accuracy of 0.1m was mounted. It was difficult to

keep the cable tensioned between the reel and entrance of the holes. The depth measurements are

not accurate. The differences in actual drilling depth and recorded depth with the reel go up to 2.1m.

Most differences are within 1m. The image is automatically horizon corrected. After the first time of

using the camera, it turned out that the video footages was blurry when moving. After this, it was tried

to hold the camera still in the hole for a second every few centimetre. On first sight, all holes show the

similar highly foliated phyllite.

Discontinuity are difficult to identify or are absent in the filmed holes. In Figure 24, screenshots of

typical video footages is given. Of all holes, MWD data of both small and large hole is available (appendix

4). The sampling interval is 20mm. Water pressure, water flow and rotational speed are missing due to

an unknown reason. The length of the holes varies between 4m and 5.7m. Holes 10, 11 and 12 are 4m

holes. These are drilled in a tunnel tube where the blasting length was reduced due to poor rock quality

and short stand-up time of unsupported rock. Hole 5 is a short hole as well. Hole 4 and 5 cross each

other at a depth 4 meters. This might indicate that the operator stopped drilling hole 5 at the depth

where the hole met hole 4.

Figure 23. The inspection camera and portable computer.

36

3.5.3 Overview of collected data

The geological data and MWD data collected at Solbakktunnelen is summarized in Table 4. Only the

usable data for this study is discussed in the report.

Table 4. Overview of collected data at the Solbakktunnelen.

Location Geological data MWD data Correlation type

Tunnel tube A Profile no. 4184 Tunnel tube B Profile no. 4276 and no. 4281

11 borehole videos capturing highly foliated phyllite, no large discontinuities visible

MWD data with a sampling interval of 2.00cm and standard deviation of 0.005cm.

Visual comparison

Figure 24. Screenshots of typical video footages.

37

4. Visual validity assessment of MWD data

In this chapter, the geological data and MWD data will be compared visually. The geological data gained

by mapping the tunnel contour will be analysed first, followed by the data collected with borehole

inspection.

4.1 Tunnel wall mapping data

The raw and processed MWD data is given in appendix 5. The processed parameters are given for

computations of a moving average of 4, 6, 8 and 10 values. In all graphs, the orange lines represent the

estimated location of the discontinuities. The discontinuities have dip directions of 70° or larger relative

to the drilling direction. The average sampling distance is 25mm with a standard deviation of 4mm.

By observing the location of fracture 1 at 954.7 in appendix 5, it can be concluded that the correct MWD

data is found. This fracture has an opening of 30-40mm with a clay infill. The operator experiences this

kind of fractures as a drop in penetration rate since the drilling settings are adjusted automatically in

such a case. The penetration rate shows the exact same behaviour, 7 cm from the observed location.

This fracture would be visible in neighbouring holes since all holes have a slightly different orientation

and the fracture is not perpendicular to the tunnelling direction. For this specific fracture, the hammer

pressure, feeder pressure and rotation pressure drop significantly. Apart from discontinuities 1, 5 and

6, there seems to be no distinctive response to the discontinuities in the MWD data. Discontinuities 5

and 6 are separated by 90mm and there seems to be one response in penetration rate and rotation

pressure.

In appendix 5, fracture 1 is clearly visible in the normalised penetration, RMS normalized penetration

and RMS rotation pressure. When a moving average out of 4 measurements is used, the response

represents the fractures aperture better. Fractures 5 and 6 remarkably result in a drop in normalized

penetration and rotation pressure. Again, the moving average of 4 measurements represents the

spacing of the fractures 5 and 6 best in the RMS normalized penetration rate. Fractures 2 and 3 seem

not to influence MWD data in any way and fractures 4 and 7 could have influenced the MWD data.

The exact position of the fracture observed in profile 510 to 505 is inaccurate. For that reason, it is

chosen to see if this fracture can be seen in 3D images of Bever Team. All parameters are inspected

with different colour settings and moving averages. The fracture with calcite infill of 5-10mm and an

orientation of 155/87 is not visible. In appendix 6, the RMS normalized penetration for a moving

average of 4, 6 and 8 measurements is given. In these images, several trends of darker colours can be

identified running diagonally through the profile. These trends are in line with the orientation of the

bedding discontinuities.

In Figure 25, the RMS normalised penetration with a moving average of 4 values of the contour of

profile 965-975 is given. It is difficult to identify discontinuities in images like these. In appendix 7, more

images are given of various parameters and moving averages, which all together led to the

interpretation in the right part of Figure 25. The lines may indicate the positions of the observed

discontinuities described in section 3.4.1. A moving average of 4 values of RMS normalized penetration

gives the clearest representation. The increase in penetration rate is not measured and calculated in all

holes on the interpreted location of the discontinuities.

In Figure 26, an image of the RMS normalised penetration with a moving average of 4 values of the

floor of profile 965-975 is given. The discontinuities are mapped 1.5 to 2m above the floor, much closer

than to the crown of the tunnel. In Figure 26, the discontinuities are better visible and in the right part

38

of the figure, the lines indicate possible positions of the observed discontinuities. Again, a moving

average of 4 values gives the best representation of the fractures, in appendix 7, images of other

parameters can be found. It can be observed that the increase in penetration rate is not present in all

holes along a fracture plane.

Actual blasting of profile 965-975 caused underbreak and dangerous hanging blocks. The round needed

to be re-blasted 3 times before getting the wanted contour shape. This might well be due to the

presence of these fractures.

Reasons for why an increase in normalized penetration is not seen in all holes on the discontinuity

intersections might be the varying aperture or the sampling interval, which is 29.1 mm on average with

a standard deviation of 7.7mm for all holes displayed in the figures.

Fracture 1,2 or 3

Fracture 4,6 or 7

Fracture 8

Figure 25. Top view of RMS normalised penetration with a moving average of 4 values of the contour showing possible locations of mapped discontinuities.

Fracture 1, 2

or 3

Fracture 4, 5

or 6

Fracture 8

Figure 26. View from underneath the tunnel, or the tunnel floor, of RMS normalised penetration with a moving average of 4 values showing possible locations of mapped discontinuities.

39

4.1.1 Overview of observations in MWD data and mapped geology

In Table 5 an overview is given of the discontinuities observed in MWD data.

Table 5. Overview of observations in MWD data and mapped geology.

Location Geological data Discontinuities observed in MWD data.


Borehole remain of about 5m long crossing 7 discontinuities of which one had clay infill (30-40mm) and one had calcite infill

- Clay infilled fracture caused a clear drop in raw and processed MWD parameters - 2 closely spaced fractures gave the expected response in penetration rate and rotation pressure - All observations are clearest in processed parameters with a moving average of 4 values

Tunnel tube A Profile no. 510 to 505

Position and orientation of a calcite vein persisting along the whole tunnel contour

- Discontinuity not visible in MWD data

Tunnel tube A Tunnel face at profile no. 970

Positions and orientations of 9 fractures persisting along the whole face. Infill varies from clayey, reddish unknown, unknown and none.

- Looking at 3D images of the contour, 3 fractures are identified - Looking at 3D images of the floor, 4 fractures are identified - Observations are clearest in RMS normalized penetration with a moving average of 4 values - Unknown which fractures out of the 9 are registered by MWD data

4.2 Televiewer data

4.2.1 Data preparation

Due to the geometry of the televiewer, presence of drilling cuttings in the borehole and other

conditions, it is inevitable that MWD depth and televiewer depth are not aligned. These two depths

have to be adjusted manually. It is chosen to shift the MWD depth since the televiewer depth is fixed

on images which cannot be changed. By looking for and comparing depths of markers, like for example

a large fracture or start of a zone with discontinuities, and corresponding responses of parameters of

the raw MWD data graphs at the approximate same depth, the depth of MWD data is adjusted. The

quality of this shifting highly depends on the presence of a good marker. This process is done by both

looking at raw data and MWD graphs.

In the next 12 subsections, the applied shifts are discussed. Boreholes 1 to 10 have been drilled with an

AMW drilling jumbo. After a first quick inspection, it seems that shifts will be in the order of a couple of

centimetres. The televiewer recordings of holes 3 to 10 are between 4.5m and 4.7m long. The last few

cm were not recorded. The televiewer results of hole 1 and 2 have a different starting point and

additional shifting is needed.

Boreholes 11 to 25 are drilled with an Atlas Copco drilling jumbo. Most televiewer recordings are close

to 4.6m long. Often the whole borehole is filmed and remarkably, the end of the borehole is visible at

this depth of 4.6. The recording of shotcrete or open air at a depth of 4.6 could imply either that the

40

holes are not 5m long or that the recording did not begin. It is assumed that the boreholes are not 5m

long for several reasons even though MWD data implies this. The first reason is that it is unlikely that

the televiewer did not get all the way to the bottom of the holes. Holes were drilled a few degrees up

and therefor flushing successfully removed all the drilling cuttings left. Secondly, the televiewer was

pushed in the hole and the televiewer always stopped abruptly. The third and most important reason

is that presumably the difficulties to find grip between the drilling bit and tunnel wall lead to premature

registration of MWD data. It is observed in several holes that the first 20cm shows strong peaks and

dips which could reflect scraping along the tunnel wall. This matter might lead to larger shifts than holes

1 to 10.

The visual representation of the televiewer and the corresponding MWD graphs can be found in

appendix 2 and 3. The raw data and the processed data for 4 different moving averages of 2, 4, 6 and

8 values are plotted against the shifted depths. For regular use of MWD data, usually a moving average

under 10 values is used.

4.2.2 Borehole 1

The reference point for the televiewer data is set at 0.0m for the bottom of the borehole. For this

reason, the MWD data is shifted with -5.0m. As a marker, the discontinuity with an upper depth of

approximately -2.43m and a lower depth of -1.744 is chosen. It seems that the penetration rate of the

normal hole is influenced by the presence of this fracture. Where penetration rate is relatively stable

before this discontinuity, it is increasing and fluctuating from this fracture on to the end of the hole.

With this discontinuity as a marker, the MWD data of the normal hole is shifted -5.09m, and it assumed

to be a reasonable shift.

The MWD data of the large hole does not show this behaviour at the marker. When applying the same

shift of 5.09m, there seems to be a close correlation between the dip in penetration rate of the large

hole around -0.7 and a discontinuities interpreted aperture. If the dip in penetration rate is chosen as

a marker, the MWD data of the large hole should be shifted -5.1m for a good alignment. With this shift,

both penetration rates show a dip at -3.42 for a reason not seen in geology. With the discontinuity as a

marker and the corresponding dip at -3.42m, the MWD data of the large hole is shifted -5.1m, and it is

assumed to be a reasonable shift.

It can clearly be seen that penetration rate, hammer pressure, feeder pressure, normalized penetration

rate and RMS normalized penetration rate of the normal hole increases from the first fracture to the

bottom of the borehole, whether if there is a discontinuity or not. The penetration rate shows a slightly

larger dip when encountering the fracture between -0.8 and – 0.55. The water flow is reducing only at

the fracture between -2.4 and -1.6.

The MWD data of the large hole does not seem to be influenced except from the dip in penetration

rate, hammer pressure, feeder pressure, rotation pressure. The fracture between -0.8 and – 0.55 is

best represented by the normalized rotation pressure and RMS normalized rotation pressure with the

higher moving average values.

4.2.3 Borehole 2

The reference point for the televiewer data is set at -1.84m for the bottom of the borehole. For this

reason, the MWD data is shifted with -3.16m. For this hole there is no clear marker reflected in the

MWD data of the normal hole. It is assumed that the peak around -0.2m is due to the calcite vein

between -3.2m and -1.8m. With this shift of -3.29m, there is a dip aligned with the interpreted aperture

41

at -0.2m. With these two markers, the MWD data of the normal hole is shifted -3.29m and it assumed

to be a poor shift.

Except for the penetration rate, the MWD data of the large hole shows a decrease between 0.2m and

0.4m. It is assumed that this is due to the calcite vein between 0.6m and 0.4m. With this as a marker,

the MWD data of the large hole is shifted -3.03m, and it assumed to be a poor shift. With this shift the

dips in penetration rate at 1.8m are perfectly aligned.

During drilling of the two holes, difficulties where encountered with finding the correct drilling direction

for the large holes. The difference between the two shifts is 26 cm and could be caused by these

difficulties.

The MWD data of the normal hole shows a saw tooth behaviour in penetration rate, hammer pressure

and feeder pressure for a reason not explained by geology. It is unsure if the peak at -2.1 is part of this

behaviour or if it reflects the presence the calcite vein between -3.2m and -1.8m. The large calcite vein

between 0.6m and 0.4m is not reflected in its totality. There is a sharp dip at 0.2m.

The MWD data of the large hole is much more constant and represents the geology much better than

the large hole. The large calcite vein between -3.2m and -1.8m can be seen back in feeder pressure,

hammer pressure, rotation pressure and processed data.

4.2.4 Borehole 3

For borehole 3, it is hard to find solid markers. It is chosen to shift -4cm, so that there is a small dip at

2.17m is aligned with the vein between 2.0m and 2.28m and so that the small peak is aligned with the

fracture at 3.8m. With these two markers, the MWD data of the normal hole is shifted -0.04m, and it

assumed to be a fair shift.

The MWD data of the large hole is not shifted. There is reflection of a marker in MWD data to determine

the correct depth of the MWD data.

The two markers used for shifting the MWD data of the normal hole are the only two discontinuities

reflected in raw and processed penetration rate. However, these observations are questionable since

the magnitude of the responses does not differ much from the rest of the measurements. The

lithological banding is not reflected consistently.

The penetration rate and normalized penetration show intense fluctuation on a small scale for most of

the length of the borehole. Between 0.8m and 1.2m fluctuation is much less compared to other depths

with lithological discontinuities. It is unclear if this absence or presence of discontinuities leads to a

more fluctuation response.

