msc thesis: geoelectrical monitoring of rock permafrost in ... · abstract mountain permafrost is...

Master of Science in Applied Geophysics

Research Thesis

Geoelectrical Monitoring of RockPermafrost in the Laboratory

Dominique Tschofen

August 8, 2014

Geoelectrical Monitoring of RockPermafrost in the Laboratory

Master of Science Thesis

for the degree of Master of Science in Applied Geophysics at

Delft University of Technology

ETH Zurich

RWTH Aachen University

by

Dominique Tschofen

August 8, 2014

Department of Geoscience & Engineering · Delft University of TechnologyDepartment of Earth Sciences · ETH ZurichFaculty of Georesources and Material Engineering · RWTH Aachen University

Copyright c© 2014 by IDEA League Joint Master’s in Applied Geophysics:

ETH Zurich

All rights reserved.No part of the material protected by this copyright notice may be reproduced or utilizedin any form or by any means, electronic or mechanical, including photocopying or by anyinformation storage and retrieval system, without permission from this publisher.

Printed in Switzerland

IDEA LEAGUEJOINT MASTER’S IN APPLIED GEOPHYSICS

Delft University of Technology, The NetherlandsETH Zurich, SwitzerlandRWTH Aachen, Germany

Dated: August 8, 2014

Supervisor(s):Prof. Dr. Hansruedi Maurer, ETH Zurich

Dr. Oliver Kuras, BGS Keyworth

Committee Members:Prof. Dr. Hansruedi Maurer, ETH Zurich

Dr. Oliver Kuras, BGS Keyworth

Dr. Guy Drijkoningen, TU Delft

Abstract

Mountain permafrost is in a delicate thermal equilibrium only marginally below 0C, hencesmall environmental changes can have drastic effects. Such effects are thaw-induced settle-ments or slope instabilities, which are difficult to predict and endanger the population andinfrastructure. Therefore, modelling and monitoring the degradation of permafrost is a criticalresearch goal. The present study aims to establish a geophysical methodology for improvingthe understanding of permafrost related processes. The approach was based on laboratoryexperiments designed to physically model seasonal permafrost behaviour and fracture forma-tion during freeze and thaw cycles. Two rock types, representing frost-susceptible lowlandrocks and highly resistive mountain rocks, were investigated. The main objective was to mon-itor the rock samples with Capacitive Resistivity Imaging (CRI) and Electrical ResistivityTomography (ERT) to assess the suitability of geoelectrical methodology and to examine thegeophysical response to changing conditions within permafrost affected rocks. The resultssuggest some advanced capability of CRI over ERT on the permafrost rock samples, in par-ticular for the highly resistive rock. They also revealed short comings in the experimentalsetup and the CRI prototype. The nature of measurement errors in the CRI and ERT datasets throughout the experiment was characterised in detail. Direct comparison of the ERTand CRI apparent resistivities show high correlation, with an offset between the two geoelec-trical measurement techniques. Significant differences in resistivity were found between thetwo different rock types, where the highly resistive mountain rock shows approximately oneorder of magnitude higher resistivities than the frost-susceptible lowland rock. Additionally,significant resistivity changes of 850% and 600% for CRI and ERT were observed betweenthe thawed and frozen conditions of the rock samples. Moreover, both geoelectric methodsallowed the determination of the temperature-resistivity relationship with high resolution forsmall temperature changes below 0C. The present results emphasised the feasibility to mon-itor permafrost processes and properties in finite rock samples using CRI, since they showedthe possibility to obtain the resistivity distribution in high resolution.

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vi Abstract

August 8, 2014

Acknowledgements

First of all I want to thank all the people who have participated in this project. This thesiscould not have reached its present form without the kind assistance and support of numerouspersons and organisations.

I gratefully acknowledge the continuous guidance and support of my supervisors, Dr OliverKuras (BGS Keyworth), Sebastian Uhlemann (BGS Keyworth) and Prof Dr Hansruedi Mau-rer (ETH Zurich) during the course of this work. I’m very thankful for the time Oliver spentdiscussing and analysing the various results and problems that appeared during the courseof this thesis. I also wish to express my gratitude toward Hansruedi for his scientific adviceand helpful comments, which broadened my understanding and led to different views on theexperimental data. Without the careful review by my supervisors, the first draft would nothave come to its final form.

The development of the BGS prototype CRI instrument used for this study was funded by aNERC Technology Proof of Concept grant (NE/I000917/1, PI Kuras). For the guidance inthe work with the CRI prototype system and the help in additional laboratory measurementsfor the understanding of the general output of the CRI system I would like to thank PhilMeldrum (BGS Keyworth) and Ed Haslam (BGS Keyworth).

Thanks are also due to Paul Wilkinson (BGS Keyworth) for assisting with permafrost mod-elling in Res3DMod, and his scientific advice during the work.

I am also indebted to Prof Julian Murton (University of Sussex) for providing the laboratoryfacilities for this thesis work and the constant supervision of the data acquisition at theUniversity of Sussex.

Finally, my warmest thanks go to my family for their love and support throughout my studies.

Swiss Federal Institute of Technology Dominique TschofenAugust 8, 2014

August 8, 2014

Table of Contents

Abstract v

Acknowledgements vii

Nomenclature xv

Acronyms xv

1 Introduction 1

1-1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1-2 Thesis objectives and outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Background 5

2-1 Permafrost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2-2 Geoelectrical measurements on frozen ground . . . . . . . . . . . . . . . . . . . 8

2-3 Measurement techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2-3-1 ERT apparent resistivity . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2-3-2 CRI apparent resistivity . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3 Physical modelling experiments in the laboratory 15

3-1 Physical layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3-2 Property measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3-2-1 Rock temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3-2-2 Rock moisture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3-2-3 Rock heave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3-3 Geoelectrical monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3-3-1 Capacitive Resistivity Imaging (CRI) . . . . . . . . . . . . . . . . . . . . 19

3-3-2 Electrical Resistivity Tomography (ERT) . . . . . . . . . . . . . . . . . . 21

August 8, 2014

x Table of Contents

4 Experimental observations: conventional parameters 25

4-1 Temperature distribution and dynamics . . . . . . . . . . . . . . . . . . . . . . . 25

4-2 Moisture distribution and dynamics . . . . . . . . . . . . . . . . . . . . . . . . . 28

4-3 Heave as an indicator for fracture formation . . . . . . . . . . . . . . . . . . . . 29

4-3-1 LVDT measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4-3-2 Visual inspection of fractures . . . . . . . . . . . . . . . . . . . . . . . . 30

5 Experimental observations: geoelectrical monitoring 33

5-1 Characterisation of data error . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5-1-1 Ground coupling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5-1-2 Reciprocity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

5-1-3 Effect of equipment swap . . . . . . . . . . . . . . . . . . . . . . . . . . 39

5-2 Apparent resistivity as a function of rock type and sample condition . . . . . . . 40

5-3 Observations specific to CRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5-3-1 CRI transfer impedance . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5-3-2 CRI applied current measurements . . . . . . . . . . . . . . . . . . . . . 44

5-4 Galvanic versus capacitive apparent resistivity time series . . . . . . . . . . . . . 45

5-5 Variations of changes in geoelectrical data with temperature . . . . . . . . . . . 48

5-6 Resistivity-Temperature Relationship . . . . . . . . . . . . . . . . . . . . . . . . 51

6 Distortion of the ERT apparent resistivities due to current channelling 53

7 Conclusions and Outlook 59

Bibliography 63

A Seasonal cycles 67

B Additional plots 71

B-1 Ground coupling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

B-2 Reciprocity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

B-3 Effect of equipment swap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

B-4 Apparent resistivity as a function of rock type and sample condition . . . . . . . 75

B-5 Electrode polarisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

B-6 ERT current injection frequency . . . . . . . . . . . . . . . . . . . . . . . . . . 77

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List of Figures

1-1 Ice-covered detachment surface laid open by a rock fall on the Matterhorn Lionridge in 2003 (Harris et al., 2009). . . . . . . . . . . . . . . . . . . . . . . . . . 1

1-2 Physical modelling of the permafrost ice segregation process. . . . . . . . . . . . 3

2-1 Permafrost terminology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2-2 Unfrozen water content at subzero temperatures for different material and porosity. 7

2-4 Generic-equivalent circuit model. . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2-5 A conceptual model of a capacitive electrode. . . . . . . . . . . . . . . . . . . . 11

3-1 Schematic of the experimental layout. . . . . . . . . . . . . . . . . . . . . . . . 17

3-2 Vector diagram to calculate current in the load. . . . . . . . . . . . . . . . . . . 20

3-3 Photos of the experimental setup. . . . . . . . . . . . . . . . . . . . . . . . . . 22

3-4 Photos of the experimental setup. . . . . . . . . . . . . . . . . . . . . . . . . . 23

4-1 Temperature distribution during a typical freezing period. . . . . . . . . . . . . . 26

4-2 Temperature Distribution during a thawing period. . . . . . . . . . . . . . . . . 27

4-3 Variation of moisture with time and depth for B1 . . . . . . . . . . . . . . . . . 28

4-4 Evolution of heave development over 15 cycles. . . . . . . . . . . . . . . . . . . 29

4-5 Visual inspection of fractures after the 27th cycle. . . . . . . . . . . . . . . . . . 31

5-1 ERT contact resistance for B3 and B6. . . . . . . . . . . . . . . . . . . . . . . . 34

5-2 CRI contact impedance for B3 and B6. . . . . . . . . . . . . . . . . . . . . . . . 35

5-3 ERT reciprocity: Cross-plot forward versus reciprocal. . . . . . . . . . . . . . . . 37

5-4 CRI reciprocity: Cross-plot forward versus reciprocal. . . . . . . . . . . . . . . . 38

5-5 Effect of the CRI unit change . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

5-6 Distribution of the ERT apparent resistivities for B3 and B6 in the two differentthermal conditions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

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xii List of Figures

5-7 Distribution of the CRI apparent resistivities for B3 and B6 in the two differentthermal conditions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5-8 CRI complex transfer impedance for both rock types . . . . . . . . . . . . . . . 43

5-9 Measured values of the applied current for CRI, the observed potential and thecalculated apparent resistivity for one specific quadrupole configuration of B6. . . 44

5-10 B3-TU: Galvanic and capacitive apparent resistivities (ρa) estimated by onequadrupole configuration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

5-11 B6-WS: Galvanic and capacitive apparent resistivities (ρa) estimated by onequadrupole configuration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

5-12 Comparison of ERT apparent resistivity data to a reference dataset during thawingfor B1-TU. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

5-13 Temperature dependency of B1 through a thaw period. . . . . . . . . . . . . . . 49

5-14 Comparison of CRI apparent resistivity to a reference measurement for B3-TU. . 49


5-16 Comparison of CRI apparent resistivity to a reference measurement for B6-WS. . 50


5-18 Temperature-Resistivity relationship for B1. . . . . . . . . . . . . . . . . . . . . 52

6-1 Apparent resistivity versus time for B1. . . . . . . . . . . . . . . . . . . . . . . . 54

6-2 Simple numerical permafrost model. . . . . . . . . . . . . . . . . . . . . . . . . 55

6-3 Apparent resistivities of a simplie permafrost model. . . . . . . . . . . . . . . . . 56

6-4 Occurrence of negative apparent resistivity over time on B1. . . . . . . . . . . . 57

B-1 ERT contact resistance for B1 and B4 . . . . . . . . . . . . . . . . . . . . . . . 72

B-2 ERT: Cross-plot forward versus reciprocal . . . . . . . . . . . . . . . . . . . . . 73

B-3 ERT-B1-TU: Permafrost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

B-4 Comparison of GeoTom change. . . . . . . . . . . . . . . . . . . . . . . . . . . 74

B-5 Distribution of the ERT apparent resistivity for B1, B2 and B4 in the two differentthermal conditions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

B-6 X-plot initial versus new ERT measurement sequence. . . . . . . . . . . . . . . . 76

B-7 X-plots 25 Hz versus 1 Hz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

August 8, 2014

List of Tables

3-1 Typical values for the capacitances, transfer impedances and maximal currents forthe used CRI system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

5-1 Number of all ERT quadrupole configurations per block, for which more than 25%of the measurements over time show a reciprocal error greater than 5%. . . . . . 38

A-1 List of all seasonal cycles including duration and mean air temperature. . . . . . 68

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xiv List of Tables

August 8, 2014

Acronyms

DUT Delft University of Technology

ETH Swiss Federal Institute of Technology

RWTH Aachen University

BGS British Geological Survey

CRI Capacitive Resistivity Imaging

ERT Electrical Resistivity Tomography

LVDT linear variable differential transformers

PT100 platinum resistance thermometers

TDR time-domain reflectometry

GeoTom100 GEOTOMMK1E100

GeoTom200 GEOTOMMK2E200

TU Tuffeau chalk

WS Wetterstein limestone

B1 Block 1

B2 Block 2

B3 Block 3

B4 Block 4

B5 Block 5

B6 Block 6

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xvi Acronyms

DC direct current

AC alternating current

August 8, 2014

Chapter 1

Introduction

1-1 Motivation

Figure 1-1: Ice-covered detachment surface laidopen by a rock fall on the Mat-terhorn Lion ridge in 2003 (Harriset al., 2009).