4.2.5 Borehole 4

As for borehole 3, it is hard to find solid markers in borehole 4. The same calcite veins as in borehole 3

are visible. It is chosen to shift -16cm, so that the small dip between 1.8m and 1.9m is aligned with the

veins between 1.7m and 2.0m. With this marker, the MWD data of the normal hole is shifted -0.16m,

and it assumed to be a shift of fair reliability.

The MWD data of the large hole is not shifted. There is no reflection of a marker in MWD data to

determine the correct depth of the MWD data.

The veins between 1.7m and 2.0m seem to correlate with the behaviour of raw and normalized

penetration. For the discontinuities, there is no clear response in MWD data detected.

42

Like for borehole 3, is unclear if the intense fluctuation in penetration rate and normalized penetration

rate is cause by lithological discontinuities.

4.2.6 Borehole 5

The larger fracture between 3.49m and 3.62m can clearly be seen in most raw MWD parameters of

both the normal and large hole. With this marker, the MWD data of the normal hole and large hole is

shifted respectively -0.102m and 0.06m. Both are assumed to be a perfect shift.

There is a clear response for the fracture between 3.49m and 3.62m, namely the penetration rate

increases, the hammer pressure drops, the feeder pressure drops, the processed penetration increases,

the RMS normalized rotation pressure increases for both normal and large hole MWD data. The water

flow and processed water are only influenced by this fracture while drilling the normal hole. The

rotation pressure and processed rotation pressure are only influenced by this fracture while drilling the

large hole. The response in MWD data for the fracture between 4.57m and 4.65m is less clear. It is

shifted forward or sometimes absent, but the response for this fracture is similar in direction. There is

a clear difference in response between the depths of these two factures and the rest of the borehole.

The lithological discontinuities do not show a sharp fluctuation but over the larger area, the penetration

rate and normalized penetration rate of the normal hole seem to be higher and more fluctuating. This

might be due to a small difference in rock properties between the uniformly coloured grey parts and

the non-uniformly coloured grey part.

4.2.7 Borehole 6

Similar to borehole 5, the large fracture between 3.3m and 3.42m is used to determine the shift for

both normal and large hole. With this marker, the MWD data of the normal hole and large hole is shifted

respectively -0.16m and 0.02m. Both are assumed to be a perfect shift.

The larger fracture between 3.3m and 3.42m is the same fracture as the fracture in borehole 5

(between 3.49m and 3.62m). The response in MWD data for the fracture around 4.4m is similar for the

fracture in borehole 5 (between 4.57m and 4.65m). It looks like the two calcite veins around 4.27m and

4.35m are included in the response of the fracture around 4.4m. There is a clear difference in response

between the depths of these two factures and the rest of the borehole.

Like for borehole 5, the lithological discontinuities do not show a sharp fluctuation. The response over

a larger area is clearer for borehole 6 than for borehole 5. Here, the difference between rock properties

might also be the cause for the difference in response.

4.2.8 Borehole 7

The calcite vein in between 2.7m and 3.12m does not seem to give a response in MWD data. The only

option to shift the MWD data is by looking at the dip around 1.4m in penetration rate. This dip is also

visible in borehole 8 at more or less the same spot. It is possible that this is related to the part of

limestone that is uniformly coloured grey (between 1.3m and 1.47m), which like in borehole 5 and 6

might cause the possible low penetration rate. If the borehole is shifted with this marker, it looks like

the first two lithological discontinuities give an expected response in penetration rate. With this marker,

the MWD data of the normal hole is shifted 0.159m, and it assumed to be shift with fair reliability.

The MWD data of the large hole is not shifted. There is reflection of a marker in MWD data to determine

the correct depth of the MWD data

There seem to be no relation between the discontinuities and any of the MWD parameters.

43

4.2.9 Borehole 8

Like mentioned before, the situation for borehole 7 and 8 is similar. If the uniformly coloured grey part

is chosen as a marker, the shift for the MWD data of the normal hole is 0.11m. For borehole 8, the large

hole MWD data seems to give the same response. For that reason, the MWD data of the large hole is

shifted with -0.03m. These shifts are classified to have a fair reliability.

There seem to be no relation between the discontinuities and any of the MWD parameters. The data

shows intense fluctuation, but this cannot be correlated to specific discontinuities.

4.2.9 Borehole 9 and 10

Unfortunately, there are no useful markers to connect the MWD data to the televiewer measurements.

No shift is applied. There is no clear relation between discontinuities and MWD data or sections without

discontinuities and MWD data.

4.2.10 Borehole 11 to 15

One fracture is visible in boreholes 12 to 15 at the depths 4.4m, 3.6m, 2.5m and 2.4m. The response in

penetration rate is a small peak in normal and large hole data. With this fracture as a marker, the MWD

data of normal sized holes of borehole 12 to 15 is shifted respectively 0.25m, 0.2m, 0.22m and 0.3m.

For the large holes these shift are 0.12m, 0.17, 0.06m and 0.21m. These shifts are assumed to be

perfect. Borehole 11 does not include this fracture. When shifting the normal hole data with 0.27m,

the penetration rate shows small peaks close to 3.3m and 3.85. On these depths a calcite vein and

possible fracture are located. This shift is assumed to be of fair reliability. The large hole data does not

show responses to these fractures in penetration rate. Instead, the rotation pressure seems to be

influenced by these fractures. With this shift of 0.27m, it is assumed to be a fair shift.

As mentioned in chapter 3.4.2., this location was chosen since the auteur had observed several

discontinuities at there. When studying boreholes 11 to 15, at least the discontinuity types calcite veins,

bedding discontinuities, an open fracture and possibly closed fractures are encountered. The open

fracture with calculated apertures of 1cm, 1.9cm and 3m gives the clearest response. In boreholes 12

to 15 the following features are found as a response to this open fracture:

1. The penetration rate peaks.

2. The large hole hammer pressure drops and increases about 0.2 to 0.25m after the fracture is

encountered.

3. The large hole feeder pressure drops.

4. The rotation pressure peaks.

5. The normal hole water flow drops and gradually increases to its old level.

6. The processed penetration and rotation pressure peak.

Water pressure does not seem to be influenced by the fracture. Other discontinuities do not show an

uniform response to all boreholes. Sometimes the peaks are followed by a short small drop.

A lithological boundary at 4.8m deep in borehole 12 gives a strong response both in normal and large

hole data. Penetration rate and rotation pressure peaks. Also the Hammer pressure, feeder pressure,

water pressure and water flow of normal hole data give strong responses. Several normal hole

parameters do not return to the old level. This gives a less good response to this fracture in processed

data. On the other hand, large hole data gives a good response. In borehole 15, a calcite vein at 3.8m

gives a response in penetration rate and normalized penetration rate. Besides these two cases, there

is no clear response to a discontinuity found.

44


The MWD data of borehole 16 is not shifted. The black area around 2.2m might be a fracture and

responses in MWD data of penetration rate, hammer pressure, feeder pressure and rotation pressure

are aligned without shifting data.

The problems with drilling large hole number 17 make it very difficult to find a proper shift. The depth

of the large hole is expected to be around 4.3m. There is however no possibility to confirm this with

MWD data, since the drilling bit has been pulled back after 5m. Two MWD logs are made and these

might overlap and that makes depth estimation of the large hole impossible. In addition, there is no

marker with response in MWD data present in the large hole data. Considering the normal hole data, it

is also difficult to come up with a good shift. Both starting depth and ending depth of drilling are

unknown. Problems with finding grip on the tunnel wall and premature depth registration make the

starting point unknown and the not finished large hole make the end depth unknown. It is chosen to

not shift data from borehole 17.

A fracture present in borehole 18 and 19 is giving responses in raw MWD parameters. With a shift of

0.2m, the rotation and feeder pressure of the normal hole data of borehole 18 are aligned with this

fracture. A 0.17m shift of large hole MWD data aligns a peak in penetration rate which is most likely

caused by this fracture. The normal hole size MWD data of borehole 19 is shifted with 0.24m a peak in

rotation pressure is aligned with this fracture. The responses to this fracture in large hole data are less

clear, but when shifting 0.26m, small changes in penetration rate and rotation pressure are aligned with

this fracture. These last shift is considered to be of fair reliability. The other shifts are of good quality.

Even though the same fracture is present in borehole 20, it is chosen to use the larger fracture at 4.5m

with an infill as a marker. There are clear responses in penetration rate, feeder pressure and rotation

pressure. With a shift in MWD data for normal hole size data of 0.22m and for large hole sized data of

0.16m, this shift is of good quality.

In boreholes 16, 18, 19 and 20 a possible fracture is at 2.2m, 4.2m, 2.1m and 1.5m is present.

Considering the orientation of the fracture and the positioning of the boreholes, this is most likely on

fracture. After some calculations, this fracture seems to have an aperture of 1.4cm, 2.3cm and 2.7cm

in boreholes 18, 19 and 20. MWD data of borehole 16 and 18, and normal hole sized MWD data of

borehole 19 show the following responses to this fracture:


2. The large hole hammer pressure drops and increases about 0.2 to 0.25m after the fracture is

encountered, except in borehole 19 and 20.

3. The feeder pressure drops, except in borehole 20.



Apart from a small decrease in water flow at the fracture in borehole 16, water parameters do not show

a response on this fracture. Sometimes the peaks are followed by a short small drop. Other

discontinuities do not show an uniform response of all boreholes.

In borehole 18 at 2m, a planar discontinuity gives a response in normal size hole MWD data. It is not

clear what kind of discontinuity this is. The penetration rate decreases, rotation pressure increases and

processed penetration and rotation pressure. In borehole 19, lithological discontinuities around 1.5m

give a strong response in normal hole size MWD data. Penetration rate peaks and rotation pressure

45

drops. The fracture at a depth of 1.5m gives a response both in normal and large hole size data.

Penetration rate peaks, rotation pressure peaks and processed data gives responses.


In all boreholes 21 to 25 there are fractures present, which are good markers. In borehole 21, the

fracture at a depth of 2.85m is used as a marker. For boreholes 22 to 24, the fractures used as a marker

are at a respective depth of 4.7m, 2.4m and 3.6m. For hole 25, two markers are needed since there are

two log files created while drilling for the normal hole size. The markers used are at 1.3m and 3.3m.

Close to 15 fractures with a greenish infill, several responses in MWD data can be observed in normal

hole size MWD data of holes 21 to 25, namely:

1. Short small peak in penetration rate, for all 15 fractures.

2. At 4 fractures the feeder pressure drops slightly.

3. A peak in rotation pressure, for 13 out of 15 fractures.

4. A drop in water flow which gradually increases to the old level, for 8 fractures.

The processed data gives shows the same, but larger, responses at the same fractures. Sometimes the

peaks are followed by a short small drop. The large hole data

5. Short small peak in penetration rate, for all 13 fractures.

6. A drop in hammer pressure for a period after encountering a fracture, for 7 of the fractures.

7. A peak in rotation pressure, for 11 out of 15 fractures.

The fractures have an aperture between 1cm and 7cm. As mentioned in section 3.4.2, the depth

registration of the televiewer was influenced by movement of the cable between the reel and tunnel

wall. The results of this are visualised in Figure 26. In borehole 21, the fracture indicated with the red

arrows has been used to shift the MWD data. This fracture is aligned with its peak in penetration rate.

The other two fractures with soft infill are not aligned with their peaks. This mismatch between the

depth of a fracture and its MWD response has been corrected by deleting a few cm of MWD data in

between these fractures. The data points which are deleted are taken spread out over this distance

between fractures, so data is scaled as evenly as possible. The data points deleted are not close to other

discontinuities. In total 11 data points are deleted in holes 21 to 23 and 25. The MWD data and

televiewer data is given in appendix 3.

In borehole 21, an approximately 4cm wide fracture zone, gives clear responses in normal hole size

MWD data. Around 0.8m, the penetration rate peaks and drops immediately after encountering intact

rock again. In borehole 22 and 24, this fracture zone around 1m and 1.3m deep, strongly influence the

large hole size MWD data. Feeder pressure and rotation pressure fluctuate and this causes responses

in processed data. In borehole 24 the penetration rate peaks. The feeder pressure and rotation pressure

peak strongly when encountering normal rock mass after the fracture zone. In borehole 23, both

normal and large hole size MWD data show responses in penetration rate, feeder pressure and rotation

pressure.

The lithological discontinuities in boreholes 21 and 24 seem to correlate with peaks in normal hole size

penetration rate and processed penetration rate. The lithological discontinuities in boreholes 22, 23

and 25 do not correlate with penetration rate as with boreholes 21 and 24.

46

In borehole 24 a new fracture zone is encountered at 0.7m. The normal hole sized penetration rate and

rotation pressure peak. The fracture zone intersects with a fracture with soft infill. This seems to affect

the rotation pressure, but there is a clear difference in penetration rate for the filled fracture and the

fracture zone.

4.2.13 Overview of observations in MWD data and mapped geology with televiewer

Overall, it can be stated that 9 regular fractures and 15 fractures with soft infill give some or all of the

listed characteristic responses below. Processed MWD data with a moving average of 4 values gives the

most accurate representation of the aperture when comparing it to mapped geology. The data

collected with the televiewer shows discontinuities with great detail. The characteristic response of

MWD data to fractures was as follows:


2. The large hole hammer pressure drops and increases about 0.2 to 0.25m after the fracture

is encountered.

3. The feeder pressure drops.


5. The normal hole water flow drops and increases gradually.


The magnitude of the responses are found to be different for each fracture and no pattern in magnitude

between open and filled fractures is found. No characteristic response is found for lithological

boundaries and no consistent response is found for calcite veins. A difference in response in MWD data

for zones with low and high discontinuity spacing is not found.

A summary of the observations is given in Table 6.

4.3 Borehole camera data

As mentioned before it is difficult to distinguish discontinuities in the video footage. It is therefore

chosen to look for fluctuations in MWD data that could indicated one or multiple discontinuities and

then try to find an explanation in video footage. The difference in drilling and recorded depth is

corrected by rescaling the recorded depth over the drilling depth. The two points where the drilling and

recorded depth can be related to each other are the bottom and start of the holes. This still gives an

incorrect representation of the depth, but it is the best way to even out the difference. The MWD data

is presented in appendix 4. The depth of the large and normal hole might be shifted a little. For example

in borehole 1, the penetration rate of the normal hole shows a peak at 4.82m while the large hole

shows a peak at 4.74m. These two peaks might be a response due a geological change at one depth.

This difference can be explained by the small uncertainty of starting position of the drill bit.

Figure 26. Illustration of mismatch between televiewer depth and MWD data.

47

Apart from a few doubtful correlations between video and MWD data, no peaks or drops in parameters

are explained by discontinuities. Twice, a zone of white minerals is located at a depth where MWD data

shows peaks and drops, i.e. borehole 1 at 4.8m and borehole 4 at 3.5m (see Figure 27). Comparing

zones with fracturing and zones without fracturing is not possible since less fracture phyllite is not

encountered.

Table 6. Overview of observation in MWD data and geological data from the televiewer inspections.

Location Geological data Discontinuities observed in MWD data.

Tunnel tube A Close to profile no. 700 Borehole 1 and 2

2 borehole images with calcite veins and lithological discontinuities

- MWD parameters increase after encountering the first discontinuity - A calcite vein gives a clear response in processed data, feeder, hammer and rotation pressure

Tunnel tube A Close to profile no. 600 Borehole 3 to 10


- MWD parameters show a clear response to 3 open fractures found in borehole 5 and 6

Tunnel tube A Close to profile no. 930 Borehole 21 to 25


- The presence of 15 fractures with soft infill gives clear and characteristic responses in MWD data as described above - Lithological discontinuities in borehole 21 and 24 seem to correlate with peaks in penetration rate

Tunnel tube A Close to profile no. 975 and 960 Borehole 11 to 20

10 borehole images with calcite veins, lithological discontinuities, fractures with softer clayey infill and fractures without infill

- Fractures present in borehole 12, 15, 16 and 18 to 20 give a clear and characteristic response as described above

Figure 27. Zones with light coloured minerals which might give a response in MWD data. Left in borehole 1 at a depth of 4.8m and right in borehole 4 at a depth of 3.5m.

48

5. Statistical validity assessment of MWD data

As mentioned in chapter 2.7, three multivariate statistical methods believed to be useful for analysing

MWD data. As discussed in chapter 4, the datasets of MWD data and geological information obtained

by the televiewer are most suited for statistical analyses. In this chapter, the use and results of principle

component analyses, cluster analyses and multiple logistic regressions will be discussed. A visual

comparison of MWD data and discontinuities such as the one performed in chapter 4, does reveal

valuable information. Nonetheless, it is very difficult to take into account the exact responses of all 30

MWD parameters that are extracted for this thesis. Statistical analyses can help to analyse multiple

MWD parameters for multiple discontinuities at the same time. A key point is that the logistic regression

is performed several times including all or only a selection of the discontinuities, based on chapter 4.

First the different datasets will be defined.

The purpose of the statistical analyses is to identify single deterministic discontinuities. The detection

of discontinuities helps to characterise rock masses before excavation, and allows. Predicting these rock

mass conditions can improve the drill and blast process, as discussed in section 1.

5.1 Data sets

In this chapter different data sets will be used which are divided based on hole size and drilling jumbo.

Datasets 1 to 4 will be used for PCA, cluster analyses and data training with the end result of different

regression equations.

1. Dataset 1 contains the MWD data of the normal boreholes 1 and 3 till 8. Borehole 9 and 10 are

not included for the reason that a valid shift could not be found in the previous chapter.

Borehole 2 is not included. It is found that the MWD data of the normal hole of borehole 2

incorrectly represents a normal drilling process. The saw-toothed shaped raw data from 2.6m

to 0m is due to the readjustments the operator needed to make to keep the drilling arm in line

with the drilling direction. Boreholes 1 and 3 till 8 are subjected to a visual inspection where

slow starts and parts where only MWD or televiewer data is available are deleted.

2. Dataset 2 contains MWD data from the large boreholes 1, 2, 5 and 6. The other holes are not

considered since the shifts performed in the previous chapter are not reliable. Again, the slow

starts and parts with only MWD or televiewer data is deleted.

3. Dataset 3. In addition to slow starts, the parts drilled in the sandstone are deleted since average

penetration rate differs from limestone. The MWD data consists of normal hole size data from

holes 11 to 15, 18 to 22 and 24.

4. Dataset 4 consists of MWD of the large holes 11 to 15, 18 to 22 and 24. Slow starts and data of

the sandstone has been deleted.

Datasets 5 to 7 will be used to test the resulting regression equations from dataset 1 to 4.

5. Dataset 5 consists of borehole 9 and 10 and will be used to test regression equations resulting

from regression analyses of dataset 1.

6. Dataset 6 consists of borehole 3, 4 and 7 to 10 and will be used to test regression equations

resulting from regression analyses of dataset 2.

7. Dataset 7 exists of normal hole sized data from boreholes 16, 17, 23 and 25. This dataset will

be used to test the regression equations, which are made based on datasets 3.

8. Dataset 8 exists of large hole sized data from boreholes 16, 17, 23 and 25. This dataset will be

used to test the regression equations, which are made based on datasets 4.

49

These datasets will be analysed and subjected to a logistic regression analysis. Unlike the principle

component analysis and cluster analysis, an input of geological information is needed. Geological

information that is to be predicted by the multivariate logistic regression needs to be vectorised with

information on the same depths as the MWD sampling depths. The issue with the televiewer data is

that there is a difference in sampling depth accuracy and sampling depth. The televiewer data does not

fall together with a MWD sampling interval. One way of solving this is to assign a 1 in case a MWD

sampling interval is between the upper and lower depth of the discontinuity. See Table 7. If the upper

depth of a discontinuity is 2.891m and the lower depth 2.775m, a 1 is assigned to the discontinuity

location vector at the MWD sampling depths of 2.78 to 2.89. To the MWD sampling depths of 2.77 or

smaller and 2.90 and larger a 0 is assigned, until another discontinuity upper or lower boundary is

encountered. If the lower depth of a discontinuity is larger than the upper depth of a neighbouring

discontinuity, the number 1 should still be assigned.

Table 7. Table showing how depth sampling of discontinuity location vector is done.

Televiewer images. Upper depth at 2.891m and lower depth at 2.775m

Depth (m)

Discontinuity location vector

2.75 0

2.76 0

2.77 0

2.78 1

2.79 1

2.8 1

2.81 1

2.82 1

2.83 1

2.84 1

2.85 1

2.86 1

2.87 1

2.88 1

2.89 1

2.9 0

2.91 0

The problem arises when discontinuities have a large dip relative to the drilling direction. In this case

the difference between the upper and lower depth of a discontinuity is large and the numbers 1

dominate the discontinuity location vector, while MWD data might not reflect the presence as clear as

with a discontinuity with a steep discontinuity plane. In the data set this happens incidentally. A second

option for creating the discontinuity location vector is to only assign a 1 to the MWD sampling depth

closest to the single depth value of a discontinuity, or average of the lower and upper depth, from the

televiewer results. The downside with this approach is that if discontinuities influence MWD data on a

larger interval due to aperture or orientation, it could cause large mismatches between discontinuity

and MWD data. In the analysis both discontinuity location vectors will be tested and evaluated.

It is unlikely that MWD data can predict the orientation of a discontinuity based on a single hole. The

sensors do not register on which side of the borehole the drilling parameters are influenced first when

encountering a fracture. Besides this information, the televiewer data leaves 3 predictable options:

50

1. The location of a discontinuity.

2. The aperture of a discontinuity.

3. The nature of infill: softer or harder than the rock itself or absence of infill.

Creating discontinuity location vectors should lead to the possibility of testing these three options. The

comparison in chapter 4 showed an order in predictability or intensity of responses for closed, infilled

or open discontinuities. Closed discontinuities or infill fractures give a less distinctive response to

fractures without infill. This is confirmed by the literature study where Schunnesson described the

damped response of infilled fractures. This order in predictability will also be tested with the cross

tabulation of the cluster analysis. Based on this order of predictability, 4 discontinuity location vectors

are defined, where the first one contains all discontinuities and the last vector contains the open

fractures, which gives the best response. The 4 vectors included:

1. all discontinuities

2. discontinuities with an aperture>0

3. discontinuities with an aperture>0 and soft infill or no infill

4. discontinuities with an aperture>0 and no infill.

Location, aperture and nature of infill can be tested simultaneously with these four discontinuities

vectors.

5.2 Principle component analysis

PCA is used to reduce the number of input variables in the regression analysis and at the same time

retain a significant and satisfying part of the variation from the original MWD parameters. First parallel

analyses are used to determine an appropriate number of principle components. Then the type of

rotations used in the principle component are discussed. Finally the results of the principle component

analyses for each dataset are given.

5.2.1 Determining the number of principle components

Since the data is mostly non-Gaussian, it is chosen to perform parallel analyses based on both normally

distributed random data and permutations of the input data. For these analyses the SPSS syntax written

by B.P. O’Connor is used (O'Connor, 2000). It is chosen to create 1000 datasets and the statistically

significance level is set to 95%.

The scree plots of the two parallel analysis and the scree plot of the dimension reduction by SPSS are

displayed in Figure 28. All methods indicate that there are 7 statistically significant components. The

parallel generated eigenvalues of the 8th component are larger than eigenvalue of the 8th component

of the original dimension reduction analysis, which is described as the benchmark for the decision of

the number of components (section 2.7.1). Dimension reduction of dataset 1 leads to 7 statistically

significant principle components.

Dataset 2 is smaller and therefore has a shorter computation times. In appendix 8 the outcome of both

parallel analyses is given. Unlike for data set 1, the SPSS evaluation and parallel analyses are not

coherent. SPSS indicates to use the 6 components with eigenvalues larger than 1, while both parallel

analyses indicate only 5 statistically significant components. Dimension reduction of dataset 2 leads to

5 components.

For both dataset 3 and 4, SPSS indicates that 7 principle components are appropriate. Both parallel

analyses indicate that 7th principle components is not statistically significant. The 7th eigenvalues of the

51

parallel analyses with the 95th percentile exceeds the eigenvalue of the SPSS raw data. The scree plots

can be found in appendix 8. Dimension reduction leads to 6 principle components.

5.2.2 Orthogonal and oblique rotation

In Table 8 the correlations between principle components with an oblique rotation are displayed for

the datasets. The correlation matrix of the principle components of the orthogonal rotation is an

identity matrix. The highest correlations for dataset 1 are 0.22, 0.27, -0.28 and 0.29. For dataset 2 the

highest correlations are -0.24 0.23 and -0.21 and the remaining correlations are below 0.2. As described

in the section 2.7.1 a correlation of 0.32 means there is an overlap of 10% in variance among principle

components. Principle components 1 and 5 in dataset 3 have an overlap of more than 10% in variance,

as the Pearson correlation is -0.37. The same counts for components 5 and 6 with a correlation of 0.42.

These do not exceed 0.8 which would likely indicate collinearity, as mentioned in section 2.7.4. It is

chosen to continue with the outcomes of the PCA with the oblique rotation direct oblimin, since this

might represents the inherent correlations between the principle components better.

Table 8. Component correlation matrices with an oblique rotation for all datasets. Highlighted in green are the highest Pearson correlations.

Component Correlation Matrix Dataset 1

Component 1 2 3 4 5 6 7

1 1.00 .03 .27 .15 -.17 -.28 .29

2 .03 1.00 -.02 .12 -.19 .08 -.02

3 .27 -.02 1.00 .22 -.16 -.07 .1

4 .15 .12 .22 1.00 -.13 .12 .12

5 -.17 -.19 -.16 -.13 1.00 .05 -.05

6 -.28 .08 -.07 .12 .05 1.00 -.16

7 .29 -.02 .10 .12 -.05 -.16 1.00

0

1

2

3

4

5

6

7

8

9

10

11

12

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Eige

nva

lue

Component number

Scree plot PCA and parallel analysis dataset 1

Eigenvalues calculatedby SPSS dimensionreduction

Eigenvalues parallelanalysis raw datapermutation

Eigenvalues parallelanalysis randomnormal datageneration

Figure 28. Scree plot for dataset 1 to determine the number of statistically significant components.

52


Component 1 2 3 4 5

1 1.00 .07 .23 -.02 -.08

2 .07 1.00 .06 -.24 -.22

3 .23 .06 1.00 .03 -.09

4 -.01 -.24 .03 1.00 .11

5 -.08 -.22 -.09 .11 1.00


Component 1 2 3 4 5 6

1 1.00 -.01 -.00 -.29 -.37 .15

2 -.01 1.00 .04 .22 -.15 -.22

3 -.00 .04 1.00 -.22 -.14 .01

4 -.29 .22 -.22 1.00 .08 -.30

5 -.37 -.15 -.14 .08 1.00 .06

6 .15 -.22 .01 -.30 .06 1.00


Component 1 2 3 4 5 6

1 1.00 -.25 -.18 -.17 .07 .22

2 -.25 1.00 -.05 .18 .06 -.03

3 -.18 -.05 1.00 -.04 -.27 -.07

4 -.17 .18 -.04 1.00 .04 .04

5 .07 .06 -.27 .04 1.00 .42

6 .22 -.03 -.07 .04 .42 1.00

5.2.3 PCA outcome dataset 1

The Kaiser-Meyer-Oklin measure of sampling adequacy equals 0.801. All communalities are larger than

0.78 except for RMS normalized water with a moving average of 8 values and water pressure, where

0.508 and 0.153 percent of their variances is being accounted for by the PCA (appendix 8). Both the

KMO test and the communalities indicate that the use of a PCA is justified.