The definition of permafrost is based on thethermal condition of the subsurface, where theground remains below or at 0C for more thantwo consecutive years. Hence the permafrostgeothermal regime is potentially sensitive to on-going climatic changes of the 20th and 21st cen-tury. In particular discontinuous permafrost- such as mountain permafrost - is in a pre-carious thermal equilibrium only slightly below0C, where small environmental changes canhave drastic effects (Williams and Smith., 1989).Such effects are for example thaw induced settle-ment or slope instabilities, such as rock falls orlandslides (Harris et al., 2009).

In the European Alps the major part of per-mafrost occurs on alpine slopes, where the sub-surface temperature is closely linked to the at-mospheric temperature. Hence, the permafrostdegradation is more directly controlled by cli-matic changes. Therefore, an increase in atmo-spheric temperature may trigger the slope in-stability due to thawing or warming of the icebounded discontinuities (Haeberli and Gruber,2008) and reducing of the shear strength of theice. Davies et al. (2001) showed that frozenjoints are destabilising with increasing temper-atures, with minimal stability between -1.5 and

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2 Introduction

0C. Those slope instabilities induced by permafrost degradation and warming of ice filledjoints, such as rock falls, landslides and debris flows including their run-out zones are haz-ardous and pose a risk to people and infrastructure.

The extreme temperatures in the summer of 2003 caused an increase in rock fall incidents inmountain permafrost and revealed ice covered detachment surfaces, which probably cementedtogether the discontinuities and lost strength during warming (Harris et al., 2009). Thisincrease in rockfall frequency is thought to be a response to seasonal thawing of permafrostand associated active layer thickening. In addition to seasonal effects, a long-term permafrostwarming could cause a rise in the lower permafrost boundary and a decrease of permafrostthickness. This permafrost warming is associated with an increasing risk of large deep-seatedlandslides like Brenva Glacier rock avalanche in January 1997 at the Mont Blanc (Harriset al., 2009).

In order to assess the risk posed by rock slopes affected by permafrost degradation, a betterunderstanding is required of the permafrost distribution, as well as the physical propertiesof the affected bedrock. Different geophysical methods are well suited to the application topermafrost as the physical properties of permafrost affected rocks vary significantly with thephase change of water to ice or vice versa. Different studies (Hauck, 2002; Krautblatter, 2008;Krautblatter et al., 2010) use Electrical Resistivity Tomography (ERT) to monitor permafrostas the resistivity of most rocks increases exponentially from the thawed to the frozen state andare distinguishable with geoelectrical instruments. A drawback associated with ERT is theneed for sufficient electrical coupling to inject current. Especially for imaging permafrost thiscan be a limiting factor, as the coupling resistance of ice and mountainous hard rock can beextremely high. Krautblatter (2008) reports problems with electrode contact at deeply frozenbedrock with temperatures below -5C. For long-term investigations the galvanic contact mayalso vary over time, as the environment of the electrodes changes throughout seasonal cycles.To overcome this problem, the use of capacitively coupled sensors has been proposed insteadof the conventional galvanic electrodes (Timofeev, 1974; Hauck and Kneisel, 2006).

The commercial OhmMapper is one of the more frequently applied systems that uses ca-pacitively coupled sensors for injecting and measuring the current or potential difference,respectively. The OhmMapper has been applied in field studies of permafrost in Canada(Calvert, 2002; Pascale et al., 2008) and mountain permafrost (Hauck and Kneisel, 2006),where it showed in general good agreement with conventional geoelectric data. However, thesensor geometry of the OhmMapper is fundamentally different to that of conventional ERT(line instead of point electrodes), thus making quantitative comparison difficult. OhmMap-per apparent resistivities on mountain permafrost were generally found to be lower than theapparent resistivities obtained by ERT. As Capacitive Resistivity Imaging (CRI) methodsuses capacitive instead of galvanic coupling, the acquisition of geoelectric data is faster andmostly easier than conventional ERT.

In addition to its distribution, the physical properties of the affected bedrock are of muchinterest. An example are frost weathering processes in the active layer, which weaken thestrength of rocks. Frost weathering is associated with the presence of water and can be dueto mechanical or chemical processes (French, 2007). Mechanical processes are in general drivenby the phase change of water to ice. Studies of the physical properties include laboratory workand mechanical models (Murton et al., 2000, 2001, 2006b; Krautblatter et al., 2012) and arebased on the ice-segregation hypothesis. This model suggest that expansion is due to water

August 8, 2014

1-2 Thesis objectives and outline 3

(a) Vertical cross section through the centre ofthe frozen chalk block. (b) Ice rich fractures in the brecciated layer.

Figure 1-2: Physical modelling of the permafrost ice segregation process. Fractures have oc-curred in the centre of the block, where fragments of chalk are separated by layersof segregated ice (Murton et al., 2001).

migration toward existing ice lenses, causing progressive expansion of pores and micro-cracks,rather than due to the 9% volume expansion of the phase transition of water to ice (French,2007). In the laboratory, finite size block samples are exposed to multiple freezing and thawcycles, simulating permafrost (bidirectional) and seasonal frost (unidirectional). Figure 1-2shows the physical modelling of ice segregation in a permafrost environment. Fracture and icelens development occur in the vicinity of the permafrost table parallel to the ground surfaceand can build up ice lenses with a thickness of more than 1 cm (Murton et al., 2001). Duringpermafrost warming, destabilisation and failure of those ice filled fractures may play a keyrole in producing rock mass that is prone to rock falls (Harris et al., 2009). Investigations ofthe destabilisation of permafrost from the perspective of rock and ice mechanics show that thefriction of ice filled fractures decreases by approximately 15% due to thawing (Krautblatteret al., 2012).

The present study aims to help establish a methodology for improving the understandingof these mechanical processes. The approach is based on laboratory experiments set up tophysically model permafrost formation in a permafrost and seasonal frost environment. Twodifferent rock types are under investigations. These different rock types are a siliceous Tuffeauchalk form the Saumur region of the Loire Valley with high porosity (30 - 40%) and a fine-grained dolomised limestone from the Zugspitze (porosity<5%); representing frost-susceptiblelowland rocks and highly resistive mountain rocks, respectively. The main body of the workaims to assess the suitability of geoelectrical monitoring applied to different rock types andto examine the geophysical response to changing conditions within permafrost affected rocks.

1-2 Thesis objectives and outline

The central research hypothesis is that CRI emulates conventional ERT methodology, henceCRI is able to provide complementary geoelectrical data. This hypothesis is led by thefollowing questions which form the primary objectives of this work:

• How do the CRI and ERT methodologies respond to changing sample conditions?

• How do the two measurement principles respond to different rock types?

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4 Introduction

• What limitations to the methodology are imposed by data errors?

For this purpose a laboratory experiment was set up, which included 6 rock samples (Tuffeauchalk (TU) and Wetterstein limestone (WS)) in two different thermal environments. Themeasurement techniques and acquisition geometry varied between the samples, due to finitesize of the rock samples and the available equipment. The aim for the setup was to determinethe influence of the freezing direction for the two thermal regimes (permafrost and seasonalfrost), and on different rock types in the same thermal regime. Two samples were furtherequipped with both geoelectrical systems, to prove the concept of direct comparability. Theobtained results are used to investigate the performance of the ERT system and the CRIprototype in different environments and on different rock types.

The structure of this thesis is as following:

• Section 2 gives a brief scientific background for the present study, with a basic in-troduction to permafrost and its properties. Subsequently, a review of the two keymeasurement techniques, ERT and CRI.

• A description of the experimental setup of the physical modelling experiment is givenin Section 3, where first the freezing system and the different rock samples are de-scribed, followed by the property measurements and the acquisition of the geoelectricalmeasurements.

• In Section 4, the conventional parameters, such as rock temperature, rock moisture androck heave, of the experimental observation are presented and analysed.

• In Section 5, the geoelectrical measurements are analysed in detail. It is mainly based onrock sample 3 and 6, to represent the two different rock types and thermal environments.First, the Data errors are characterised, followed by a statistical analysis of the variationof changes in geoelectrical data with temperature and the direct comparison of the CRIand ERT apparent resistivities. At last, a resistivity-temperature relationship obtainedby ERT is given.

• The occurrence of distorted ERT apparent resistivities due to current channelling isevaluated in Section 6. The analysis is based on a simplistic two half-space resistivitypermafrost model, done in Res3DMod.

• Finally Section 7, draws conclusions an presents an outlook to possibilities of futurework.

August 8, 2014

Chapter 2

Background

The aim of this chapter is to introduce the scientific background for the present study. First,a basic introduction to permafrost and its properties is given. Subsequently, the relationshipsof temperature and moisture content with resistivity, are explored, as these are importantparameters for geoelectric measurements on frozen ground. A brief review of the two keymeasurement techniques, ERT and CRI, is given in the last subsection.

2-1 Permafrost

Introduction of permafrost. Permafrost was first defined as permanently frozen groundby S. W. Muller (1943). Nowadays the term perennially frozen ground is preferred topermanently, as it is based on the thermal condition of lithospheric material and noton the phase condition of the water content. In this context perennially frozen meansthe ground remains below or at 0C for more than two consecutive years and appliesto all lithospheric material (unconsolidated rocks and soils as well as bedrock), regard-less of the water presence in any form (solid or liquid state) (Muller et al., 2008; French, 2007).

Permafrost underlies approximately 24% (36.2 million km2) of the global land mass (149.0million km2), where Russia with 11 million km2 has the highest contribution (French, 2007;Washburn, 1979). As the thermal condition of the surface results from the heat and moistureexchange of atmosphere and lithosphere, the following factors influence the appearance ofpermafrost:

1. The interaction of the air and ground temperature, whereby the mean annual groundtemperature remains below 0C.

2. Geographical regions of high latitude (polar permafrost), high altitude (alpine/mountainpermafrost) and continental or maritime influence, respectively. Continental climate ismore erratic than the maritime at the same latitude.

August 8, 2014

6 Background

3. The different soil and rock properties themselves, such as albedo, thermal conductivityand heat capacity. In general the differences are small, but if pores or fractures filledwith water or ice are considered the bulk thermal properties can differ substantially.The thermal conductivity increases by a factor of 4 at the phase transition water-ice(Williams and Smith., 1989).

4. Geothermal heat flux from below the permafrost determines its thickness and temper-ature.

Micro-climatic factors are an additional influence and act as a buffer between atmosphericand lithosphere exchanges (Luthin and Guymon, 1974). These are:

5. Snow cover, which acts as an insulation against ground warming (spring) and heat loss(winter).

6. Oceans, lakes and rivers are mostly above 0C and therefore warm permafrost.

7. Vegetation, which dampens the influence of solar radiation and air temperature (Woo,2012).

Figure 2-1 illustrates the terminology of permafrost (Woo, 2012). The two main terms used todescribe a permafrost system are the permafrost itself and the seasonally frozen and thawedlayer above the permafrost, known as the active layer. Attention has to be drawn to the factthat they are based on two different definitions. Permafrost is defined by thermal condition(0C intersections) and the active layer is associated with phase state of the pore water. Thisoverlap is called seasonally active permafrost (French, 2007).

Figure 2-1: Permafrost terminology. The left side is based on the thermal condition of theground, whereas the right side is based on the phase state of the water (Woo,2012).