In Table 9 it can be seen how much of the total variance of the variables is explained by which

component. Automatically SPSS first computes the component which contains most of the variance,

36.082%. The total variance explained by all 7 principle components is 86.8%. The 7th component

explains only 4.2% of the variance.

53

Table 9. Total variance explained by principle components.

Component Extraction Sums of Squared Loadings

Component Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

Dataset 1 Dataset 3

1 10.8 36.1 36.1 1 8.0 25.7 25.7

2 4.4 14.8 50.9 2 5.9 18.9 44.5

3 3.5 11.6 62.5 3 4.6 14.9 59.4

4 2.4 7.9 70.4 4 2.7 8.7 68.2

5 1.9 6.5 76.8 5 2.0 6.4 74.6

6 1.7 5.7 82.6 6 1.5 4.7 79.2

7 1.3 4.2 86.8 Dataset 4

Dataset 2 1 7.9 25.5 25.5

1 11.6 38.7 38.7 2 5.6 18.2 43.7

2 6.2 20.7 59.4 3 4.0 12.8 56.5

3 3.8 12.6 72.0 4 2.9 9.4 66.0

4 2.6 8.7 80.8 5 2.3 7.5 73.4

5 1.5 5.0 85.8 6 1.3 4.2 77.7

In Table 10 the pattern matrix of the PCA is given. This matrix contains the correlations between

components and explanatory variables. It should be noted that ideally a variable should be correlated

with only one component. Both for an oblique rotation, as show in the table, and an orthogonal rotation

this is not the case. However, most variables seem to be correlated highly with one component. These

variables are marked green. There are 6 variables that show a similar correlation with 2 or 3

components, these are marked in yellow. As can be seen Table 10, water pressure has no good

correlation with any of the components as well as RMS normalized water MA 8.

Which variable is represented by which component is a delicate interpretation. A few careful

statements can be made about each component.

- Component 1. Three out of four normalized rotation pressures are positively correlated with

component 1. The fourth normalized rotation pressure is not correlated significantly with

component 1. In addition, processed water pressure and flow seems to be good and medium

correlated with component 1.

- Component 2. Two out of four normalized water variables have a medium correlation with

component 2. Four of the raw drilling parameters are strongly correlated with component 2.

- Component 3. This component seems to represent the normalized penetration and the

penetration rate. One of the normalized penetrations is also correlated with component 1.

- Component 4. Four out of three RMS normalized penetrations are strongly correlated with this

component.

- Component 5. This component is correlated with two RMS normalized rotation pressures, one

normalized rotation pressure and the raw rotation pressure.

- Component 6. This component is correlated with the remaining RMS normalized rotation

pressure and the two remaining RMS normalized water variables, although one is shared with

2 other components.

54

- Component 7. The remaining RMS normalized penetration and rotation pressure are correlated

with this component.

Table 10. Pattern matrix giving the correlation between parameters and principle components for dataset 1.

In appendix 8 the component score coefficient matrix is given. These weights can be used to calculate

the component scores. The weights should be multiplied with the standardized variables. The total sum

of these multiplications lead to the scores.

Component

1 2 3 4 5 6 7

Normalized rotation pressure MA 2 normal hole (%) .935 -.102 .135 .151 .080 -.031 -.188

Normalized rotation pressure MA 6 normal hole (%) .817 -.061 .102 .134 .023 -.084 .223

Normalized rotation pressure MA 8 normal hole (%) .809 -.067 .098 .147 .067 -.088 .234

RMS normalized water MA 6 normal hole (%) -.796 .044 -.083 -.008 .164 .142 -.145

RMS normalized water MA 2 normal hole (%) -.787 .004 -.118 -.087 .126 .122 -.201

Normalized water MA 2 normal hole (%) -.777 .168 -.094 -.086 .139 .128 -.190

Normalized water MA 4 normal hole (%) .662 .510 .126 -.034 -.034 .586 .031

Normalized water MA 8 normal hole (%) .651 .487 .147 .076 -.037 -.010 .225

Normalized water MA 6 normal hole (%) .088 .881 .121 .008 .126 .042 .074

Water flow normal hole (l/min) -.024 .809 .187 -.033 -.186 -.143 .013

Feeder pressure normal hole (bar) .207 -.757 .299 -.043 .202 -.058 .053

Hammer pressure normal hole (bar) .271 -.688 .404 -.071 -.035 -.152 -.022

Water pressure normal hole (bar) -.039 .265 -.064 .240 -.009 -.039 -.132

Normalized penetration MA 2 normal hole (%) .130 .009 .895 .076 -.052 -.004 -.180

Penetration rate normal hole (m/min) .172 -.167 .848 .030 -.075 -.068 -.163

Normalized penetration MA 8 normal hole (%) .045 .085 .835 .150 -.081 -.020 .199

Normalized penetration MA 4 normal hole (%) -.767 .056 .819 .013 .046 .100 -.012

Normalized penetration MA 6 normal hole (%) .200 .054 .777 .131 -.098 -.037 .210

RMS normalized penetration MA 6 normal hole (%) .109 -.005 .069 .913 -.069 -.101 .009

RMS normalized penetration MA 8 normal hole (%) .132 -.015 .011 .885 -.102 -.107 .037

RMS normalized penetration MA 4 normal hole (%) .107 .030 .145 .826 -.021 .081 -.100

RMS normalized rotation pressure MA 8 normal hole (%) -.123 -.123 -.041 .154 -.911 .186 .106

RMS normalized rotation pressure MA 6 normal hole (%) -.039 -.134 -.010 .178 -.898 .150 .019

Normalized rotation pressure MA 4 normal hole (%) -.139 -.284 -.138 .171 .755 .198 .034

Rotation pressure normal hole (bar) -.120 -.282 -.148 .157 .742 .195 .048

RMS normalized rotation pressure MA 4 normal hole (%) .003 -.067 -.055 -.081 -.218 .844 -.286

RMS normalized water MA 4 normal hole (%) -.139 -.015 -.053 -.105 .042 .841 -.153

RMS normalized water MA 8 normal hole (%) -.193 -.005 .066 .360 .082 .452 .341

RMS normalized penetration MA 2 normal hole (%) -.107 -.012 .107 .142 .090 .152 -.897

RMS normalized rotation pressure MA 2 normal hole (%) -.155 -.027 .020 -.032 .041 .178 -.855

55


The Kaiser-Meyer-Oklin measure of sampling adequacy is 0.827. This indicates that a PCA is an

appropriate data reduction method. The communalities are given in appendix 8, the lowest

communality is 0.267 for the RMS normalized water variables with a moving averages of 8 values. The

other variables have communalities of 0.606 and higher. This confirms the justification for the use of

PCA.

The total variance explained by the 5 principle components is 85.8%, where the 1st component explains

38.7% and the 5th component explains 5.0% of the total variance (Table 9).

- Component 3 represents normalized water and water flow.

- Component 4 represents RMS normalized penetration.

- Component 5 represents RMS normalized water.

- Component 6 represents RMS normalized rotation pressure.

In appendix 8 the component score matrix is given.

Table 11 is the pattern matrix of the PCA of dataset 2. Unlike for dataset 1 there are several variables,

which are correlated well with one component and correlated medium with another component. The

following statements can be made about which variables are represented by which components.

- Component 1. This components contains 10 variables. Three out of the four normalized

rotation pressures and normalized water variables are correlated with this components. In

addition there are 4 individual variables correlated to this component.

- Component 2. Three out of four RMS normalized penetrations and RMS normalized rotation

pressures are correlated by this component.

- Component 3. This component is correlated with water pressure and normalized water.

- Component 4. Three of the raw drilling parameters are correlated with this component.

- Component 5. Penetration rate and three out of four normalized penetrations are correlated

are partly correlated with this component.



The Kaiser-Meyer-Oklin measure of sampling adequacy equals 0.824. All communalities are larger than

0.602 except for water pressure and rotational speed, where 0.087 and 0.159 percent of their variances

is being accounted for by the PCA (appendix 8). Both the KMO test and the communalities indicate that

the use of a PCA can be justified.

In Table 9 it can be seen how much of the total variance of the variables is explained by which

component. Automatically SPSS first computes the component which contains most of the variance,

25.7%. The total variance explained by all 6 principle components is 79.2%. The 6th component explains

only 4.7% of the variance.

In Table 12 the pattern matrix of the PCA is given. While the division of parameters among components

for dataset 1 and 2 was mixed, dataset 3 groups parameters according to suite.

- Component 1 represents normalized penetration, penetration rate, hammer pressure and

feeder pressure.

- Component 2 represents normalized rotation pressure and rotation pressure.

- Component 3 represents normalized water and water flow.

56






Component

1 2 3 4 5

Normalized rotation pressure MA 6 large hole (%) .972 .065 -.006 .096 .003

Normalized rotation pressure MA 8 large hole (%) .970 -.002 -.103 .157 .012

Normalized rotation pressure MA 2 large hole (%) .954 .112 .068 .051 -.015

Normalized water MA 2 large hole (%) -.945 -.111 .412 .102 -.015

RMS normalized water MA 6 large hole (%) -.927 -.046 -.142 .077 .074

RMS normalized water MA 2 large hole (%) -.913 -.078 -.187 .074 .036

RMS normalized penetration MA 2 large hole (%) .857 .208 .153 -.086 -.054

RMS normalized rotation pressure MA 2 large hole (%) .854 .194 .163 -.105 -.041

Normalized penetration MA 4 large hole (%) -.843 .095 -.147 .036 -.516

RMS normalized water MA 4 large hole (%) -.718 .236 -.014 -.024 .220

RMS normalized penetration MA 6 large hole (%) .192 .855 .055 .046 -.130

RMS normalized penetration MA 4 large hole (%) .092 .805 .040 -.009 -.130

RMS normalized rotation pressure MA 6 large hole (%) -.126 .792 -.071 -.280 -.017

RMS normalized rotation pressure MA 8 large hole (%) .234 .716 -.042 -.158 -.143

RMS normalized rotation pressure MA 4 large hole (%) -.397 .704 -.062 -.255 .088

RMS normalized penetration MA 8 large hole (%) .429 .703 .070 .134 -.194

Normalized water MA 6 large hole (%) -.027 -.048 .972 .032 -.068

Water pressure large hole (bar) .050 .040 -.950 -.003 .038

Normalized water MA 4 large hole (%) .367 .086 .805 -.032 -.019

Normalized water MA 8 large hole (%) .522 .011 .718 -.010 -.070

RMS normalized water MA 8 large hole (%) -.099 .212 .357 .254 .274

Feeder pressure large hole (bar) -.070 -.290 .147 .794 .011

Rotation pressure large hole (bar) .043 .033 -.470 .789 .050

Hammer pressure normal large (bar) .001 -.203 .311 .767 -.213

Normalized rotation pressure MA 4 large hole (%) .017 -.056 -.612 .618 .078

Penetration rate large hole (m/min) .210 .188 .110 .141 -.835

Normalized penetration MA 6 large hole (%) -.423 .258 -.034 -.002 -.831

Normalized penetration MA 2 large hole (%) .203 .255 .064 -.093 -.800

Normalized penetration MA 8 large hole (%) -.661 .202 -.079 .041 -.715

Water flow large hole (l/min) .269 -.161 .460 -.052 -.603

57


Component

1 2 3 4 5 6

Normalized penetration MA 4 normal hole (%) .974 .032 .059 .032 .025 .011

Normalized penetration MA 6 normal hole (%) .955 .018 .048 .014 -.002 .015

Normalized penetration MA 2 normal hole (%) .953 .048 .065 .055 .061 .021

Normalized penetration MA 8 normal hole (%) .927 .013 .033 -.006 -.016 .030

Penetration rate normal hole (m/min) .887 -.001 -.011 .100 -.016 -.005

Hammer pressure normal hole (bar) .771 -.076 -.213 -.047 -.209 .044

Feeder pressure normal hole (bar) .698 -.154 -.230 -.075 -.076 .037

Rotation speed normal hole (rpm) -.249 -.077 -.123 .208 .005 .026

Normalized rotation pressure MA 4 normal hole (%) -.014 .966 -.014 -.002 -.033 -.010

Normalized rotation pressure MA 2 normal hole (%) .052 .956 -.020 -.010 .016 -.014

Normalized rotation pressure MA 6 normal hole (%) -.053 .953 -.008 .008 -.059 -.003

Normalized rotation pressure MA 8 normal hole (%) -.080 .934 -.009 .023 -.076 .011

Rotation pressure normal hole (bar) .078 .865 -.044 -.020 .029 -.004

Normalized water MA 6 normal hole (%) -.031 .006 .952 .002 -.093 .006

Normalized water MA 4 normal hole (%) -.023 .001 .945 .010 -.126 -.026

Normalized water MA 8 normal hole (%) -.031 .015 .940 -.009 -.055 .023

Normalized water MA 2 normal hole (%) -.011 -.008 .880 -.006 -.142 -.084

Water flow normal hole (l/min) .057 -.102 .753 -.030 .205 .092

RMS normalized penetration MA 6 normal hole (%) -.018 -.003 -.029 .944 -.082 -.032

RMS normalized penetration MA 4 normal hole (%) .038 .027 -.024 .932 -.034 -.051

RMS normalized penetration MA 8 normal hole (%) -.069 -.022 -.034 .901 -.091 -.004

RMS normalized penetration MA 2 normal hole (%) .141 .063 .048 .763 .059 -.093

RMS normalized water MA 6 normal hole (%) .041 .041 .002 .094 -.937 .024

RMS normalized water MA 8 normal hole (%) .030 .081 -.071 .115 -.908 .046

RMS normalized water MA 4 normal hole (%) .047 .032 .106 .024 -.897 .011

RMS normalized water MA 2 normal hole (%) -.016 .005 .163 -.096 -.758 -.025

RMS normalized rotation pressure MA 4 normal hole (%) -.079 .086 .031 .177 .113 -.832

RMS normalized rotation pressure MA 6 normal hole (%) -.115 .015 -.005 .200 .102 -.830

RMS normalized rotation pressure MA 8 normal hole (%) -.161 -.039 -.005 .213 .065 -.782

RMS normalized rotation pressure MA 2 normal hole (%) -.027 .171 .011 .030 .114 -.725

Water pressure normal hole (bar) .057 -.039 -.018 -.086 -.098 -.278


The Kaiser-Meyer-Oklin measure of sampling adequacy is 0.806. This indicates that a PCA is an

appropriate data reduction method. The communalities are given in appendix 8, the lowest

communalities are 0.326 and 0.141 for the penetration rate and rotational speed. The other variables

have communalities of 0.569 and higher. This justifies the use of PCA.

The total variance explained by the 6 principle components is 77.7%, where the 1st component explains

25.5% and the 6th component explains 4.2% of the total variance (Table 9).

58


Component

1 2 3 4 5 6

Hammer pressure normal large (bar) -.881 .081 .098 .069 .027 .063

Feeder pressure large hole (bar) -.858 .096 -.164 -.044 .057 -.011

Normalized penetration MA 4 large hole (%) .780 -.032 -.207 -.023 .031 .245

Normalized penetration MA 6 large hole (%) .779 -.029 -.201 -.036 .059 .249

Normalized penetration MA 2 large hole (%) .758 -.036 -.203 -.016 -.013 .217

Normalized penetration MA 8 large hole (%) .744 .003 -.179 -.049 .115 .173

Penetration rate large hole (m/min) -.551 .040 -.066 .053 -.039 .269

Normalized water MA 2 large hole (%) .051 -.933 -.008 .040 -.008 -.001

Normalized water MA 4 large hole (%) .115 -.924 .036 .022 .002 .002

Normalized water MA 6 large hole (%) .154 -.877 .064 -.032 .011 .013

Water flow large hole (l/min) .125 -.830 .046 .023 -.069 .101

Water pressure large hole (bar) -.271 -.826 -.098 -.017 .056 -.104

Normalized water MA 8 large hole (%) .119 -.812 .078 -.068 -.018 .079

Normalized rotation pressure MA 2 large hole (%) .006 .006 -.940 .022 -.045 .016

Normalized rotation pressure MA 4 large hole (%) .084 .024 -.930 .038 -.003 -.034

Normalized rotation pressure MA 6 large hole (%) .113 .035 -.908 .044 .024 -.055

Normalized rotation pressure MA 8 large hole (%) .110 .034 -.869 .056 .060 -.048

Rotation pressure large hole (bar) -.056 -.027 -.841 -.026 -.043 .034

RMS normalized water MA 6 large hole (%) -.051 -.083 -.061 .953 .005 -.007

RMS normalized water MA 4 large hole (%) -.014 .017 -.027 .926 .012 -.016

RMS normalized water MA 8 large hole (%) -.094 -.148 -.065 .909 -.005 -.010

RMS normalized water MA 2 large hole (%) .108 .244 .072 .688 .042 .010

RMS normalized rotation pressure MA 4 large hole (%) .020 -.046 .043 .023 .905 .106

RMS normalized rotation pressure MA 6 large hole (%) .004 -.012 .040 .022 .884 .138

RMS normalized rotation pressure MA 2 large hole (%) .035 -.015 .054 .032 .834 -.051

RMS normalized rotation pressure MA 8 large hole (%) .037 .058 .038 .032 .832 .071

Rotation speed large hole (rpm) .060 -.011 .201 .054 -.282 .034

RMS normalized penetration MA 4 large hole (%) -.002 -.010 .015 -.004 .049 .927

RMS normalized penetration MA 6 large hole (%) -.003 -.006 .053 -.015 .096 .906

RMS normalized penetration MA 8 large hole (%) .022 .006 .076 -.007 .108 .836

RMS normalized penetration MA 2 large hole (%) .043 -.033 -.034 .002 .025 .749

Table 13 is the pattern matrix of the PCA of dataset 2. Unlike for dataset 1 there are several variables,

which are correlated well with one component and correlated medium with another component. The

following statements can be made about which variables are represented by which components.

- Component 1 represents normalized penetration, penetration rate, hammer pressure and

feeder pressure.

- Component 2 represents normalized water, water pressure and water flow.

- Component 3 represents normalized rotation pressure and rotation pressure.

59





5.2.7 Overview of PCA analyses

Table 14 gives a short overview of the outcome of the principle component analyses.

Table 14. Overview of PCA analyses.

Dataset Extraction Principle components

1 7 components retaining 86.8% of the variability

Principle components contain a mix of parameters and often one parameter is dominant in a component



- All principle components represent a group of processed parameters and their raw parameters - Rotational speed is not well correlated with any of the principle components


5.3 Cluster analysis

The goal of this analysis is to identify and understand possible clusters in the data. These clusters might

represent measurements in rock without discontinuities, with bedding discontinuities, discontinuities

with an infill and an aperture, discontinuities without infill and an aperture or a combination of

discontinuities. Unlike supervised methods like regression where accuracy can be measured, there is

no objective way of to measure if the clustering introduced by k-means analysis is any good.

5.3.1 K-means cluster analysis

Like described in the section 2.7.2, a k-means cluster analysis is capable of handling large amounts of

data. It is chosen to run the cluster analysis for the groups of standardized MWD data and the principle

components for k-values from 2 to 10. The outcome of Variance Ration Criterion (VRC) is given in Table

15. The lowest 𝜔𝑘 value indicates the ‘correct’ number of clusters. In addition it is important to choose

the number of clusters for which the iterations are low.

Table 15. The outcome of Variance Ration Criterion.

Number of clusters

𝝎𝒌 for MWD clusters dataset

1

𝝎𝒌 for principle component

clusters dataset 1


2


clusters dataset 2

3 534831 4842 50460.46 192.80

4 370152 5571 14868.23 396.22

5 209394 25 1484.25 3646.96

6 45497 2060 3225.05 3434.84

7 28777 330 12356.72 1034.55

8 13300 2031 255.24 1130.49

9 17025 761 19555.68 1166.60

10 18807 7395 19032.77 5464.08

60

Number of clusters


3


clusters dataset 3


4


clusters dataset 4

3 3495.44 793.43 6566.26 608.32

4 7894.45 1296.42 1775.94 1428.48

5 4996.48 293.90 1761.48 1974.87

6 556.15 43.30 2069.11 1752.62

7 260.80 21.38 1720.32 711.63

8 529.42 286.72 195.13 106.52

9 653.28 164.25 837.28 218.36

10 461.92 1659.93 706.11 1326.43

For the MWD clusters of dataset 1 the best number of clusters is 8 and for principle component the

best number of clusters is 5. Both the VRC and the iterations indicate this. For dataset 2, the VRC and

iterations do not point at the same number of clusters. The lowest 𝜔𝑘 value for MWD clusters is 255.24

for 8 clusters, but there are 19 iterations needed to create the 8 clusters. For the principle component

clusters, VRC indicates 3 and 4 clusters while iterations are 23 and 20. In both these cases, it is chosen

to pick a higher number of clusters, so cross tabulation in the next part will not hide information. For

the MWD and principle component clusters of dataset 2, 8 and 7 clusters are chosen. The VRC indicates

7 clusters for both MWD and principle components clusters. 17 and 19 iterations are required to come

to a stable division of clusters in the MWD and principle component dataset. For dataset 4, 15 iterations

were needed to come to 8 clusters for both MWD and principle component dataset.

5.3.2 Cross tabulation clusters and discontinuity groups

The way the clusters are evaluated is by looking at the number of samples or percentage of samples in

a cluster and compare this to the number of samples with a specific discontinuity. A discontinuity is

either present or not, which makes a cluster analysis like k-means with hard boundaries suited for the

cross tabulation with discontinuities. In order to do this 5 discontinuity groups are defined.

1. A group with samples of no discontinuities

2. A group with samples of lithological discontinuities

3. A group with samples of discontinuities with calcite infill and an aperture>0

4. A group with samples of discontinuities with soft infill and an aperture>0

5. A group with samples of discontinuities without infill and an aperture>0

As observed in the visual assessment of the MWD data, it is difficult to differentiate samples from group

1 and 2 and easier to differentiate between samples from group 4 or 5 and other groups. It could be

possible that groups 1 and 2 cluster together and groups 3, 4 and/or 5 cluster together. In order to be

able to observe these characteristics better, the clusters are cross tabulated with the 5 different

discontinuity groups.

5.3.2.1 Dataset 1

The MWD cross tabulation is given in Table 16. It can be seen that cluster 7 separates 38.3% of the

discontinuities with calcite infill together with 95 samples from other groups. Clusters 2, 4, 6 and 8

capture 79.6% of the samples with no discontinuity and 93.7% of samples with lithological

discontinuities. These two groups cannot be separated by the cluster analysis. Nine samples or 47.4%

of discontinuities without infill and an aperture are separated after 6 iterations in clusters 4 and 6.

Clusters 4 and 6 contain 25 other samples.

61

In Table 17 the cross tabulation of the principle component clusters is given. Similar observations as

with MWD clusters can be made. Clusters 2, 4 and 5 capture 86.9% of the samples with no discontinuity

and 95.2% of samples with lithological discontinuities. These clusters contain only a few samples of

other groups. Cluster 2 contains nearly only samples with no discontinuity. 39.8% of samples with

discontinuities with calcite infill are captured together with 109 other samples in cluster 3. 47.4% or 9

samples of discontinuities without infill and an aperture are separated by cluster 1 together with 20

samples belong to other groups.

Overall it seems that there is a correlation between clusters and discontinuity groups, even though

there are no clusters only containing samples from a single discontinuity group. The challenge with

dataset 1 is the difference in sample size between discontinuity groups.

Table 16. Cross tabulation of MWD clusters and discontinuity groups for dataset 1.

Table 17. Cross tabulation of principle component clusters and discontinuity groups for dataset 1.

Cluster 1 2 3 4 5 Total

Iterations 6 5 8 8 6 -

Number of cases 29 73 187 1099 203 1591

Percentage 1.8% 4.6% 11.8% 69.1% 12.8% 100.0%

Discontinuity 4 groups (no, bedding, infill & aperture>0 and no infill & aperture>0) * Cluster of 5-means analysis principle components

No disc. 14 72 78 423 114 701

2.0% 10.3% 11.1% 60.3% 16.3% 100.0%

Lithological disc. 6 0 26 569 72 673

.9% 0.0% 3.9% 84.5% 10.7% 100.0%

Disc with calcite infill and an aperture

0 1 78 102 15 196

0.0% .5% 39.8% 52.0% 7.7% 100.0%

Disc. Without infill and with an aperture

9 0 5 3 2 19

47.4% 0.0% 26.3% 15.8% 10.5% 100.0%

Cluster 1 2 3 4 5 6 7 8 Total

Iterations 3 4 6 14 6 14 12 2 -

Number of cases 7 106 27 538 92 582 166 73 1591

Percentage 0.4% 6.7% 1.7% 33.8% 5.8% 36.6% 10.4% 4.6% 100.0%

Discontinuity 4 groups (no, bedding, infill & aperture>0 and no infill & aperture>0) * Cluster of 8-means analysis MWD data

No disc. 0 51 19 191 58 244 66 72 701

0.0% 7.3% 2.7% 27.2% 8.3% 34.8% 9.4% 10.3% 100.0%

Lithological disc. 3 55 3 291 17 285 19 0 673

.4% 8.2% .4% 43.2% 2.5% 42.3% 2.8% 0.0% 100.0%


0 0 0 54 15 50 76 1 196

0.0% 0.0% 0.0% 27.6% 7.7% 25.5% 38.8% .5% 100.0%


4 0 5 0 2 3 5 0 19

21.1% 0.0% 26.3% 0.0% 10.5% 15.8% 26.3% 0.0% 100.0%

62

5.3.2.2 Dataset 2

The MWD cross tabulation is given in Table 18. 50% of the samples without infill and with an aperture

are separated after 4 iterations in clusters 2 and 5. There are only 4 samples of the group with no

discontinuities in these two clusters. 96.5% of the samples with lithological discontinuities are clustered

together with 52.2% of the samples with no discontinuities. 11.6% of the discontinuities with calcite

infill are separated after 1 iteration in cluster 6. As in dataset 1, it looks as if half the discontinuities

without infill and an aperture can be separated and that the groups ‘no discontinuities’ and lithological

discontinuities cluster together.

In Table 19 the cross tabulation of the principle component clusters is given. After 3 iterations, 40% of

the samples with discontinuities without infill and an aperture are separated together with only one

other sample. 10.7% of the samples with calcite infilled discontinuities are separated in cluster 1. It

takes up to 10 iterations to create the clusters 2, 3, 6 and 7, which represent a mix of samples with no

discontinuities and lithological discontinuities. The cross tabulation of dataset 2 is similar to the cross

tabulation of dataset 1. A fair part of open fractures can be separated and lithological discontinuities

and intact rock cluster together.

Table 18. Cross tabulation of MWD clusters and discontinuity groups for dataset 2.