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2-1 Permafrost 7

Figure 2-2: Unfrozen water content at sub-zero temperatures for differentmaterial and porosity (Williamsand Smith., 1989).

Water/ice content. Water content influencesmany permafrost related issues directly or indirectly.Hence it is an important parameter (French, 2007).It comprises gravitational water, capillary water andhygroscopic water. Gravitational water is free waterin macropores of the soil and can drain freely dueto force of gravity. Capillary water is stored in mi-cropores or between soil particles. It does not drainfreely as it is held against the force of gravity by co-hesion and adhesion. Hygroscopic water is absorbedon the soil particle surface (Woo, 2012). Absorptionforce increases with decreasing soil particle size andtherefore increasing soil particle surface. These ab-sorption properties define the unfrozen water con-tent at subzero temperatures (see Figure 2-2). Assoil is freezing the gravitational water freezes in-situand builds pore-ice.

The solid and liquid phase of water can coexist aslong as the Gibbs free energies are in equilibrium(freezing point). If the temperature drops furtherthe free energy of the liquid phase is raised andchanges its phase to reach the new equilibrium. Theabsorption property of a soil particle surface lowersthe free energy too, and supports liquid water con-tent below 0C (Williams and Smith., 1989). Thephase transition of water to ice emits latent heat sothat the temperature remains approximately con-stant until the transition process slows down andless latent heat is produced. This is the momentwhen the freezing is advancing.

Rock fracturing. Rock fracturing in the context of ice formation was believed to be causedby in-situ freezing of ice in the pores and the associated volume expansion of 9%. Walderand Hallet (1986) showed that rock fracturing is rather caused by ice segregation, becauseincreased rock heave occurs in the thawing cycle. This conflicts with the theory of rockfracturing by volume expansion through freezing. As explained above, the imbalance of thefree energy during the freezing process causes a temperature and pressure gradient, wherebythe liquid water with higher free energy migrates to the forming or existing ice lenses orlayers with lower free energy in order to maintain equilibrium. This migration of water tothe freezing zone is called cryosuction. A thin film of unfrozen water between soil ice and soilparticles, caused by absorption properties of the soil particle surfaces, forms the pathway forthe migrating water.

Two stages of rock fracturing emerge after Murton et al. (2006b). First, the rock experiencesa steady increase of rock heave associated with evolution of micro-cracks. As the rock isweakened with micro-cracks (intra crack pressure rises above the stress-corrosion limits ofthe rock), increased heave occurs during a thaw period due to segregated ice lenses or layers

August 8, 2014

8 Background

forming in macro-cracks. This increased heave happens during the thaw period as in thisthermal state the conditions for ice segregation are optimal. The temperature gradient isincreased and causes amplified cryosuction, and the raised temperature enhances the watersupply. Ice layer or lenses tend to grow parallel to the surface, perpendicular to the frost heave,water migration and heat flow. Hence, different freezing regimes tend to produce differentrock fracture patterns. In a permafrost environment, associated with bidirectional freezing,water and heat flows from the centre of the active layer in both directions (bidirectional) tothe surface and to the permafrost table. Fracturing is therefore more likely to occur at thepermafrost table or the base of the active layer. As seasonal frost is unidirectional, heat andwater flow is only upwards, and fracturing preferentially occurs close to the surface.

2-2 Geoelectrical measurements on frozen ground

Direct investigations such as borehole measurements can only provide local information aboutthe subsurface. Hence, they are likely to miss inconsistencies in physical properties of thesubsurface. Moreover, borehole investigations are intrusive and might disturb the physicalproperties in the vicinity of the boreholes, thereby altering the measurements. In contrast,geophysical measurements are capable of volumetric ground sampling in an indirect and non-invasive manner and provide an enhanced areal coverage with high spatial resolution. 2Dand 3D geoelectric datasets enable the construction of images of the subsurface, even if thesubsurface is highly complex (for example Electrical Resistivity Tomography (ERT)). Inaddition, time-lapse imaging (4D) datasets can be acquired repeatedly over time to monitorthe amount and rate of changes (Kuras, 2002).

(a) Temperature-Resistivity-Relationship fordifferent material. (Hoekstra et al., 1975).

(b) Water content-Resistivity-Relationship for asilt soil (Hoekstra et al., 1974).

August 8, 2014

2-2 Geoelectrical measurements on frozen ground 9

Direct current (DC) is conventionally used for investigations of resistivity in the subsurfaceand is frequently applied to permafrost studies, since electrical resistivity is a highly versatileparameter and important rock property (Krautblatter and Hauck, 2007; Krautblatter, 2008;Krautblatter et al., 2010; Hauck, 2002; Hauck et al., 2003). It depends directly on porosity,spatial distribution of the pores, water saturation and the bulk water resistivity (Krautblatterand Hauck, 2007).

Archie’s law. The apparent bulk resistivity of clay-free sedimentary rocks, above the freezingpoint, can be calculated with Archie’s empirical equation (Archie, 1942):

ρe = aΦ−mS−nρw, (2-1)

where ρe is the resistivity of the thawed rocks, a the tortuousity factor, Φ the porosity, m thecementation exponent of the rock, S the pore space occupied by liquid water, n the saturationexponent (usually close to 2) and ρw the resistivity of the pore water. Transient changes inresistivity are more likely due to variations in pore water resistivity, water content, or watermobility, and less likely due to variations in porosity and water chemistry (Krautblatter, 2008).

Permafrost or seasonal frost regimes show an exponential increase of resistivity when thetemperature drops below freezing point (Hoekstra et al., 1975; Hauck, 2002). Hoekstra et al.(1975) investigated the increasingly resistive behaviour of different ground types with decreas-ing temperature (Figure 2-3a). The predominant factor for those increases is the reduction inliquid water content, which depends on the temperature, moisture content, pore water salin-ity, pore shape, and pore water chemistry (King et al., 1988). Figure 2-3b shows the watercontent-resistivity relationship for a silt soil by Hoekstra et al. (1974). The sharp decreaseof resistivity with increasing water content levels off at higher water content values. Withadvancing freezing the mobility of the free ions conducting the electrical current decreases,hence resistivity increases.

Temperature above and below freeze point. Overall the resistivity in permafrost re-gions depends on the type of ground material, unfrozen and frozen water content, and thetemperature. McGinnis et al. (1974) and Hauck (2002) give a relationship for the resistivity(ρ) and temperature (T) above and below the freezing point.

T & 0C : ρ =ρ0

1 + α (T − T0), (2-2)

Equation 2-2 represents the relationship above freezing point where ρ0 is the resistivity ata reference temperature T0 and α is the temperature coefficient of resistivity (approximately0.025K−1, Keller and Frischknecht (1966)). The relationship below freezing point is expressedas

T . 0C : ρ = ρ0 expb(Tf−T), (2-3)

where Tf and b are additionally the temperature at freezing point and a constant factor (ma-terial specific) controlling the rate of decrease respectively. These relationships can then beused to perform temperature or moisture content imaging based on resistivity measurements(Krautblatter et al., 2010).

Once temperature is accounted for, tomographic imaging (ERT) is further able to image therock matrix and permits therefore insight into the structural fabric, which may be used to

August 8, 2014

10 Background

monitor fracture formations with time-laps measurements. A baseline model is chosen fromwhich the changes over time are then derived. Such differences to the baseline may reflectfracturing, temperature or moisture changes over time.

2-3 Measurement techniques

This section will briefly review the measurement techniques of ERT and CRI. ERT is welldescribed in the literature (see for example Reynolds, 1997), therefore the focus will lie onCRI.

Figure 2-4: Generic-equivalent electrical circuit model after Wait (1995). a) generic, b) ERT andc) CRI. Where Zearth is the ground impedance, Zc1, Zc2, Zp1 and Zp2 are contactimpedances for current injection (Zc1, Zc2) and potential measurement (Zp1, Zp2)(Kuras et al., 2006).

Both measurement techniques are based on four electrode measurements with one pair ofelectrodes for potential measurement and another for current injection. Therefore Figure 2-4-a) shows an abstract electrical circuit model for both systems, where Zearth is the groundimpedance, Zc1, Zc2, Zp1 and Zp2 are contact impedances for current injection (Zc1, Zc2) andpotential measurement (Zp1, Zp2). b) and c) represent the electrical circuit models for ERTand CRI with galvanic and capacitive ground coupling mechanism, respectively (Wait, 1995;Kuras et al., 2006).

Galvanic ground coupling uses the physical contact of the electrode surface with the soil toinject direct current into the ground. Capacitive ground coupling on the other hand usesinsulated electric sensors (conductors) to accumulate electrical charge Q+ in the vicinity ofthe surface, which induces a charge of opposite sign Q− in the ground. Hence, an electricfield is established between the sensor and the ground. External electromagnetic (EM) noisecan affect the data quality. Figure 2-5 shows the conceptual model of the capacitive groundcoupling (Kuras, 2002).

August 8, 2014

2-3 Measurement techniques 11

Figure 2-5: A conceptual model of a capacitive electrode (Kuras, 2002).

2-3-1 ERT apparent resistivity

ERT is a well-established technique of measuring resistivity in the subsurface, which usesswitched or low frequency current flow between 1 and 25 Hz. A significant advance was thedevelopment of Electrical Resistivity Tomography (ERT) where 2D and 3D images are in-verted from individual resistivity measurements. Sophisticated inversion algorithms improvesimple starting models iteratively to find a suitable fit between the model and the measureddata, in order to determine the true spatial resistivity distribution. Those individual resis-tivity measurements are known as apparent resistivity and depend on injected current I, theobserved voltage ∆V, and geometric factor Kdc, which depends on the electrode geometry.For a homogeneous half-space the apparent resistivity ρa and the geometric factor are givenas:

ρa =∆V

I×Kdc, (2-4)

Kdc =2π[

1r11

+ 1r22

− 1r12

− 1r21

] . (2-5)

rij is the distance between the current and the potential electrodes. The first subscript i isthe current electrode and the second subscript j describes the potential electrode.

2-3-2 CRI apparent resistivity

The fundamental concept of the CRI technique is based on the following after Kuras (2002):

1. Usage of moderate frequencies in audio range (10 to 100 kHz).

2. An alternating current is used to establish an electric current flow in the ground.

3. The capacitive coupling mechanism is primarily provided by the electrical field, andtherefore the inductive effects are negligible.

4. With a low induction number B (aimed at the CRI) the quasi-static approximation isvalid.

5. If the above conditions hold, the CRI resistivity is equivalent to the direct current (DC)method and conventional DC interpretation schemes are adequate.

August 8, 2014

12 Background

For the derivation of the apparent resistivity from CRI measurements, we start off by calcu-lating the transfer impedance Z, using the concept of the electrostatic quadrupole introducedby Grard (1990). The field distribution at a point P of a time varying charge Q(t) and itsmirror image Q(t)’ = -αQ(t) close to the interface can be derived by (Grard, 1990; Kuraset al., 2006):

V (P ) =Q(t)

4πε0

(1

r− α 1

r′

), (2-6)

where r and r’ are the distances between the position of Q(t)P and Q′(t)P , respectively.Factor α contains the dielectric properties of the two media (eg. air-ground). In case oftime-varying charges the dielectric permittivity (ε) is complex.

ε = ε0εr − i1

ρω, (2-7)

ε0 is the permittivity of free space (8.854∗10−12 Fm−1), εr the relative permittivity, ρ theresistivity of the ground and ω the measurement frequency. As a consequence α is complexas well.

α =ρωε0 (εr − 1)− iρωε0 (εr + 1)− i

. (2-8)

To quantify a potential difference, four poles C1, C2, P1 and P2 are required. C1 and C2 carrythe charge Q(t) and Q(t), respectively and are the current sources. P1 and P2 measure thepotentials V1 and V2 from which the potential difference ∆V can be derived (Grard, 1990):

∆V (t) = V1 − V2 =Q(t)

4πε0

(1

r11+

1

r22− 1

r12− 1

r21− α

(1

r′11+

1

r′22− 1

r′12− 1

r′21

)), (2-9)

where rij is the distance between a current electrode Ci and a potential electrode Pj ortheir current images (r’ij). To simplify this expression the geometric factor KES (2-10) andthe mutual capacitance (2-11) of the configuration in free space (Kuras et al., 2006) areintroduced. In reality an alternating current I(t) is used instead of instantaneous charges.Therefore expression 2-12 needs to be substituted in equation 2-9, The new expression for thepotential difference is 2-13.