Cluster 1 2 3 4 5 6 7 8 Total

Iterations 3 4 3 18 2 1 11 18 -

Number of cases 192 9 6 311 5 14 13 205 755

Percentage 25.4% 1.2% .8% 41.2% .7% 1.9% 1.7% 27.2% 100.0%


No disc. 129 4 2 213 0 0 7 117 472

27.3% .8% .4% 45.1% 0.0% 0.0% 1.5% 24.8% 100.0%

Lithological disc. 53 0 3 2 0 0 0 84 142

37.3% 0.0% 2.1% 1.4% 0.0% 0.0% 0.0% 59.2% 100.0%


5 0 0 96 0 14 6 0 121

4.1% 0.0% 0.0% 79.3% 0.0% 11.6% 5.0% 0.0% 100.0%


5 5 1 0 5 0 0 4 20

25.0% 25.0% 5.0% 0.0% 25.0% 0.0% 0.0% 20.0% 100.0%

5.3.2.2 Dataset 3 and 4

The cross tabulation of dataset 3 and 4 is in contrast with the cross tabulation of dataset 1 and 2.

Samples are spread out over all clusters. There seems to be no cluster that represents a fair amount of

a discontinuity group including a small number of samples from other groups. Apart from a few

exceptions, iterations are high. This indicates that the k-means cluster analyses had to shift the centre

points of the clusters often until a stable division of clusters was reached. A possible reason could be

that responses to fractures are not significant enough to be separated by k-means cluster. The cross

tabulation of dataset 3 and 4 can be found in appendix 9.

63

Table 19. Cross tabulation of PC clusters and discontinuity groups for dataset 2.

Cluster 1 2 3 4 5 6 7 Total

Iterations 3 18 15 16 3 19 19 -

Number of cases 14 165 196 18 9 207 146 755

Percentage 1.9% 21.9% 26.0% 2.4% 1.2% 27.4% 19.3% 100.0%


No disc. 0 93 131 9 0 119 120 472

0.0% 19.7% 27.8% 1.9% 0.0% 25.2% 25.4% 100.0%

Lithological disc. 0 2 55 1 0 84 0 142

0.0% 1.4% 38.7% .7% 0.0% 59.2% 0.0% 100.0%


13 70 5 6 1 0 26 121

10.7% 57.9% 4.1% 5.0% .8% 0.0% 21.5% 100.0%


1 0 5 2 8 4 0 20

5.0% 0.0% 25.0% 10.0% 40.0% 20.0% 0.0% 100.0%

5.3.3 Overview of cluster analysis outcome

Table 20 gives a short overview of the cluster analysis outcomes.

Table 20. Overview if cluster analysis outcome.

Dataset Cluster content

1 - Lithological discontinuities cluster together - A small number of data points with discontinuities with calcite infill cluster together - A substantial number of data points with open fractures cluster together

2

3 and 4 - Samples are spread over all clusters

5.4 Logistic regression

Here the results of the logistic regressions are discussed. For each dataset, separate regressions are

performed for the 4 discontinuity location vectors discussed in section 5.1. The cluster analyses of

dataset 1 and 2 confirmed the order of predictability on which the discontinuity location vectors are

based By studying the Pearson’s correlations between the two types of discontinuity vectors, containing

only the average position or upper and lower boundary, it seemed that the vectors containing only the

average are correlated much less than the vectors containing upper and lower boundaries. Therefor

the discontinuity vector containing the average location is disregarded.

5.4.1 Training dataset 1

In appendix 10 the Pearson’s correlations between the principle components and the discontinuity

location vectors is given. These correlations are not high, even though SPSS indicates a number as

significant. The relevant SPSS results are presented in appendix 11.

5.4.1.1 All discontinuities

This vector contains 888 samples with a discontinuity present and 701 with no discontinuity present.

This gives a predictive capacity 55.9% when considering no explanatory variables.

64

For all the 5 generated possibilities by SPSS that meet the condition of the Wald statistics, the Omnibus

Test of Model Coefficients shows that the generated possibilities are an improvement from the model

without explanatory variable. However the Hosmer and Lemeshow’s test indicates that for none of the

generated models the data fits with the logistic regression models. The p-values are 0. The fact that the

sample number is high and the ratio ones and zeros in the discontinuity location vector is good for the

Hosmer and Lemeshow’s test shows that the data including all discontinuities undoubtedly does not fit

the logistic regression model.

5.4.1.2 Discontinuities with an aperture>0

This vector contains 215 samples with a discontinuity present and 1374 with no discontinuity present.

This gives a predictive capacity 86.5% or 1383/1598. As discussed in section 2.7.5, this high ratio might

affect the outcome of the Hosmer and Lemeshow’s test.

In total SPSS generated 5 possible combinations of explanatory variable for which the condition of the

Wald statistic is met. All show to be improving the model without explanatory variable according to the

Omnibus Test of Model Coefficients. For none of these combinations, the data set is found to fit the

logistic regression model, according to the Hosmer and Lemeshow’s test. The pseudo 𝑟2 value are

below 0.168. Regardless if the Hosmer and Lemeshow’s test is effected, the pseudo 𝑟2 value are to low

for an useful model. The best model predicts 28 of the 218 samples with a discontinuity present.

5.4.1.3 Discontinuities with an aperture>0 and no infill

There are 19 sampling points with an open non-infilled discontinuity and 1570 sampling points with no

discontinuity. This ratio might affect the outcome of the Hosmer and Lemeshow’s test.

SPSS generated 5 models that meet the Wald statistics, Omnibus Test of Model Coefficients and

Hosmer and Lemeshow’s test. From the 19 sampling points, two models predict 4 sampling points with

a discontinuity correct. The other models predict 2 or 3 sampling points correct. This model has a

pseudo 𝑟2 value of 0.539.

The results of the regression analysis of these two models is given in appendix 11. The model in step 5

is the best model. The regression equation of step 5 is given below.

𝑝 =exp (−7.870−2.552𝑃𝐶2−0.674𝑃𝐶3+0.692𝑃𝐶4+2.086𝑃𝐶5+0.987𝑃𝐶6)

exp (−7.870−2.552𝑃𝐶2−0.674𝑃𝐶3+0.692𝑃𝐶4+2.086𝑃𝐶5+0.987𝑃𝐶6)+1 Regression Eq. 1

The standardized beta-weight are respectively -0.0382, 0.0081, 0.0083, 0.2895 and 0.0121. Principle

component 2 represents some of the raw MWD data, component 3 represents normalized penetration

and principle component 4 represents RMS normalized penetration. Raw and processed rotation

pressure is represented by component 5 and 6.

The model is subjected to a sensitivity test. Generated beta-weights by leaving out a random 10% of

the data do not deviate significantly form the original beta-weights. The results are given in Table 21.

When comparing the predicted probabilities with the discontinuity location vector in more detail, it is

found that the given model predicts 2 out of the 3 discontinuities with a small shift in location. Both

fractures in borehole 5 are detected. The fracture at a depth of 3.5m is represented in the discontinuity

location vector with 8 samples. The regression equation predicts 4 samples of which 2 are shifted 4cm

out of the defined location. The fracture at a depth of 4.6m is defined with 3 samples. The regression

equation predicted 4 samples of which again 2 are shifted 4cm out of the defined location. This is

visualized in appendix 15. Although the pseudo 𝑟2 value for this regression equation is not high, a fair

part of the fractures in the training set is predicted.

65

Table 21. Outcome of sensitivity analysis of regression analysis with discontinuities with an aperture >0 and no infill.

Principle components

Original Generated for sensitivity analysis

Beta-weight Average beta-weight Standard deviation beta-weight

2 -2.552 -2.929 0.673518

3 -0.674 -0.816 0.211748

4 0.692 0.747 0.054972

5 2.086 2.591 0.726557

6 0.987 1.034 0.086609

Constant -7.870 -8.716 1.155729


In appendix 10 the correlation coefficients of the discontinuity location vectors and principle

components are given. Compared to dataset 1, there are higher and more significant coefficients. It can

be expected that regression equations predict locations of discontinuities better. The relevant SPSS

outcome can be found in appendix 12.


The discontinuity location vector contains 755 samples of which 283 with a discontinuity. This ratio is

the recommended ratio for the use of the Hosmer and Lemeshow test.

SPSS generates two models for which the Wald statistics are met. One of the models fits the data

according to the Hosmer and Lemeshow test. I has pseudo 𝑟2 value of 0.94 and predicts only 40 out of

the 283 samples with a discontinuity correct.


There are 141 out of 755 samples which represent discontinuities with an aperture. The ratio is close

to the recommended 1 to 4 ratio for the use of the Hosmer and Lemeshow test.

From the 4 models SPSS generates, 3 models fit the data according to the Hosmer and Lemeshow test.

One has a pseudo 𝑟2 value of 0.471. It predicts 45.4% of the samples with a discontinuity correct. The

regression equation corresponding to this model is given below.

𝑝 =𝑒𝑥𝑝 (−2.247+1.102𝑃𝐶3−1.526𝑃𝐶4−1.008𝑃𝐶5)

𝑒𝑥𝑝 (−2.247+1.102𝑃𝐶3−1.526𝑃𝐶4−1.008𝑃𝐶5)+1 Regression Eq. 2

The model is not sensitive to deleting data. The results of the sensitivity analysis are given in Table 22.

Table 22. Outcome of sensitivity analysis of regression analysis with discontinuities with an aperture >0.




3 1.102 1.108 0.03052

4 -1.526 -1.485 0.05400

5 -1.008 -1.006 0.04596

Constant -2.247 -2.249 0.04650

These principle components represent raw MWD data, some of the processed water parameters and

normalized penetration. The calcite veins at -1.1m and -0.7m in borehole 1, at 0.2m in borehole 2 and

the fracture at 3.5m in borehole 5 are predicted by the regression equation. The first and last encounter

of the upper and lower boundaries of the discontinuities are predicted maximum 4cm of the defined

location. Other discontinuities are not predicted or predicted with only a couple of samples.

66


Out of 755 samples, 20 are representing fracture locations. This ratio might affect the Hosmer and

Lemeshow test.

All 5 models which SPSS generates met the Wald statistics and Hosmer and Lemeshow test. The best

model has a pseudo 𝑟2 value of 0.631. It predicts just over half the samples correct. The regression

equation is given below.

𝑝 =

𝑒𝑥𝑝 (−8.252−0.941𝑃𝐶1+0.980𝑃𝐶2

−3.297𝑃𝐶3−1.207𝑃𝐶4+1.793𝑃𝐶5)𝑒𝑥𝑝 (−8.252−0.941𝑃𝐶1+0.980𝑃𝐶2

−3.297𝑃𝐶3−1.207𝑃𝐶4+1.793𝑃𝐶5)+1

Regression Eq. 3

The average and standard deviation of a sensitivity analysis is given in Table 23.





1 -0.941 -0.943 0.052515

2 0.980 1.024 0.017099

3 -3.297 -3.447 0.20961

4 -1.207 -1.150 0.062708

5 1.793 1.654 0.137164

Constant -8.252 -8.301 0.213749

When studying the predicted probabilities in more detail, it turns out that the fracture at 3.5m in

borehole 5 and the fracture at 3.3m in borehole 6 are predicted. For both the size of the predicted

fracture is 4cm smaller than the defined fracture. This is visualized in appendix 15.


The Pearson’s correlations between discontinuity location vector and principle components are given

in appendix 10. The correlations are low. The principle components representing normalized water

parameters, RMS normalized penetration and RMS normalized rotation pressure seem to be best

correlating with discontinuity vectors. The relevant SPSS outcome can be found in appendix 13.


There are 296 samples which are defined as discontinuities. With a total of 1302 samples, the ratio

between the two cases is good for the Hosmer and Lemeshow test.

All 4 generated models by SPSS met the Wald statistics and Hosmer and Lemeshow test. The pseudo

𝑟2 values are between 0.13 and 0.141. At the most, 14.9% of the samples with discontinuities is

predicted correct. All of the predictions are in boreholes 21, 22 and 24.


The regression models generated for the discontinuity groups with an aperture, soft infill and no infill,

are predicting less than 10% of the defined discontinuity locations.


The ratio between samples with and without discontinuities present is 49 to 1253. This ratio might

affect the Hosmer and Lemeshow test.

67

SPSS generates 4 models which meet the Wald statistics. The Hosmer and Lemeshow test indicates that

3 regressions fit the data. The pseudo 𝑟2 value are between 0.23 and 0.251. The regression equation is

given below. Normalized water parameters and normalized penetration are not included in the

regression model.

𝑝 =exp (−3.812−0.438𝑃𝐶2+0.569𝑃𝐶4+0.517𝑃𝐶5−0.555𝑃𝐶6)

exp (−3.812−0.438𝑃𝐶2+0.569𝑃𝐶4+0.517𝑃𝐶5−0.555𝑃𝐶6)+1 Regression Eq. 4

The model is subjected to a sensitivity analysis. By deleting a different 10% of the data 5 times, the

average and standard deviation is calculated. The results are displayed in Table 24, the model is not

sensitive.

This regression predicts only 6 out of the 49 samples which represent a discontinuity. It is only covering

the fracture at 0.65m in borehole 24.





2 -.438 -.396 0.034311

4 .569 .559 0.032667

5 .517 .528 0.059723

6 -.555 -.566 0.050331

Constant -3.812 -3.821 0.071536


Like with the correlation coefficients of dataset 1 and dataset 2, there are better correlations with the

large hole dataset 4 than with normal hole dataset 3. Processed penetration rate, represented by

principle components 1 and 6, are highest correlated with discontinuity vectors. In appendix 10 the

correlations can be found. The relevant SPSS outcome can be found in appendix 14.

5.4.4.1 Discontinuities and with an aperture>0

The ratio samples with or without discontinuities is close to the recommended ratio. The Hosmer and

Lemeshow test does not indicate that the discontinuity location vector including all discontinuities fits

the logistic regression model. When considering discontinuities with an aperture>0, there is one model

which meets the significance level of the Hosmer and Lemeshow test. The pseudo 𝑟2 value 0.104 and

only 7.8% of the samples with a discontinuity is predicted correctly

5.4.4.2 Discontinuities with an aperture>0 and soft or no infill

There are 118 samples representing a discontinuity. With the in total 1531 samples, the ratio between

samples with and without discontinuities might affect the Hosmer and Lemeshow test.