KES =

1r′11

+ 1r′22− 1

r′12− 1

r′211r11

+ 1r22− 1

r12− 1

r21

(2-10)

C0 =4πε0

1r11

+ 1r22− 1

r12− 1

r21

(2-11)

Q(t) =1

iωI(t) + const. (2-12)

∆V (t) =I(t)

iωC0

(1−KESα

). (2-13)

In the end the derivation of the transfer impedance is given as an expression of the free-space value Z0, the geometric factor KES and the complex value α (value for the electricalpermittivity of the subsurface):

Z =∆V

I(t)=

1

iωC0

(1−KESα

)= Z0

(1−KESα

)(2-14)

August 8, 2014

2-3 Measurement techniques 13

With the introduction of time varying current I(t) as replacement of Q(t), Maxwell’s elec-trodynamics theory is ignored, where current I(t) generates a electromagnetic field whichinduces a secondary current. Grard and Tabbagh (1991) show that it is valid to replace theinstantaneous charges Q(t) with the alternating current I(t) if the representing wavelengthsof an electromagnetic field are much larger as the distinctive distances r and r’. The inductionnumber B describes this characteristic as the ratio between the sensor separation L and theelectromagnetic skin depth δ or the magnetic vacuum permeability µ0 (4π × 10−7 V s

Am), mea-surement frequency f or angular frequency ω, and the half-space conductivity σ or resistivityρ, respectively (McNeill, 1980).

B =L

δ= L

√ωµ0σ

2= L

√πfµ0ρ

, (2-15)

Equation 2-16 is a condition, based on experience, until when the quasi-static approximationis valid (Benderitter et al., 1994).

B2 1 (2-16)

In the present experiment, the sensor separation L varies approximately between 0.1 and 0.5m, the applied frequency f is 15 kHz and the resistivities for the rock samples range between100 and 100 kΩm. Hence, the largest B2 occurs with a sensor separation of 0.5 m and asmall rock resistivity of 100 Ωm and is approximately 3.7 ∗ 10−5 and far smaller than 1.For this reason the experimental setup fulfils the quasi static conditions for an electrostaticquadrupole.

Finally, the expression for the apparent resistivity is (Kuras et al., 2006):

ρa = − 1

2ωε0

((1−Reα)2

Imα+ Imα

), (2-17)

with

Reα =1

KES

(1 + ωC0Z sinϕ

)=

1

KES(1 + ωC0ImZ) (2-18)

Imα = − 1

KES× ωC0Z cosϕ = − 1

KES× ωC0ReZ. (2-19)

Grard (1990) shows that Re(α) ≈ 1 at low induction numbers and KES ≈ 1 if the quadrupoleis close to the surface. Both are valid under the quasi-static assumption. Therefore, theexpression for the apparent resistivity is reduced to (Kuras et al., 2006):

ρa ≈ −Imα

2ωε0≈ C0

2ε0ReZ. (2-20)

At last, the expression for C0 and Kdc equations 2-11 and 2-5, respectively, are applied andthe final expression for the apparent resistivity is given by:

ρa ≈Re∆V

I×Kdc, (2-21)

with the in-phase component of the obtained potential difference ∆V , the amplitude of thetransmitter current I and the DC geometrical factor Kdc.

August 8, 2014

14 Background

August 8, 2014

Chapter 3

Physical modelling experiments in thelaboratory

A physical modelling experiment for rock permafrost was set up in the laboratory to act asa test bed for the geophysical techniques explored in this study. This chapter explains theexperimental design. First, the freezing system and the different rock samples are described.Subsequently, the property measurements, such as rock temperature, rock moisture, rockheave and the acquisition of the geoelectrical measurements are presented. Finally, the CRIand ERT measurement instrumentation is described in more detail.

3-1 Physical layout

The experimental methodology for the physical modelling of rock permafrost was developedby Murton et al. (2000) in a pilot study to test rock weathering by ice segregation. Thetechnique simulates the annual thermal regime of permafrost in bedrock.

The freezing system. The physical modelling experiment took place in two steel tanksinside a coldroom at the permafrost laboratory at the University of Sussex, where controlledfreezing and thaw cycles could be implemented. The two tanks simulated different thermalregimes, namely permafrost and seasonal frost. In the permafrost tank a basal cooling platemaintained sub-zero temperatures at the bottom of the tank to ensure consistent permafrostin the lower half of the rock samples. One complete temperature cycle emulates the seasonalchanges over one year and includes one freeze (winter) and one thaw (summer) period. Bothtanks have a water-saturated gravel bed, on which all rock samples are placed. At thebeginning of the experiment, all samples are hydrated either under gravity (TU) or by fullimmersion (WS). Due to the basal cooling plate, the water supply for Block 1 (B1), B2and B3 is cut off as soon as the plates are turned on and the water is frozen, hence theblocks might dry out over time. The upper part of these samples is cycled between ∼ -10Cand room temperature, simulating an active layer with bidirectional freezing. In contrast to

August 8, 2014

16 Physical modelling experiments in the laboratory

tank 1 simulating permafrost, tank 2 simulates seasonally frozen ground, with a unidirectionaltemperature pulse from the surface, hence freezing and thawing fronts travel only downwards.

The rock samples were hydrated by capillary rise and wrapped with cling film to preventthe blocks from drying out. In order to ensure predominately vertical heat flux and to mini-mize lateral heat loss, all samples were further insulated with Styrofoam and self-expandinginsulation foam.

During the freezing period chilled air is circulated in the coldroom and the rock samplesfreeze from their surface downwards. The samples in the permafrost tank 1 additionallyfreeze from the permafrost table upwards (bidirectional freezing). During the thawing cyclethe chilled air is turned off and the doors to the cold room were opened, resulting in warmroom temperatures. Hence, the blocks thawed from the surface downwards.

In total, 28 full temperature cycles were carried out from 20 April 2012 until 30 June 2014,with a typical cycle duration of 24 days. One seasonal cycle can be divided into a freezingand thawing period, whereby the freezing period starts when the first air temperature probefalls below 0C. The thawing period starts as soon as the first air temperature is above 0C.The mean duration for the freeze and thaw period are 8±7 days and 15±20 days, respectively,with mean air temperatures of -9.75±1.1C and 10.4±1.9C. A summary of all cycles is givenin Table A-2, in Appendix A.

Experimental rock types. In total, 6 blocks of two different rock types were under investi-gation. The two rock types are a siliceous Tuffeau chalk (TU) from the Saumur region of theLoire Valley with an expected porosity of 30% to 40% and Wetterstein limestone (WS), a fine-grained dolomised limestone obtained as an erratic block from the Zugspitze in Germany withan expected porosity of less than 5% (Murton et al., 2006a; Krautblatter et al., 2010). TheTU represents, by virtue of its pore properties and fine-grained matrix, a frost susceptible soilor typical lowland bedrocks affected by palaeopermafrost (e.g. chalks in Southern Englandor Northern France). In contrast, the WS represents highland Alpine bedrock with highlyresistive properties. No blocks showed any signs of weathering and all lack visible joints.An exception is Block 6 (B6) (WS), which has a pre-existing fracture on one side. For thepurpose of the experiment it is therefore assumed that the blocks are internally homogeneousand any pre-existing defects (microcracks) were uniformly distributed.

Experimental objectives. Each rock sample is 0.45 m high and 0.3 m by 0.3 m wide. Thissize is necessary to provide an adequate volume for simulating permafrost with an active layer.Based on this finite size and the amount of monitoring equipment a bespoke experimentallayout had to be designed in order to obtain as much comparable data as possible. The aim forthe setup was to determine the influence of the freezing direction for the two thermal regimes(permafrost and seasonal frost), and on different rock types in the same thermal regime. Twosamples were further equipped with both geoelectrical systems, to prove the concept of directcomparability. The full setup is illustrated in Figure 3-1.

3-2 Property measurements

In order to evaluate geoelectrical techniques for monitoring permafrost and seasonal frostdynamics, the following parameters (rock temperature, rock moisture, rock heave) have to bemonitored carefully to allow later comparison with the measured electrical properties.

August 8, 2014

3-2 Property measurements 17

Figure 3-1: Schematic of the experimental layout showing the instrumentation for each rocksample.

August 8, 2014


3-2-1 Rock temperature

The spatial distribution of temperature is an important factor for monitoring permanentlyfrozen ground especially as the definition of permafrost is based purely on temperature. Forthis reason each rock sample was mounted with 10 platinum resistance thermometers (PT100).The PT100 were placed in 50 mm deep holes, starting at the top (0 mm) down to 450 mmat the bottom with depth increments of 50 mm (see Figure 3-4-right). In order to resolvethe temperature pulse travelling through the samples and the temperature distribution of theunidirectional and bidirectional freezing a high measurement density was acquired. Hence,the temperature was measured every 10 minutes. To smooth the data a running average over30 minutes was then applied.

3-2-2 Rock moisture

During the freezing and thawing cycles the pores within the rock matrix can be filled withwater, ice or air. Even at temperatures below 0C a significant amount of water can still beunfrozen (capillary or hygroscopic water). By monitoring the moisture content it is possibleto observe the phase change between liquid and solid water (see Section 2-1). Over time itmight be also possible to detect some depletion in moisture content, due to ice segregation.Hence, the water content of the rock samples was measured continuously during the physicalmodelling experiment with time-domain reflectometry (TDR). The TDR probes have a sensorlength of 150 mm and are mounted on the rock samples right next to the PT100 with thesame vertical spacing. The TDR measurement technique is based on measuring the dielectricconstant, which is about 4 in dry media or ice and 80 for liquid water. By determining thedielectric constant, the volumetric water content (moisture content) can be estimated by usingTopp’s equation (Topp et al., 1980). The frequency of the TDR measurements was once every10 minutes. As both TDR and CRI are electromagnetic methods using AC signals, the TDRsensors were only mounted on B1, Block 2 (B2) and B4, in order to minimise any interferencesbetween the two types of measurements.

3-2-3 Rock heave

Rock heave and settlement measurements can give an indication of fracturing experienced bythe rock samples as a result of periodic freezing and thawing. Hence, to measure rock heaveand settlement linear variable differential transformers (LVDT) were placed centrally on topof each rock sample. The output is proportional to the vertical displacement (heave) andalso indicates the direction, positive or negative from the reference point. All LVDTs werecalibrated with a micrometer before the experiment. The calibration accuracy was ± 0.08mm. This calibration was then used to transform the voltage output into rock heave in mm.The frequency of the rock heave logging was every 10 minutes.

August 8, 2014

3-3 Geoelectrical monitoring 19

3-3 Geoelectrical monitoring

3-3-1 Capacitive Resistivity Imaging (CRI)

The CRI measurement system. The CRI measurement system used in this study is aresearch prototype designed and built at the British Geological Survey (BGS) (Kuras et al.,2006; Uhlemann, 2012). It measures the complex transfer impedance by determining thereal and imaginary components of the measured potential difference induced by an injectedalternating (sinusoidal) current.

The basic features of the BGS CRI system are, after BGS (2012):

1. Generation of a sine wave with a programmable frequency (10 kHz to 100 kHz).

2. Feed sine wave into a power amplifier, which generates a complementary output voltage(160 V) and distribute it to two selected transmitter plates.

3. Measure potential signals on two selected receiver plates.

4. Two lock-in amplifiers resolve the amplitude and relative phase of the measured poten-tial difference into real (in phase) and imaginary (quadrature) components. The lock-inamplifier uses the reference input signal to find the potential signal, which is in-syncwith the reference and ignoring all signals not in-sync (Scofield, 1994).

For transmitting and receiving the signal the CRI unit provides 128 sensor connections, where64 can be used as transmitters for injecting current and the other 64 as receivers for measuringthe potential. One limitation of the CRI prototype is its incapability to use the same channelfor transmitting and receiving, therefore the cables have to be swapped manually to acquireany reciprocal measurements. The capacitive sensors used for this experiment were madeof thin copper foil with a sensor width of 50 mm. They were distributed over two oppositefaces of the sample, each with two vertical columns of 8 electrodes (see Figure 3-3-right or3-4-middle.). To prevent galvanic contact and facilitate assembly to the rock samples, theelectrodes were first attached to an insulating acetate sheet and then mounted on the sampleswith adhesive spray. The coaxial cables used to connect the electrodes with the CRI systemare actively shielded in order to prevent electromagnetic coupling.

The true injected current at the sensors (current at the load) is estimated through the currentin the transmitter, the return current in the cable and the phase shift between them. Thevector diagram in Figure 3-2 is used to estimate the load current and load phase.

CRI measurements of the rock samples were acquired three times per day with an increment ofapproximately 8 hours. For Block 3 (B3) and B6 344 cross-hole combinations were measured,for Block 5 (B5) with the 3D setup, 688 cross-hole measurements were acquired. No over-edgeor same-face measurements were made, as these are likely to be subjects of errors (Uhlemann,2012). An operational frequency of 15 kHz was chosen for this experiment. On day 199 intothe experiment, the initial CRI unit was replaced with an upgraded (but otherwise identical)system that included a number of enhancements.

Capacitance estimation. The plate capacitances, the contact impedances and the injectedcurrent are critical parameters for the CRI technique. The aim is a high capacitance (small

August 8, 2014


Figure 3-2: Vector diagram to calculate current in the load. (BGS, 2012).

contact impedance), so a sufficient injection current is achieved. In the following, the capaci-tance, contact impedance and maximal injected current is derived for for different separationsbetween the plates.

The capacitance of the plates is given by (Fogiel et al., 1976):

C = εrε0A

d, (3-1)

where C is the capacitance in Farads [F], A is the area of the plates in square meters, εris the relative permittivity of the material between the conductors, ε0 is the permittivity offree space (8.854 ∗ 10−12 Fm−1) and d is the separation between the plates in meters. Theimpedance Z of the capacitive sensors can then be calculated with the measurement frequencyf of 15 kHz:

Zcapacitor =1

jωC=

1

j2πfC(3-2)

As the CRI unit is a 160 V system and the contact impedances of the current sensors are inseries, the maximal expected current in the load is:

Imax =appliedV oltage

Ztotal(3-3)

The separation is composed of the acetate sheet and air or water between the rock surfaceand the acetate sheet. It is assumed, that the air/water gap varies approximately between0.1 and 5 mm. To derive the total capacitance the formula for two capacitances in series wasused.

1

Ctotal=

1

CacS+

1

Cair(3-4)

Ctotal =CacS ∗ Cair

CacS + Cair(3-5)

Table 3-1 summarises typical values for the above parameters with a constant value for theplate area of 0.0025 m2.

August 8, 2014


Table 3-1: Typical capacitances, transfer impedances and maximal applied currents for differ-ent capacitor separations, showing the influence of the air/water gap between theinsulation and the block.

dacS

[m]εr,acS

dair/water

[m]εr,air εr,water

Ctotal

[pF]Ztotal

[kΩ]Imax

[mA]

0.001 ∼5 0.0001 1 - 74 144 0.550.001 ∼5 0.0005 1 - 32 336 0.240.001 ∼5 0.001 1 - 18 575 0.140.001 ∼5 0.005 1 - 4 2493 0.030.001 ∼5 0.0001 - 80 110 96 0.830.001 ∼5 0.0005 - 80 107 99 0.810.001 ∼5 0.001 - 80 104 102 0.780.001 ∼5 0.005 - 80 85 126 0.64

The relatively small values for the maximal injection current with increasing sensor separationshow the importance of a sufficient mounting of the sensors to the rock surface. The aimshould be to minimise the separation of the two capacitors. In particular air and ice (εr ≈ 4)in the gap should be avoided.

3-3-2 Electrical Resistivity Tomography (ERT)

For comparison with the CRI data, conventional ERT electodes were mounted to the blocksin order to image the resistivity distribution of the rock samples. Machine screws (M4) wereused as electrodes, which were placed on the blocks right next to the CRI sensors. A smallhole was drilled and the electrodes screwed in using contact paste (graphite grease) for bettergalvanic coupling. The system used for this study was the GEOTOMMK1E100 (GeoTom100)as it provides injection currents as small as 0.1 µA, which are needed due to the densesensor network and the expected high contact resistances on frozen rocks. On day 207 intothe experiment, the GeoTom100 was replaced with the GEOTOMMK2E200 (GeoTom200)for convenience. The GeoTom100 has only 100 channels, so it was necessary to swap theelectrodes cable manually every day to measure all blocks. The GeoTom200 has 200 channelsand therefore all electrodes could be connected at once. The measurement frequency for B1,B2 and Block 4 (B4) is twice and for B3 and B6 once per day.

August 8, 2014


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RI

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August 8, 2014


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August 8, 2014


August 8, 2014

Chapter 4

Experimental observations:conventional parameters

Monitoring conventional parameters such as temperature distribution, moisture distributionand weathering processes are of key interest for gaining an enhanced understanding of per-mafrost dynamics. The present experiment simulated natural freeze-thaw cycles with a highdegree of control over these parameters in order to assess the capability of geoelectrical mon-itoring techniques to quantify these dynamics. This chapter provides an analysis of tem-perature measurements, moisture content and heave within the blocks through the seasonalcycles.

4-1 Temperature distribution and dynamics

Two different freezing mechanisms are simulated in this experiment, both associated withdifferent temperature patterns. First the unidirectional freezing associated with seasonalfrost, whereby the freezing direction is from the surface downwards. Secondly bidirectionalfreezing where we have a second freezing direction from the permafrost table upwards. Inaddition, the different rock types (TU, WS), have a different effect on the temperaturedistribution.

Temperature distribution of the freezing period. Figure 4-1 shows the characteris-tic temperature distributions throughout a freezing period for the different rock types andthermal environments modelled (permafrost and seasonal frost). The top row contains thetemperature distribution for WS and the bottom row that for TU. The two columns representthe different thermal regimes (left − permafrost, right − seasonal frost). It is clear that therock type (porosity/density being the dominant factors) has a strong influence on the tem-perature distribution. Hard rock quickly freezes and adapts a new thermal equilibrium, inboth environments after a few hours (approximately one day). The permafrost environmentresponds more rapidly during freezing as it has two freezing fronts. The temperature-depth

August 8, 2014

26 Experimental observations: conventional parameters

Figure 4-1: Temperature distribution during a freezing period (28th cycle) for two different rocktypes and thermal regimes. The purple-dashed lines indicate the time position ofthe temperature depth profiles (right side).

profile shows that the permafrost table lies roughly at -0.1 m as the first two temperatureprofiles (775, 776 (1)) in the thawed state are crossing the zero temperature line at -0.1 m.These two profiles also indicate how the permafrost gets temperate within the thawed state.This influences the moisture content and ice-water ratio, respectively. In the seasonal frostregime, it can be seen how the surface responds to the temperature input and then the freezingfront moves downwards until the whole block is in equilibrium.

The soft rock (TU), on the other hand requires much more time to revert to a thermalstabilised state, especially in the seasonal frost environment, where it takes up to 5 days. Thepermafrost colour map as well as the depth profiles show the bidirectional freezing (associatedwith this regime) very clearly. Freezing directions are from the surface downwards and fromthe permafrost table (-0.25 m) upwards. This causes the thermal profiles to bulge in directionof higher temperature centred on the active layer, as the freezing front approaches from twosides. The seasonal frost environment also shows unidirectional freezing from the surfacedownwards as seen in the WS. The temperature distribution and the temperature profilesboth show an extended time period, where almost the whole sample is a 0C. This phenomenonis due to the phase change of water to ice with the release of latent heat. The freezing frontemerging from the bottom of the sample can be explained by the freezing of the saturatedgravel-bed in the non-insulated tank. This causes unwanted heat loss at the base of thesample.

Temperature distribution of the thawing period. Figure 4-2 illustrates the temperaturedistribution during the thawing period. With the temperature input only from the surface, the

August 8, 2014

4-1 Temperature distribution and dynamics 27

Figure 4-2: Temperature distribution during a thawing period for two different rock types andthermal regimes. The purple-dashed lines indicate the time position of the temper-ature depth profiles (right side).

temperature-depth profiles shift almost simultaneously to higher temperature and thereforeto the summer thermal equilibrium. The TU in the seasonal frost regime shows once more anextended time period close to 0C. This time, this effect is due to the reversed phase transitionfrom ice to water. The phase transition from ice to water needs much more energy (339J∗g−1)compared to the energy needed for 1C increase of water (4.18J ∗ g−1) or ice (2.09J ∗ g−1)(Williams and Smith., 1989). Interesting are the differences between thermal behaviour forthe two rock types in permafrost. Permafrost has a bigger impact on the thermal regime ofthe active layer in WS as in TU, as it keeps it in a much cooler state, hence the permafrost isat -0.10 m. The TU shows a higher temperature variation and therefore a steeper temperaturegradient, with the permafrost table at -0.25 m.

These clear differences in the thermal response between the two rock types is a result of therock specific thermal conductivity and specific heat capacity, porosity and water/ice content.The difference in the specific thermal conductivity and specific heat capacity for the rockmatrix are small and negligible (Williams and Smith., 1989). The important difference isthe porosity, with associated water content, as water has a specific heat capacity of 4.210kJ ∗ kg−1 ∗K−1 to 4.187 kJ ∗ kg−1 ∗K−1 for 0.1C and 15C, respectively. This decreasesby 50% after the phase change to ice (2.108 kJ ∗ kg−1 ∗K−1). The specific heat capacity ofan average rock matrix is approximately 2.0 kJ ∗ kg−1 ∗K−1.

August 8, 2014


4-2 Moisture distribution and dynamics

Figure 4-3: Variation of moisture with time and depth for B1. a) Comparison of various depth,b), c), d) and e) measured temperature and moisture distribution for depth -0.10m, -0.20 m, -0.30, and -0.40 m respectively. The thick gray curve indicates thetemperature for the specific depth, measured with the PT100.

The quality of the moisture content data varies over time and with depth. B1 (TU inpermafrost) provided the best data and was chosen as an example to demonstrate theresponse of the moisture content to temperature changes during the freezing and thawingperiods. The selected time window is from the start of the experiment until day 130 (thawperiod of 3rd temperature cycle) into the experiment, because the quality of the data at thattime was still reasonably good. After day 100 the quality of the moisture data deterioratedand could not be used for analysis. Figure 4-3 shows the variation of moisture content atdifferent depth levels (-.10 m to -0.40 m). Plot a) is a comparison for all depths. At thebeginning of the experiment the deepest measurements (orange, -0.4 m) show the highestsaturation (almost 40%); this decreases with greater elevation. This is likely due to the watersupply from the bottom of the block through the gravel bed and hence an upward saturation.During the first freezing cycle, depths -0.40 m and -0.30 m retain approximately the samelevel of moisture content (10%). It only rises marginally higher during the thawing stage (11- 12%), or more when the temperature gets close to 0C (-0.1C) and the freeze-thaw-point

August 8, 2014

4-3 Heave as an indicator for fracture formation 29

was approached, detectable as a spike in d) and e) on day 62. This shows that the mainphase change happens in the vicinity of the freeze-thaw-point. The slight moisture contentvariation can probably be explained with enhanced phase change of the hygroscopic andcapillary water. The same behaviour can be seen in b) and c) at lower depths of -0.20 m and-0.10 m. Throughout the frozen state they show a moisture content of 10% and increase tothe initial value of 28% and 20% for -0.20 m and -.10 m respectively. A slight depletion ofthe moisture content could be detected in b)after the first cycle (thaw state: 20% → 17.5%;frozen state: 10% → 8%), which might be associated with cryosuction, where liquid wa-ter migrates in the direction of ice bodies close to the permafrost table, here roughly at 0.25 m.

4-3 Heave as an indicator for fracture formation

4-3-1 LVDT measurements

The LVDT data are of good quality. It is possible to detect small heave changes throughoutthe seasonal cycles and the formation of bigger fractures over the experiment. Figure 4-4 showsthe linear vertical displacement for all rock samples in the two thermal environments. Thegrey curves represent the air temperature (light grey) and that in the saturated gravel beds(dark grey). For the seasonal frost the basal gravel temperature fluctuates above and below

Figure 4-4: Evolution of heave (LVDT) over a time window of 15 cycles, showing a significantepisode of heave at 150 days after the start of the experiment.