SPSS generated 4 models which meet the Wald statistic. Three of the discontinuity vectors seem to fit

the logistic regression. The highest pseudo 𝑟2 value is 0.425. This model predicts 33 of the 118 samples

with a discontinuity correct. The regression equation is given below.

𝑝 =exp (−3.513+1.036𝑃𝐶1+0.312𝑃𝐶3+0.441𝑃𝐶5+0.748𝑃𝐶6)

exp (−3.513+1.036𝑃𝐶1+0.312𝑃𝐶3+0.441𝑃𝐶5+0.748𝑃𝐶6)+1 Regression Eq. 5

In Table 25the results of a sensitivity analysis are given. There are no large differences with the original

model.

68

Table 25. Outcome of sensitivity analysis of regression analysis with discontinuities with an aperture >0 and soft or no infill.




1 1.036 1.068 0.009751

3 .312 0.320 0.043779

5 .441 0.443 0.043597

6 .748 0.753 0.010833

Constant -3.513 -3.564 0.028872

None of the fracture in boreholes 11 to 20 are predicted. Form the in total 9 fractures with soft infill in

boreholes 21, 22 and 24, 6 are predicted with small deviations in size and location. For the fractures

with no infill 2 out 4 for are predicted. This is visualized in appendix 15. Even though the pseudo 𝑟2

value is not high, a fair part of the discontinuities with a soft infill are predicted.


The ratio samples with and without a fracture is 70 to 1498. This ratio deviates significantly form the

recommended ratio and it might influence the outcome the Hosmer and Lemeshow test.

Out of the 3 models SPSS generates, 1 model meets both Wald statistics and the Hosmer and Lemeshow

test. The pseudo 𝑟2 value is 0.281 and only 9 out of 70 samples is predicted correctly. When studied

with more detail, it turns out that only the fracture at 1m in borehole 22 is predicted. The regression

equation is given below.

𝑝 =exp (−3.810+0.768𝑃𝐶1+0.622𝑃𝐶5+0.316𝑃𝐶6)

exp (−3.810+0.768𝑃𝐶1+0.622𝑃𝐶5+0.316𝑃𝐶6)+1 Regression Eq. 6

The model is not sensitive to deleting data. The results are given in Table 26.





1 .768 0.814 0.037938

5 .622 0.624 0.044856

6 .316 0.316 0.047984

Constant -3.810 -3.862 0.064417

5.4.5 Summary of logistic regression analyses

Training of the different datasets leads to multiple models which meet the Wald statistics and Hosmer

and Lemeshow test. The quality of the model is studied by looking at predictions of input fractures. For

each attempt to predict a certain type of discontinuity that meets the Wald statistics and Hosmer and

Lemeshow test, the best predicting equation is given in the previous sections. These models are

summarized in Table 27.

69

Table 27. Overview of the best predicting models which meet the Wald statistics and Hosmer and Lemeshow test.

Equation Discontinuity type

Equation Data Prediction

1 Open fracture

All principle component except PC 1, which represents normalized rotation pressure

1570 sampling points of which 19 represent an open fracture

2 out of 3 fractures are predicted with a small shift in locations

2 Discontinuities with an aperture>0

3 principle components representing processed water parameter, raw parameters and normalized penetration

755 sampling points of which 141 have a discontinuity with an aperture

4 out of calcite veins are predicted with a maximum shift in location of 4cm

3 Open fractures All principle components.

755 sampling points of which 20 have an open fracture

The two largest open fractures are predicted

4 Open fractures 4 principle components representing Normalized rotation pressure and RMS processed data

1253 sampling points of which 49 represent an open fracture

Only 6 of the 49 sampling points are predicted correctly

5 Fractures with a soft or no infill

4 principle components which represent all processed penetration and rotation pressure

1531 samples of which 118 samples represent an open or filled fracture

6 of the 9 fractures with soft infill are predicted with a small deviation in size and location Open fractures were not predicted

6 Open fractures 3 principle components representing normalized penetration and RMS normalized penetration and rotation pressure

1498 sampling points of which 70 represent a fracture

Only 9 of the 70 sampling points are predicted correctly

5.5 Regression equation

Datasets 5 to 8 are reserved to test the regression equations found in the previous subchapter. In this

subchapter, principle components are calculated for each dataset by use of the corresponding

component score coefficient matrix in appendix 8. Next, the regression outcome of dataset 1 is tested

with corresponding dataset 5. The same counts for dataset 2 and 6, etc. The test datasets do not contain

data used for principle component analysis, cluster analysis or regression analysis.

5.5.1 Testing regression outcome from dataset 1

As mentioned in section 5.1, dataset 1 does not include boreholes 9 and 10. For boreholes 9 and 10 is

was not possible to find an appropriate shift. Regression equation 1 is supposed to predict the present

of open fractures. When testing dataset 5, the expected outcome is to find no open fracture.

The predicted locations of an open fracture are at 4.74-4.82m in borehole 9, at 0.76m and 4.86m in

borehole 10. Except for a peak in penetration rate at 0.76m in borehole 10, there are no causes found

for these expected locations. There are no open fractures present in dataset 5.

70


Dataset 6 consists of boreholes 3, 4 and 7 to 10. Aligning the televiewer and MWD depth gave an

uncertainty which is the reason they are not included in dataset 2. Regression equation 2 is generated

with discontinuities with an aperture>0 as input. Regression equation 3 should predict open fractures.

In borehole 4 a calcite vein at 3.68m deep is predicted correctly. Next to this prediction none of the 5

discontinuities with an aperture is predicted. Drops in penetration rate, hammer-, feeder- and rotation

pressure between led 10 times to a prediction of a discontinuity while these responses are not due to

geology. The remaining 3 predictions of a discontinuity cannot be related to certain responses in MWD

data.

The 6 predictions made by regression equation 3 are all related to the same drops in raw MWD data as

discussed above. In dataset 6 there are no open fractures present.


Dataset 7 consists of boreholes 16, 17, 23 and 25. There is only an unprocessed televiewer image

available of borehole 16. The depth registration of borehole 17 is not correct and cannot be shifted.

Borehole 23 showed an extremely low penetration rate and the MWD data of borehole 24 has been

merged out of two MWD files. Regression equation 4 is supposed to predict open fractures. There is at

least one open fracture present in borehole 23.

Regression equation 4 does not predict any open fractures in dataset 7.


Dataset 8 includes 6 discontinuities with soft infill and at least one open fracture. Regression equation

5 and 6 are supposed to predict discontinuities with soft infill and open fractures. Dataset 8 consists of

boreholes 16, 17, 23 and 25.

None of the named discontinuities is predicted. In total, equation 5 and 6 predict 4 discontinuities

where there is a drop in raw MWD data.

5.5.5 Overview of predicted discontinuities

In Table 28, an overview is given of the correct, incorrect and present discontinuities in the test datasets

5, 6, 7 and 8.

Table 28. Overview of predictions in the test datasets.

Testing dataset

Equation Discontinuities to be predicted

Correct predicted discontinuities

Incorrect predicted discontinuities

5 1 No open fractures - 7 sampling intervals

6 2 5 discontinuities with an aperture

1 calcite vein is predicted

3 No open fractures - 6 sampling intervals

7 4 1 open fracture - -

8 5 6 fracture with soft infill and 1 open fracture

- 4 discontinuities

6 1 open fracture - 4 discontinuities

71

6. Discussion

The goal of this study is to investigate the ability to detect discontinuities and their geometrical

properties in MWD data. Furthermore, this study aims to evaluate the usability of MWD data in terms

of detecting discontinuities for Norwegian Public Roads Administration as a client. To achieve this goal,

a literature review, fieldwork, visual comparisons and statistical comparisons of MWD data have been

carried out. This chapter summarizes the most important findings and potential implications.

6.1 Literature study

In past studies, the mismatch between scale in geological mapping and MWD data led to complications

when comparing geology with MWD data. In order to study the response of MWD data to individual

discontinuities, a much more detailed method than the regular geological mapping by engineering

geologists was needed.

Whereas normal users of MWD software interpret the drilling parameters without utilizing geological

data, this study aims to characterize responses to discontinuities based on detailed geological data. In

order to get a more in depth understanding and confirmation of visual characteristics, two

unsupervised statistical methods and one supervised statistical method have been found suitable for

MWD and geological data. Principle component analysis was presumed to reduce the dimension of the

MWD datasets. K-means cluster analysis was presumed to identify clustering in the data. Logistic

regression was presumed to be useful for training and testing of predictive equations. For this study,

the geological translation to statistical input had no continuous nature. The presence or absence of a

discontinuity gave a binary input and logistic regression was used to study the relation between

geological input and MWD data. It was found that a linear regression is not capable of tackling binary

independent variables and is restricted by additional assumptions.

6.2 Data gathering and collected data

Knowing where a certain fracture is located in a tunnel and finding the corresponding MWD data was

the major challenge. Mapping geology in a tunnel under construction is complicated a range of factors.

Artificial light, reachability and safety influenced the quality of the geological data collected.

Mapping discontinuities in a borehole remain is a good method if MWD data and geological records

can be aligned. Without borehole remains, one is restricted to approximate the position of larger

discontinuities. The aperture and nature of infill are important geometrical properties since these

influence the magnitude of response to discontinuities. Without doubt, records from televiewer

inspections are the most detailed and useful data collected in this study. This method captures rock

masses which are least influenced by blasting. Geological data gathered by mapping the tunnel contour

and filming boreholes with an inspection camera did not result in data detailed enough for statistical

analysis.

The majority of the collected data was appropriate for a visual comparison. The data collected with the

televiewer was also appropriate for a statistical analysis.

6.3 Visual validity assessment of MWD data

Comparing the mapped geology and 3D images of MWD data revealed that fractures with a certain infill

or aperture are visible in MWD data. Even though not all holes showed the presence of a fracture,

enough holes showed the location of a fracture so that their positions and orientation could be

extracted.

72

The geological record of a borehole remain allowed for a detailed comparison of discontinuities and

MWD data. It gave a clear response for clay filled fractures. Other fractures occasionally gave a

response.

Studying the correlation between MWD data and discontinuities in highly foliated phyllite did not

provide good results. Thus, it can be stated that a study like this should not be aimed on poor rock

masses.

It was found that for the Bjørnegårdtunnel, the parameter RMS normalized penetration gives the

clearest response to fractures with a moving average of 4 values.

The visual comparison of MWD data and the televiewer recordings led to clear responses to open

fractures and fractures with soft infill. The following responses were observed for open fractures or

fractures with soft infill with a dip angle larger than 62° and an aperture more than 1cm:

- The penetration rate peaks.

- The large hole hammer pressure drops and increases about 0.2 to 0.25m after the fracture is

encountered.

- The feeder pressure drops.

- The rotation pressure peaks.

- The normal hole water flow drops and then increases gradually.

- The processed penetration and rotation pressure peak.

Sometimes the peaks were followed by a short small drop before the parameter could reach its old

level. In MWD data of large hole drilling, the hammer pressure often dropped when encountering a

fracture. Fractures with dip angles smaller than 62 degrees and with an aperture smaller than 1cm were

not observed. The magnitude of the responses were found to be different for each fracture and no

pattern in magnitude between open and filled fractures was found. This is in contrast with the findings

listed in Schunnesson thesis (section 2.4). Sometimes the peaks were followed by a short small drop

before the parameter could reach its old level.

A point worth discussing is the gathering of geological data. Gathering more detailed geological data

has shown to contribute to the understanding of MWD data and fracturing. It can however be argued

that only a small amount of data was mapped at the tunnel face and contour. It should be emphasized

that the accuracy of geological data from contour mapping is highly dependent on the rock mass quality

and on drill and blast activities. Another aspect, which should be noted is that there are other rock

properties besides fracturing, which could influence the processed MWD data. To study the actual

response of discontinuities, it is preferred that MWD data is not influenced by other factors than

fracturing. Since the studied rock masses are influenced by other factors than fracturing, this has led to

the evaluation of geological conditions, which are experienced in real life excavations. This suits the aim

of evaluating the practical applications of MWD technology for the Norwegian Public Roads

Administration.

6.4 Statistical validity assessment of MWD data

From visual comparison, it is suspected that not more than the actual location and aperture can be

predicted. Orientation did not seem predictable based on a single hole. It was chosen to assign the

number 1 to each MWD sampling depth between the upper and lower boundary of a discontinuity. For

intact rock, a 0 is assigned to each MWD sampling depth. For regressions including only the more

predictable discontinuities like fractures, the fractures are assigned a 1. Other discontinuities are

assigned a 0.

73

The principle component analyses showed that the dataset could be reduced to a manageable number

of 5 to 7 components. Two of the datasets were nicely reduced to 6 components, where each

component contained one type of processed parameters and their raw parameters. All principle

component analyses showed high communalities, which indicates that high proportions of each

variable's variance were preserved after the principle component analysis. Close to 80% of the total

variance was explained by the principle components of each dataset. PCA was found to be an

appropriate analysis for MWD data gathered for this thesis.

K-means cluster analyses was found to be partly valuable 2 out of 4 datasets. It led to the understanding

that responses in MWD data due to lithological discontinuities, which have no aperture, cannot be

separated from intact rock. A fair part of fractures with no infill were separated by clusters, which

indicates that the MWD responses at the location of these fracture differ significantly from MWD

responses elsewhere.