August 8, 2014


zero as intended. The basal gravel temperature of the permafrost should remain continuouslyat -10C. This is true except around day 150, where the gravel temporarily warms up to15C, causing the permafrost to thaw. The unexpected rise was due to a failure in the basalcooling system where it was shut down for five days. For the experiment this event can beseen as an unplanned heat wave similar to the summer heat wave in 2003 where the activelayer thickens due to deeper thawing. The effect of an extreme summer is clearly visible atthe heave measurements, as both TU blocks display heave of 3 mm. Murton et al. (2006b)experienced two fracture stages in earlier physical modelling of ice segregation in permafrost(see also chapter 2-1). Due to the basal cooling failure and the associated heat wave verticaldisplacement advanced rapidly, as certainly much more liquid water was available because ofthe high temperature compared to a normal thaw cycle. The subsequent gradual increasemay be attributed to, micro fracturing (normally before fast displacement) or further iceaccumulation in the existing ice filled fractures. For the permafrost environment the two(TU) blocks (blue and red) behave in a very similar fashion, whereas the WS (green) showsalmost no heave variation. In the seasonal frost environment the WS (red) shows again nochanges in heave during the time, whereas the two (TU) blocks behave quite differently. B5has almost no linear displacement apart from seasonal effects. B4 on the other hand shows agradual increase after the 3rd cycle. Variations between B3 and B5 may be a result of, despitethe assumption of uniformity, differences in mineralogy (clay content). This was supportedby visual inspection.

4-3-2 Visual inspection of fractures

Insulation was removed and samples were inspected after the 27th seasonal cycle in orderto see how fractures had developed. In Figure 4-5 all blocks are shown. The top row ofphotos reflect the permafrost regime. B1 and B3 (TU) show mm thick horizontal cracks filledwith segregated ice in the vicinity of the permafrost table (-0.25 m). In addition, B1 appearsvisually dryer in the top half than B3, probably due to cryosuction. The small tilt angle of thefractures may be caused by pre-existing stress patterns in the material or the experimentaldesign and thus variable heat flux. The sample face adjacent to another block sample has lessinsulation, so the heat flux is slightly higher on that side and the fractures appear higher. B4and B5 (TU) show very different depth sections. B4 has multiple small and bigger fracturesover the whole sample where B5 shows almost no fractures, apart from a small feature at-0.18 m depth (27 cm). This apparent difference in behaviour can also be seen in the heavemeasurements, where B4 (Figure 4-5-b) blue line) has a gradual increase associated withdevelopment of micro cracks. B5 (green line) on the other hand, only shows small seasonalcycling fluctuations. The WS blocks (B2 and B6) show no signs of cracks until the 27th cycle.For B6 the spalling on the left corner was pre-existent. The same is true for the diagonalcrack along a calcite vein, but it is not clear if it opened wider throughout the experiment. Ingeneral the fracture positions are as expected, cracks appear parallel to the permafrost tablein the permafrost regime and close to the surface for seasonal frost environment (Murtonet al., 2006b).

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4-3 Heave as an indicator for fracture formation 31

Figure 4-5: Visual inspection of fractures after the 27th cycle.

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August 8, 2014

Chapter 5

Experimental observations:geoelectrical monitoring

In this chapter, the geoelectrical measurements will be be analysed in detail. The aim isto assess the suitability of the two geoelectrical methodology for monitoring freeze-thaw pro-cesses in the two different rock types TU and WS, as well as their applicability in the differentthermal regimes (permafrost and seasonal frost). The focus of the investigation will lie onB3 and B6, as these two samples represent both thermal regimes, both rock types and aremeasured with both geoelectrical measurement techniques, therefore they allow direct com-parison. A direct comparison is made, of the CRI and ERT apparent resistivities, and finallythe resistivity-temperature relationship obtained by ERT is examined.

5-1 Characterisation of data error

The section begins with a detailed characterisation of geoelectrical data errors and their po-tential sources. Errors in the geoelectric data can be systematic or random. Systematicerrors occur with a flaw in the equipment or poor electrical (galvanic/capacitive) contact andneed to be corrected or rejected. Random errors are stochastic fluctuations in the electrodecoupling or changes in the current pathways and can be characterised by forward and re-ciprocal measurements (Krautblatter et al., 2010; Slater et al., 2000). This section analysesand evaluates different error sources for both measurement techniques. To begin with, thedifferent ground coupling mechanism are investigated, which is followed by the reciprocity ofboth measurement methodologies and the effect of the equipment swap.

5-1-1 Ground coupling

Electrode coupling is a critical error source for ERT measurements. High contact resistancesmay prevent current injection or generate unstable potential measurements, therefore poorelectrode coupling can bias geoelectrical measurements very significantly (Zonge and Hughes,

August 8, 2014

34 Experimental observations: geoelectrical monitoring

1986). The ERT contact resistance is based on the physical contact of the electrodes andthe ground (galvanic contact). The CRI contact impedance on the other hand is impairedby the conductor separation and related dielectric. But both determine the possible injectioncurrent and thus effect the stability of the measurement.

ERT on TU. The ERT contact resistances were specifically measured once in the thawedand frozen state after the 27th and during the 28th cycle respectively. Unfortunately, thecontact resistances were not assessed systematically at the beginning of the experiment. Theresults of the ERT contact resistance measurements for B3 and B6 are shown in Figure 5-1.Further results for B1 and B4 are included in Appendix B, Figure B-1. On the left are themeasurements taken in the unfrozen and on the right in the frozen state. The two rock typesshow a broad range of coupling resistances in line with expectations. TU (Figure 5-1a) has

(a) B3-TU: Permafrost

(b) B6-WS: Seasonal frost

Figure 5-1: ERT contact resistance for B3 and B6. On the left, the rock sample are the thawedstate, where the right side represents the frozen state. Note the different contactresistance range.

August 8, 2014

5-1 Characterisation of data error 35

(a) B3-TU: Permafrost (b) B6-WS: Seasonal frost

Figure 5-2: CRI contact impedance for B3 and B6. The block samples are in the frozen state.Note the different contact impedance ranges.

a coupling resistance with medians of 62.74 kΩ and 238.48 kΩ for the unfrozen and frozenstates respectively. The thawed state (left) has an elevated contact resistance (>70 kΩ) inthe lower part of the rock sample, which is associated with the permafrost and temperaturesranging between -5 and 0C. The upper part has much lower resistances around 30 kΩ, dueto the thawed state of the rock sample and thus enhanced galvanic coupling. In the frozenstate (right) all contact resistances are significantly higher (median of 238.48 kΩ), includingthe permafrost. This can be explained with the sample temperature of -10C throughout,which is lower than the temperature of the permafrost in the thawed state. B1 and B4 (theother TU samples) show similar behaviour and contact resistance ranges (see B-1).

ERT on WS. The second example is the WS in the seasonal frost thermal regime. Asexpected the WS shows much higher contact resistances than the TU over the whole block.The median values for hard rock are 1533.24 kΩ and 12969.48 kΩ for the unfrozen and frozenstates, respectively. Crucially, this is by a factor of 100 higher than for the TU. In the thawedstate the temperature for the whole block lies at 10C and decreases to -10C for the frozenstate. This temperature change is clearly visible in the contact resistances, which increasedramatically. Nevertheless, a few outliers (49, 64, 120, 121 and 128) stay more or less thesame. At high contact resistances, such as those measured for the WS, it is a significantchallenge to obtain stable, noise free ERT measurements.

These results clearly highlight the direct relationship between galvanic coupling and tempera-ture. Particularly ERT measurements on the WS and possibly those on the TU in the frozenstate, are likely to be affected by coupling-related noise.

CRI. In an attempt to overcome the limitations of the galvanic coupling, the CRI methoduses capacitive coupling with the ground to inject current. The challenge is to make thecoupling impedance sufficiently small. Large separation between the capacitors (rock surfaceand sensors) will increase the coupling impedance, potentially reducing the injected current(see 3-1). Therefore a coupling test similar to that undertaken for ERT was devised for the

August 8, 2014


CRI measurements. The CRI prototype system is not able to do this test automaticallylike the GeoTom, therefore it had to be done manually. The contact impedance is given bydividing the measured applied voltage on one sensor by the applied current. Figure 5-2 showsthe results of this coupling test in the frozen state. Measurement in the thawed state arescheduled to be done soon. The TU (5-2a) shows a contact impedance median of 440.62 Ω,whereas the contact impedance median for the WS (5-2b) is 1361.06 Ω. These values arecompatible with the values calculated for different capacitor separations in Section 3-3, listedin Table 3-1. The results imply that the elevation of the sensor does not increase beyond0.005 mm and hence a sufficient injection current of 0.01 to 0.1 mA is possible, depending onthe nature of the dielectric between the sensor and the rock (water, ice or air). These contactimpedances measured in the frozen state and both blocks were at a temperature around-10C. No interferences could therefore be drawn on the relationship of the contact impedancewith temperature, similar to the approach pursued for ERT. In the case of CRI, the contactimpedance appears more indicative of sensor separation, and can therefore be used to assesswhether sensors are still properly attached to the rock surface.

5-1-2 Reciprocity

The geoelectrical reciprocity theorem after (Parasnis, 1988) states it is insignificant whichdipole of a configuration acts as the current or the potential dipole, as they both are identical.In practice this is not strictly the case and they differ to some degree. The difference betweenforward and reciprocal measurements can be used to assess random error (Krautblatter et al.,2010). Forward and reciprocal measurements are standard practice in ERT, therefore the ERTmeasurements are the mean of the two measurements including the reciprocal error (plottedas grey bars). The following standard deviation calculation was used for the reciprocal error:

recipError = std =

√√√√ 1

n

n∑i=1

(xi − x)2, (5-1)

with,

x =1

n

n∑i=1

xi, (5-2)

and n is the number of elements.

ERT. Figure 5-3 shows the forward and reciprocal measurements as cross-plots in the thawedand frozen state. TU shows for both states a high correlation, with a R2 of 0.99 and a gradientfor the linear regression of 0.98 and 0.99 respectively. The TU shows also an increase of trans-fer resistance from the thawed to the frozen state, as expected, which could be explained withleakage currents (discussed in the next Section 5-2) or the presence of permafrost, wherebythe lower transfer resistances are associated with the active layer and the higher resistanceswith the permafrost. The latter is perhaps less likely as the variation between the active layerand the permafrost should be less pronounced. The WS shows a good correlation for thethawed state, which deteriorates dramatically in the frozen state (R2 = 0.99 and R2 = 0.73,respectively). This poor reciprocity of the transfer resistance in the frozen state reflects the

August 8, 2014


(a) B3-TU: Permafrost in thawed state.R2 = 0.99, lin. regression y = 0.98x

(b) B3-TU: Permafrost in frozen state.R2 = 0.99, lin. regression y = 0.99x

(c) B6-WS: Seasonal frost in thawed state.R2 = 0.99, lin. regression y = 1.01x

(d) B6-WS: Seasonal frost in frozen state.R2 = 0.73, lin. regression y = 0.73x

Figure 5-3: ERT reciprocity: Cross-plot forward versus reciprocal. a) and c) represent data setsmeasured on the thawed samples. b) and d) were measured on the frozen sample.Note the different axes.

high contact resistances for the WS (discussed in chapter 5-1-1), and highlights further theproblems, that can occur with ERT measurements in highly resistive environments.

An attempt was made to quantify ERT data quality over time in a simple manner. Table 5-1shows the number of all quadrupole configurations per block, for which more than 25% of themeasurements over time show a reciprocal error greater than 5%. This shows the limitation ofthe ERT technique on highly resistive material, as practically all configurations develop a highreciprocal error during the frozen states of the experiment (see Figure 5-3d). This is becausethe ERT system has to drop the current, so that the resulting potential measurement becomesunstable. Hence, this has to be considered in the evaluation of DC electrical measurementdata.

CRI. Whiles forward and reciprocal measurements are standard practice for ERT, they aremore complicated to perform with the CRI prototype system, because the transmitting andreceiving channels of this instrument are fixed and can not be interchanged. As a consequence,the reciprocal measurements have to be done manually by swapping and re-connecting all

August 8, 2014


Table 5-1: Number of all ERT quadrupole configurations per block, for which more than 25%of the measurements over time show a reciprocal error greater than 5%.

error limitNumber of configurations with > error than thelimit (total configurations: 344)

B1−TU 5 % 95 (28 %)B2−WS 5 % 343 (99 %)B3−TU 5 % 115 (33 %)B4−TU 5 % 43 (13 %)B6−WS 5 % 344 (100 %)

(a) B3-TU: Permafrost in thawed stateR2 = 0.37, lin. regression y = 0.64x

(b) B3-TU: Permafrost in frozen stateR2 = 0.69, lin. regression y = 0.86x

(c) B6-WS: Seasonal frost in thawed stateR2 = 0.82, lin. regression y = 0.94x

(d) B6-WS: Seasonal frost in frozen stateR2 = 0.89, lin. regression y = 0.66x

Figure 5-4: CRI reciprocity: Cross-plot forward versus reciprocal. a) and c) represent data setsmeasured at the thawed samples. b) and d) were measured at the frozen sample. Themeasurements were taken after and during the 27th 28th cycle. Note the differentaxes.

August 8, 2014


transmitter and receiver cables. This implies considerable effort and was done three timesthroughout the experiment, twice in the unfrozen state and once in the frozen state.

Figure 5-4 shows the cross-plot of the CRI forward and reciprocal measurements for B3 andB6 for the thawed and frozen states. Plot a) and b) show the TU in the permafrost regime.The scatter of the measurements for both states are relatively high with R2 = 0.37 and R2

= 0.69 for the unfrozen and the frozen states, respectively, with a slightly better correlationfor the latter. Linear regression through the origin shows a significant deviation from theintended behaviour of y = x. Plot c) and d) on the other hand show the hard rock WS inthe seasonal frost regime. Here the scatter of the reciprocal data is much smaller comparedto the TU and R2 is 0.82 and 0.89 for the two different regimes. The deviation of the linearregression particularly for the unfrozen state is close to the desired behaviour, the deviationfor the frozen state however is larger.

One potential cause of this systematic error between forward and reciprocal measurementscould be differences in the co-axial cables, that are used for the transmitting and receivingsides. The co-axial cables have slightly different capacitance values. The grey colouredtransmitter cable and the copper coloured receiver cable have a cable capacitance of 63.65pFm−1 and 96.45 pFm−1, respectively (see Figure 3-4-left). The cables are approximately 2meters in length, hence the cable capacitances are roughly 130 pF and 195 pF. However, theobserved deviation of the transfer impedance between forward and reciprocal measurementsare not entirely consistent between the two rock types and regimes. The deviation for theTU is large in the thawed state and reduces in the frozen one. Whereby for the WS thedeviation is small for the thawed state and increases for the frozen state. In general, thoseresults suggest a much more stable performance of the CRI technique in highly resistiveenvironments. Particularly the linearity of the reciprocity is much higher for the hard rockWS as the soft rock TU.

5-1-3 Effect of equipment swap

For logistical reasons both geoelectrical measurement units had to be exchanged during theexperiment. Theoretically these events should not affect the data significantly, as only thehardware was exchanged (identical systems) and no changes were made to the sensors or atthe rock samples themselves. But in practice the exchanges did have an effect on the dataquality.

CRI hard rock. The offset between the initial and new CRI units can be quantified bylooking at measurements taken right before and after the exchange on the same day. Thesystem swap had almost no affect on the data quality of B6, see Figure 5-5b. The twodatasets are in good agreement, hence R2 is 0.98 and the gradient suggests a deviation of 4%.Therefore the effect on B6 can be regarded as negligible.

CRI soft rock. B3 in comparison to B6 shows larger deviations. Whiles the datasetsthemselves are still in relatively good agreement (R2 = 0.86), the gradient of the linearregression suggests that the latter measurements with the new CRI unit are significantlylower compared to the initial unit. This is unexpected and difficult to explain, especially asB6 shows no effect of the swap and the measurements are taken within the same measurementsequence and a small time delay of 3.5 hours. That said, the apparent resistivities over time

August 8, 2014


(a) B3-TU: Permafrost,R2 = 0.86, y = 1.21x

(b) B6-WS: Seasonal frost,R2 = 0.98, y = 1.04x

Figure 5-5: Effect of the CRI unit change

show an improvement of the measurement quality with the new CRI unit. It is thereforeprudent to avoid direct comparison of datasets from the two different devices.

ERT. Unfortunately, for the GeoTom swap no direct measurement exist before and afterthe event. Hence, a comparison can only be made over a longer time interval. A large timedifference could imply large deviation in temperature and possible fracture development.The latter is true especially as the swap occurred close to the rapid vertical displacement.Consequently, any differences observed by direct comparison are likely to be dominated byother effects, and will therefore not give any information about the impact of the GeoTomswap.

5-2 Apparent resistivity as a function of rock type and samplecondition

In this subsection, the distribution of the apparent resistivity estimated with the ERT andCRI methods is under investigation. The range of apparent resistivity varies substantiallybetween the two rock types and the two different sample conditions.

ERT soft rock. The distributions of the ERT apparent resistivity for B3 and B6 in thedifferent thermal states are shown in Figure 5-6. B3 shows a strong bimodal distributionfor both thermal states. The first mode includes apparent resistivities smaller than 1 kΩmand 3 kΩm for the thawed and frozen states, respectively. The second mode contains fewermeasurements and ranges between 2 to 4 kΩm and 4 to 8 kΩm for the two conditions.This indicates an elevation of apparent resistivity by a factor of 2 between the two thermalstates. The lower resistivity values from the first mode matches the resistivity of tap water(Uhlemann, 2012). Hence, it is possible that ERT might be affected by leakage currents.Assuming that most ERT electrodes have direct contact to the water film between the rocksample and the insulation of the CRI sensors. This water film and the water in the gravelbed might build a preferable flow path for the current around the rock sample, which maycause leakage currents.

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5-2 Apparent resistivity as a function of rock type and sample condition 41

(a) ERT-B3-TU: Permafrost

(b) ERT-B6-WS: Seasonal frost

Figure 5-6: Distribution of the ERT apparent resistivities for B3 and B6 in frozen (blue) andthawed (red) thermal states. Note the different axes.

ERT hard rock. In contrast to B3, the range of the apparent resistivity distribution for B6is much broader and shows a uni-modal distribution (note the different axes). The rangeslie between 1 kΩm to 12 kΩm and -50 kΩm to 150 kΩm for the thawed and frozen states,respectively. The direct comparison of the thawed and frozen thermal states shows a smallshift of the peak value to higher resistivities, but mostly it shows a significant broadeningof the resistivity distribution. This broadening can probably be explained with the strongincrease in reciprocal errors (see Section 5-1-2 , Figure 5-3d). The distributions of the otherrock samples B1, B2 and B5 are in Appendix B, Figure B-5. The distribution ranges varysignificantly for the same rock type and different thermal environments, all TU have a bimodaldistribution and the WS show a uni-modal distribution.

CRI. Figure 5-7 shows the corresponding apparent resistivity distributions for the CRI tech-nique. The main peaks of the TU distributions are roughly in the same apparent resistivityrange as the higher mode of the bimodal distribution of the ERT measurements. The WS onthe other hand, shows considerably higher apparent resistivity values for both thermal states,whereby the distribution is dominated by a peak value at 20 kΩm for the thawed state andbroadens for higher values in the frozen state.

August 8, 2014


(a) CRI-B3-TU: Permafrost

(b) CRI-B6-WS: Seasonal frost

Figure 5-7: Distribution of the CRI apparent resistivities for B3 and B6 in frozen (blue) andthawed (red) state. Note the different axes.

5-3 Observations specific to CRI

5-3-1 CRI transfer impedance

As CRI uses an alternating (sinusoidal) current, we obtain the complex transfer impedance bymeasuring the real and imaginary components of the observed potential difference. Accordingto the theory and numerical simulations the measured complex impedances are expected tohave a small phase angle and lie in the fourth quadrant (Uhlemann and Kuras, 2014). Inpractice this is mostly ( but not always) true; the complex transfer impedances are shown in5-8 for both rock types and in different thermal states.

The observed complex signal from the WS lies practically always in the forth quadrant andhas a small phase angle of approximately ϕ = -9 for both thermal states. In comparison, theTU phase angle varies between the two sample conditions (-10.8 and 25.8); it also changesover time. At the beginning of the experiment and in the final thaw cycle (where TDRmeasurements had been switched off), the phase angle varied to lesser degree and remainedcontinuously in the first quadrant throughout, in the range of 10 to 30.

A number of hypothetical explanations exist for the large phase angle variation and theoccurrence of measurements in the first quadrant instead of the fourth. These might include

August 8, 2014

5-3 Observations specific to CRI 43

(a) B3-TU: thawed state (2.8C),R2 = 0.14, mean ϕ = -10.8

(b) B3-TU: frozen state (-10.2C),R2 = 0.19, mean ϕ = 25.8

(c) B6-WS: thawed state (8.9C),R2 = 0.20, mean ϕ = -8.9

(d) B6-WS: frozen state (-10.5C),R2 = 0.31, mean ϕ = -9.8

Figure 5-8: Complex transfer impedance acquired with CRI for both rock types.

electromagnetic noise in the surrounding environment, the porosity including water content,or the different dielectric properties of the rock material, water and ice. Noise induced by thesimultaneous TDR measurements is less likely, as TDR measurements operate at significantlyhigher frequencies in the MHz range. Moreover, the TDR sensors are never located on thesame sample as CRI sensor arrays. The CRI unit applies a 15 kHz frequency and uses a lock-inamplifier, which detects frequencies which are in sync with the applied frequency. Dielectriceffects, which are not accounted for in the simplest form of CRI theory, are more likely tocause significant deviation of the estimated apparent resistivity. The TU has a relatively highporosity (30-40%) and therefore a high water/ice content. From the visual inspection (seeSection 4-3, Figure 4-5), it also seems that the TU has a higher clay content than assumedin the beginning. Clay and water are highly polarisable, and this may influence the complexCRI signal more strongly than the present theory can account for.

Comparing the spread of the measurements, the TU shows greater scattering and a broaderdistribution than the observed values from the WS. Both rock types show better alignment inthe frozen state than in the thawed state. These results seem to corroborate earlier indicationsthat CRI performs significantly better in highly resistive environments.

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5-3-2 CRI applied current measurements

The applied current is an important property for both geoelectrical methodologies, given thatthe apparent resistivities are calculated by dividing the observed potential difference by theapplied current, then multiplying with the geometric factor (see Equation 2-4). Many DCERT instruments apply a known constant current, so that the observed potential difference isthe main source of error. The current applied by the CRI system is dependent on the contactimpedance between the sensors and the rock sample surface, and varies for each measurement.As described in Section 3-3-1, the applied current is calculated with the measured current inthe transmitter, the return current in the cable and the phase shift between them, because adirect measurement at the capacitive sensors is technically very difficult. The measurementestimate of the applied current depends therefore on the accuracy of those three values.

The impact of the applied current on the apparent resistivity is shown in Figure 5-9. Thepurple curve is the estimated apparent resistivity, the green curve is the observed potentialdifference and the red curve the applied current. The bottom plot shows the temperaturedistribution with depth including the electrode positions (white dashed lines). The appliedcurrent shows unexpected steps in the thawed state, throughout the experiment. Basedon the simplicity of the geoelectric measurement technique, where a potential difference ismeasured in the potential field generated by an injected current, the observed potential shouldbe directly linked to the applied current. In the illustrated example this is not exactly thecase. The observed potential does not consistently follow the lead of the applied current andtherefore the estimated of apparent resistivity is likely to be subject to errors. The variations

Figure 5-9: Measured values of the applied current for CRI (red), the observed potential (green)and the calculated apparent resistivity for one specific quadrupole configuration(small inset block) of B6.

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5-4 Galvanic versus capacitive apparent resistivity time series 45

of current and potential seen in Figure 5-9 suggest a problem with the measurement of thecurrent system output, that of the cable return current or the calculation of the sample currentfrom both. The source of the problem is not yet identified. In the mean time, as the observederrors reflected in the current steps are high, they have to be corrected or indeed rejectedaltogether before quantitative interpretation such as resistivity inversion are done.