Training the data with logistic regression analyses confirmed that responses for lithological boundaries

cannot be differentiated from the MWD data of intact rock. Logistic regression did however

differentiate between MWD data from open fractures and other MWD data in dataset 1 and 2. 2 out

of 3 fractures where predicted. These regression equations have pseudo 𝑟2 values of 0.539 and 0.631,

which is not particularly high. This is due to fact that the exact location and size of predicted fractures

differed slightly from the input locations. Furthermore, as stated in section 2.7.4, the sample number

can have great influence on the outcome of logistic regression. All datasets have a high enough number

of samples. However, the number of samples which are assigned a 1 due to presence of a discontinuity

with infill or open fractures is low. As mentioned in section 2.7.4, for the Hosmer and Lemeshow test a

ratio of 0.25/0.75 is recommended. The ratio of the predictive models described is often lower. For

dataset 3 and 4, the prediction of open fractures did not provide any conclusive results. The regression

with the fractures with soft infill was successful for dataset 4. 6 out of 9 fractures with clayey infill were

predicted with a small deviation in size and actual location. One regression led to the prediction of

45.4% of the samples with calcite infill.

Testing the regression equations on test datasets, which were not part of the input for data training,

did not lead to the correct prediction of fractures. A reason for this might be that the MWD data in the

test datasets was less appropriate as input for a regression analysis. Extremely late drilling on full speed,

uncommon drilling behaviour or data without a clear marker were a part of the test datasets. In

addition, the number of fractures and amount of data was not too much.

Another point of discussion is the translation of geological data to statistical input. With translating the

depths of discontinuities to discontinuity location vectors, it was assumed that the response to a

fracture is between the boundaries of the fractures. In reality, responses in MWD data are not bounded

by the actual location of discontinuities. As observed in this study, occasionally the water flow and

hammer pressure dropped when drilling through a fracture. These parameters did however reach their

old level long after the fracture had been drilled through. Furthermore, it was assumed that one

response takes place over the whole interval where a discontinuity is present. It was observed that

parameters occasionally showed a peak followed by a small drop within the boundaries of a fracture. It

might well be that this has influenced the outcome of regression. Another aspect, which might have

influenced the outcome of the statistical analyses is the fact that processed data involves, among other

operations, averaging of values ahead of the computed value. This leads to premature responses when

considering the actual location of a fracture

Ultimately, despite being limited by its apparent inability to predict each type of discontinuity, MWD

technology has great potential within the field. It can be questioned if the used statistical analyses are

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appropriate methods to study this data since the outcome of the statistical analyses are not

overwhelming. The discussed assumptions considering vectorisation of geological data and the

sampling interval might however be the main cause for the moderately convincing outcome of the

statistical analyses. If in the future both knowledge of data processing expands and data flow develops

with IT’s developments, it is believed that MWD data can definitely be useful in terms of fracture

recognition.

6.5 Current usability of MWD

Considering applications of MWD technology in terms of fractures, using MWD data for predicting rock

quality designation would lead to a severe underestimation of the RQD. The RQD value for the

Bjørnegårdtunnel is mainly dependent on lithological discontinuities, which could not be identified. This

leads to the conclusion that using MWD data for Q-system evaluation is not possible with today’s MWD

technology. Another aspect of tunnelling where the usability of MWD technology is being studied is

understanding and predicting grout volumes. Since grout seals fractures, the findings in this study could

contribute to research in grouting volumes. A final application, which showed to be useful for fracture

detection are 3D images of MWD data. Whilst a prediction of a single fracture cannot help to estimate

the orientation of a fracture or to understand the structural implications of excavation, a 3D image of

MWD data of a blast round with multiple detections can help. As shown in this thesis, 3D images

showing fractures with a certain aperture detected in multiple holes gave a good understanding of the

in situ fracture structure, even if responses to fractures were not present in all holes. This knowledge

can be used to anticipate possible under- and overbreak results due to these fractures before blasting.

As a precaution, one could achieve a better contour by reducing blast round length or adjusting the

blast design. In terms of rock support, MWD data can assist in the decision making concerning spot

bolting to secure wedges due to large fractures.

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7. Conclusion and recommendations

This chapter summarizes the most important findings and conclusions, provides answers to research

questions presented in this study and gives recommendations for similar research in the future.

Firstly, it can be concluded that the more detailed geological information collected for this thesis gives

much better insight into the detection of discontinuities and its potential than found in reviewed

studies. Geological data collected with the optical televiewer in particular provides useful data to gain

insight into responses to discontinuities. Based on the visual comparison, it can be stated that open

fractures or fractures with a soft infill give a consistent characteristic response. In 3D images of MWD

data, the orientation of these fractures can be obtained. In practice, this contributes in decision making

in spot bolting and anticipating possible under- and overbreak. The principle component analysis is an

useful analysis for MWD data. It showed the ability to reduce datasets significantly and at the same

time retain a high level of variability. However, the question still remains whether a k-means cluster

analysis is an appropriate method for analysing clustering in MWD data. Logistic regression occasionally

showed the possibility to detect the location and aperture of open fractures and fractures with a soft

infill, which were part of the input data. Finally, it can be concluded that there is no characteristic

difference in responses between lithological discontinuities and intact rock or between zones with low

and high discontinuity spacing.

The main research question is: To what extent can discontinuities be detected by MWD data and used

for reporting geology in a tunnel, predicting geology ahead of the tunnel face and predicting the amount

of rock support needed? Based on the whole thesis, this question can be answered as follows. With

today’s MWD technology, discontinuity detecting and its applications is limited to fractures with a

certain aperture and infill. Nonetheless, the potential of MWD technology remains. The detection of

open and infilled fractures gives multiple applications and could open doors for research in different

fields. If in the future both knowledge of data processing expands and data flow develops with IT’s

developments, it is believed that MWD data can definitely be useful in terms of fracture recognition.

Research questions

The first objective was to conduct a literature study on the subjects MWD technology, discontinuities in rock masses, statistical analysis that can be used to evaluate MWD data and previous research on the subject of MWD validity.

- What is MWD technology in drill and blast tunnelling? The utilisation of raw and processed drilling parameters to gain knowledge about the rock mass quality in terms of hardness, fracturing and water conditions.

- What kind of discontinuities can be encountered? Discontinuities that can be encountered are lithological discontinuities, foliation, fractures, faults and shear zones. For this project, blast induced fractures are also relevant.

- How is geology mapped in tunnels by the Norwegian Public Roads Administration? The engineering geologist documents the input for the Q-system, classifies rock type and draws large fractures in an illustration of an unfolded tunnel contour. This is done for every drill and blast cycle.

- How is MWD data filtered and processed? The processing of MWD data involves a series of steps which filter out influences like drill bit wear, percussive pressure and depth dependency. The result is a modified penetration rate, torque and water pressure where fluctuation intensity is suppressed and intensified by a moving average. This outcome is currently used to interpret rock mass in terms of hardness, fracturing and water conditions.

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- What statistical analyses are appropriate for analysing MWD data to enhance and understand the predictability of discontinuities?

Three statistical analyses are found to be useful in order to get a better understanding of MWD data and predict locations of discontinuities namely, Principle Component Analysis, k-means cluster analysis and multiple logistic regression.

- What is the result of previous research on the validity and usability of MWD? In previous research, it is found that the hardness parameter correlates well with geological boundaries and there is a visible relationship between MWD water conditions and fracturing with respect to the grouting volumes. RQD is found to correlate moderately with MWD fracturing number. Schmidt hammer hardness has not been shown to correlate with the MWD hardness number. The second objective was to gather data, which is appropriate to study relations between MWD data and discontinuities.

- What is the local geology of the tunnel sites? At the Bjørnegårdtunnel site, sedimentary rocks are folded and tilted towards extensional faults of the partial rifted Oslo field. The rock types excavated at the Solbakktunnel are Precambrian gneiss and highly foliated phyllite.

- What methods can be used to map discontinuities in tunnels? Methods to map discontinuities are mapping the tunnel face and contour, mapping borehole remains and inspecting boreholes with an inspection camera or optical televiewer.

- What are the parameters to obtain during fieldwork? The most important and difficult information to obtain is the location of a discontinuity with respect to MWD data. In addition, the aperture and nature of infill are important. Finally, the parameters to obtain are overall RQD, Schmidt hammer strength of discontinuity planes and intact rock, joint set, orientation, persistence, weathering state of discontinuity plane, joint roughness and joint alteration number.

- To what extent is it possible to work on sites where the degree of fracturing is the only variable parameter?

There were other rock properties, besides fracturing, which could influence the processed MWD data. Finding locations where the degree of fracturing is the only variable is very difficult.

- What is the quality of the collected data? The data gathered with the optical televiewer is detailed enough for a visual comparison and statistical analyses. The data gathered by mapping geology at the tunnel face is only useful for a visual comparison of MWD data and larger discontinuities. Artificial light, reachability and safety reduced the level of detail gathered from mapping geology, which makes statistical analyses of data not possible. The data collected with the inspection camera is less useful due to intense foliation and absence of other discontinuities. The third objective was to evaluate the data by visual comparisons of geological data collected during fieldwork and MWD data.

- What kind of discontinuities are visible in MWD data or what kind of geometrical properties make discontinuities visible in MWD data?

Fractures with an aperture of a couple of centimetres are visible in MWD data. Infill which is much softer than the rock or no infill give a clearer response. Aperture and nature of infill are the most important properties.

- Is there a characteristic response of MWD data to discontinuities? Open fractures or fractures with a soft infill give multiple or all of the following responses in MWD data:


2. The large hole hammer pressure drops and increases about 0.2 to 0.25m after the fracture

is encountered.

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3. The feeder pressure drops.


5. The normal hole water flow drops and increases gradually.

6. The processed penetration and rotation pressure peak. The magnitude of the responses are found to be different for each fracture and no pattern in magnitude between open and filled fractures is found. This is in contrast with the findings listed in Schunnesson thesis (section 2.4). The fourth objective was to apply statistical methods to come to a more in depth understanding of the relation between MWD data and discontinuities and confirm findings in objective 3.

- What geological data can be used for statistical analyses? Geological data gathered with the optical televiewer can be used for a statistical analysis if the depth of geological recordings and MWD can be aligned.

- How should geological data be translated to statistical input? Considering a single hole, the information that could be predicted is location, aperture and possibly nature of infill. This information needs to be translated to the same depth scale as MWD data. It is chosen to assign a 1 to the sampling points where a discontinuity is present and 0 zero elsewhere.

- Are the statistical analyses appropriate for the MWD data collected? Principle component analysis is useful. The datasets are reduced to a manageable number of components. K-means cluster analyses was found to be an appropriate analysis for 2 out of 4 datasets. Regression analysis was found to be useful for detecting fractures with an aperture larger than 1 cm and soft or no infill.

- Can discontinuities and/or certain geometrical properties be predicted by use of statistical methods?

Regression analysis showed that open fractures and fractures with a soft infill can be partly predicted with a small deviation in depth and aperture with respect to the input data. The last objective will be to discuss the current and future usability of MWD data for the Norwegian Public Roads Administration.

- To what extent are discontinuities visible in MWD data? Fractures with a certain aperture and soft or no infill give a characteristic response. There is no characteristic difference in responses for lithological discontinuities and intact rock. There was no clear difference in response in MWD data between zones with low and high discontinuity spacing.

- In which way could the outcome of this study be used to improve the process of drill and blast tunnelling?

Although the focus of this study has not been to investigate how to improve drill and blast tunnelling, the outcome of this study could potentially be used in two ways. Multiple large fractures in one blast round might give under- and overbreak. By detecting these fractures before blasting, measure can be taken. Another field where the outcome might be of interest is the research in grouting volumes. Grouting volumes are partially dependent on fracturing in rock and the detection of fractures with an aperture could contribute to research in grouting volumes.

- What significance do detected discontinuities have for rock support determination? Since lithological discontinuities contribute to the RQD but are detected by MWD data, interpreted

MWD data cannot be used to predict RQD. The detecting of fractures could for example help in decision

making for securing wedges with spot bolting.

Recommendations

Users of MWD software and services should be cautious with the interpretation of MWD data and

fracturing, especially when using sampling intervals much greater than the common 2 cm. An outcome

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of MWD data defined as ‘fracturing’ or ‘fracturing index’ does not differ from a processed MWD

parameter as described in this thesis, even though the name of an outcome parameter might suggest

differently. Furthermore, MWD technology is believed to be useful as a support for mapping the

geology and not supposed to replace it.

An aspect that future researchers could invest in is the sampling interval of MWD data. Several attempts

to decrease the sampling interval to 1cm or 0.5cm did not lead to a smaller sampling interval of the

output data. Whether this information was lost in data flow or the systems on the drilling rig could not

get to a sampling interval smaller than 2cm is unknown. A smaller sampling size would allow researchers

to investigate discontinuities with smaller apertures, ultimately allowing a more precise study of MWD

responses.

As discussed in section 6.4, the number of samples representing a discontinuity was often low

compared to the total dataset. In order to improve the training of data and computing predictive

equations it is recommended to gain a high number of fractures in the data. Of course this is easier said

than done, but when planning to drill inspection holes this should be considered as a very important

point.

Another point discussed in section 6.4 is the reason for disappointing results from testing the regression

equations. For a future research it is recommended to work with a larger test dataset. This does not

have to be data gathered by inspection. On the other hand, this might be too expensive and time

consuming. Detailed mapped geology of the tunnel contour where boreholes are inspected would give

plenty of MWD data to test regression equations.

Considering the geological mapping in a tunnel to gain detailed information about discontinuities, 3

final recommendations are made. A borehole camera with a 90° view gives a much better view of

fractures than a camera with a forward aimed lens. If possible, it is recommended to map tunnel

contour a couple of sections behind the tunnel excavation. This way, there might be not too much

disturbance for researcher and contractor. Regarding the use of a televiewer, the depth measurements

can be increased by leaving as little space between the borehole and reel. This prevents wobbling of

the cable and incorrect depth measurement as described in section 4.2.12.

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