5-4 Galvanic versus capacitive apparent resistivity time series

The CRI and ERT apparent resistivities were acquired over multiple freeze and thaw cycles.By way of example, cycles 4 to 9 are analysed in the following. A direct comparison of the twomeasurement techniques is likely to be subject to small inaccuracies due to the lateral offsetof the electrode positions on the small scaled block samples. However, for the permafrostregime (B3) two quadrupole configurations were chosen as an example and these are shownin Figure 5-10. One configuration covers the upper part and active layer of the block and theother quadrupole the lower part with the permafrost. The range of the apparent resistivitiesvaries with configuration and measurement technique.

Permafrost on soft rock. For the permafrost (Figure 5-10a) the resistivity ranges from 3kΩm to 10 kΩm for CRI and from 2 kΩm up to 5 kΩm for ERT, for the frozen and thawedstate. This implies a relative resistivity increase of 250 to 330% between the two states. Notethat the temperature of the permafrost in the thawed state does not rise above -1C. As theincrease of the resistivity between the two thermal states is large, both techniques are able tomonitor the small temperature deviations close to freezing point; for example in the thawedstate the small gradient in the resistivity curves is comparable to the slow increase in tem-perature. The offset between the two geoelectric techniques as relative difference normalizedby the CRI values are 0.34 and 0.5 for the frozen and thawed conditions, respectively.

Active layer on soft rock. In comparison, Figure 5-10b shows the active layer. Theapparent resistivity ranges between 2 kΩm and 10 kΩm for CRI (relative change of 500%).This is similar to the CRI range in the permafrost and shows only lower resistivities in thethawed state, due to higher temperatures and further decrease in resistivity. On the otherhand, the ERT apparent resistivities range between 0.2 kΩm and 1.5 kΩm (relative changeof 750%) and is significantly lower than to the permafrost measurements. This drop in theapparent resistivity between the permafrost and the active layer could potentially be explainedby leakage currents, whereby the current preferentially uses a thin water layer around thesample (see Section 5-2).

Seasonal frost on hard rock. The results for the WS in the seasonal frost environmentare shown in Figure 5-11. The chosen quadrupole configurations are shown as a small blockin the left bottom corner and as dotted lines in the temperature plots. In contrast to theTU, the WS has much higher resistivities and the ERT measurements are likely to be subjectto errors, visible in the reciprocal errors plotted as grey bars. The CRI and ERT apparentresistivities remain in similar ranges for both quadrupole configurations as the whole blockcycles through the temperature changes. However, the resistivity range for CRI is from 25kΩm to 150 kΩm for the thawed and frozen state in the deeper quadrupole configuration (see5-11a). For the higher configuration (5-11b) the resistivities in the thawed state are slightlylower (20 kΩm) due to the marginal higher temperature closer to the surface. These ranges

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(a) Permafrost

(b) Active layer

Figure 5-10: B3-TU: Galvanic and capacitive apparent resistivities (ρa) estimated by onequadrupole configuration. ρa, temperature and the linear displacement are plottedversus time for multiple seasonal cycles. The temperature color plot includes theelectrode positions (white-dashed lines) c1, c2 for the current and p1 and p2 forthe potential. The small block on the left bottom corner displays the location ofthe electrode on the rock sample.

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5-4 Galvanic versus capacitive apparent resistivity time series 47

(a) deeper quadrupole configuration

(b) higher quadrupole configuration

Figure 5-11: B6-WS: Galvanic and capacitive apparent resistivities (ρa) estimated by onequadrupole configuration. ρa is plotted against time, temperature and the lineardisplacement for different seasonal cycles. ρa, temperature and the linear displace-ment are plotted versus time for multiple seasonal cycles. The temperature colorplot includes the electrode positions (white-dashed lines) c1, c2 for the current andp1 and p2 for the potential. The small block on the left bottom corner displaysthe location of the electrode on the rock sample.

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imply relative resistivity changes of 600% and 1000%. After day 220 into the experiment,a thermal equilibrium close to the freezing point was experienced and a resistivity of 70kΩm and 55 kΩm was obtained close to freezing point. The resistivity difference of the twoconfiguration can once again be explained with a temperature deviation, with temperatures<0C and > 0C for a) and b), respectively.

ERT has a resistivity range of 6 kΩm to 50 kΩm (relative change of 670%) and 6 kΩm to80 kΩm (relative change of 1300%) and apparent resistivities of 22 kΩm and 25 kΩm around0C (day 220) for deeper and higher configurations. The freezing cycles at 242, 290 and 308days into the experiment show further unrealistic behaviour of the ERT measurements. Theresistivity measurements show a step to lower resistivities after an increased resistivity level.The last freezing cycle (day 320) and additionally the behaviour of the resistivity values ofthe second configuration (5-11b) seem to show representative behaviour of ERT in the frozenstate. The difference between the apparent resistivities normalized by the CRI resistivity are0.76 to 0.66 for deeper (a) and 0.70 to 0.46 for higher (b) configurations for both thermalconditions.

The above differences between the two geoelectric techniques may be due either to a systematicerror or dielectric variations with fractional ice volume. Dielectric effects occur above 1 kHzin highly resistive environments (Hauck and Kneisel, 2006), such as permafrost or the WShard rock and may influence primarily the CRI measurements, which operates at 15 kHz.Nevertheless, it is shown that both geoelectric measurements follow the same resistivity-temperature behaviour throughout the seasonal cycles.

5-5 Variations of changes in geoelectrical data with temperature

This section analyses the changes in the geoelectrical data through a thawing period. Themeasured data is from the 28th cycle, because for this thaw cycle the measurement frequencywas increased to 15 minutes for CRI and 30 minutes for ERT, in order to achieve a highermeasurement coverage throughout the thawing process. The focus will lie on B3 (TU) andB6 (WS) for CRI and B1 (TU) for ERT.

(a) ∆T = -10.27C, R2 = 0.94,lin. regression y = 2.12x

(b) ∆T = -5.03C, R2 = 0.97,lin. regression y = 1.67x

(c) ∆T = -0.25C, R2 = 0.99,lin. regression y = 1.02x

Figure 5-12: ERT-B1-TU: Comparison of apparent resistivity data to a reference dataset duringthawing. The reference is in the unfrozen state with a sample temperature of0.5C.

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5-5 Variations of changes in geoelectrical data with temperature 49

Figure 5-13: Temperature dependency of the linear regression gradient through a thaw period.

ERT. The reference state chosen for B1, is in the unfrozen state with a sample tempera-ture at the depth of 0.15 m of 0.5C. Figure 5-12 shows the results of three different stagesthroughout the thawing cycle. a) is in the frozen thermal equilibrium at -9.78C, hence thereis a temperature difference (∆T) to the reference model of -10.27C. R2 with 0.94 indicatesa high correlation, which is also visible in the low scatter of the measurement points. Thelinear regression through the origin has a gradient of 2.12, hence the apparent resistivity isapproximately by a factor of 2 higher in the frozen state compared to the unfrozen state.The grey bars indicate the forward and reciprocal errors of ERT measurement. With ongoingthawing the temperature difference and the gradient of the linear regression are continuouslydecreasing in comparison to the reference model (-5.06C and -0.25C for b) and c), respec-tively). Hence, Figure 5-13 is the temperature dependency of the linear regression gradientthrough the thaw period. It starts with a maximal factor of 2.2 and decreases until it levelsoff close to one. This confirms the strong temperature dependency of the apparent resistivitybelow 0C for ERT measurements.

CRI. The same analysis was made with the CRI data. The reference states for B3 and B6 areat a sample temperature of 2.1C (0.15 m depth) and 3.9C (0.35 m depth), respectively. Thedifferent depths for the representative temperature were chosen due to the different thermalregimes (permafrost and seasonal frost). For the permafrost regime a temperature reference




Figure 5-14: CRI-B3-TU: Comparison of apparent resistivity to a reference measurement. Thereference is in the unfrozen state with a sample temperature of 2.1C.

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depth of 0.15 m in the active layer was chosen, in order to get temperature fluctuation aboveand below freezing point. B6 is in the seasonal frost regime and has no permafrost, thereforethe whole sample fluctuates above and below freezing point. B6 (WS) has further a fastertemperature adaptation than B3 (TU) (see Section 4-1). According to this, a deeper andmore dampened reference temperature of 0.35 m was chosen.

CRI on soft rock. Figure 5-14 a) to c) are different stages with different temperature devia-tions to the reference model in the unfrozen state for B3. R2 is very high (>0.95) throughoutall blocks, which indicates good correlation and stable measurements. The gradient of thelinear regression decreases with decreasing temperature deviation, which is illustrated for thewhole thaw period in Figure 5-15. In the frozen state the apparent resistivity is by a factorof 1.6 higher than in the reference measurement. During thawing the gradient decreases con-stantly and levels off at a factor of 1. Figures 5-15 and 5-13 can be compared directly, becauseboth rock samples (TU) are in the same thermal environment (permafrost). The ERT mea-surement show a larger deviation between the frozen and the unfrozen states, as the maximalfactor for ERT is 2.2 and for CRI is 1.6. Hence, the rate of change is greater for the ERTmeasurements as the temperature range spans approximately 8C for both measurements.

CRI on hard rock. In comparison, Figure 5-16 represents B6 the hard rock (WS) in the




Figure 5-16: CRI-B6-WS: Comparison of apparent resistivity to a reference measurement. Thereference is in the unfrozen state with a sample temperature of 3.9C. The mea-surements coloured in green are rejected outliers.

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5-6 Resistivity-Temperature Relationship 51


seasonal frost environment. The goodness of the linear fit decreases with higher temperaturedeviations (R2 = 0.62 and 0.63 for ∆T = -13.38 and -5.91, respectively). The green measure-ment points (Figure 5-16c) are outliers and not included in the linear regression calculations.These outliers are measurements made with the CRI current sensor 57, which shows unex-plained steps in the current injection (explanation in Section 5-3-2). Hence, all configurationsthat include current sensor 57 are rejected. The temperature dependency is again very strongand has for the WS a maximal factor of >4.5 (see Figure 5-17).

5-6 Resistivity-Temperature Relationship

The resistivity-temperature relationship can be used to image the temperature instead ofresistivity (Krautblatter et al., 2012; Uhlemann, 2012). Due to a lack of sufficiently densemeasurements for CRI, the temperature changes during the cycles. The following analysis ofthe resistivity-temperature relationship is only based on the ERT data from B1, as only forthis block a complete freeze and thaw period was measured with high measurement density.

The resistivity-temperature measurements for a specific configuration (small block) are shownin Figure 5-18. The measurement points were fitted with a linear function for values abovethe 0C for both periods and an exponential function below 0C for the freeze period andquadratic function for the thaw period. The fitting functions for the freeze period are:

T > 0C : ρa(T ) = −5.52 ∗ T + 269 (5-3)

T < 0C : ρa(T ) = 239 ∗ exp(−0.12 ∗ T ) (5-4)

and for the thaw period, respectively:

T > 0C : ρa(T ) = −10.17 ∗ T + 293 (5-5)

T < 0C : ρa(T ) = −1.04 ∗ T 2 − 47.20 ∗ T + 308 (5-6)

These functions are unlikely to be very valid as they are derived from one freeze/thaw periodonly and reflect a single electrode configuration. But they show clearly the hysteresis of of theresistivity-temperature relationship over different periods, which suggests that two separatecalibration curves are needed.

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Figure 5-18: Temperature-Resistivity relationship for B1. In blue and red are the freeze andthaw temperature-resistivity hystereses, respectively. The temperatures above 0Care fitted with linear functions and below 0C with an exponential function for thefreeze period and a quadratic function for the thaw period starting from 0 until-5C.

The different hysteresis curves are likely associated with changes in freeze and thaw pointand supercooling of water. During freezing the remaining bulk water accumulates salts andminerals as ice is formed and the freezing point is lowered marginally. More crucial is thesupercooled state of the water, where water remains liquid even below its freezing point. Thisdevelopment is shown in the exponential curve of the freezing hysteresis below 0C. Duringthe thaw period, the phase change happens on the thaw path.

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Chapter 6

Distortion of the ERT apparentresistivities due to current channelling

This chapter examines a possible distortion of the apparent resistivities due to current chan-nelling in fractures. This phenomenon is associated with occurrences of negative, elevatedor reduced apparent resistivities under the same sample conditions and identical quadrupoleconfiguration. Such distortions appeared systematically in the ERT measurements in thepermafrost environment on the frost-susceptible rock sample B1 and B3 (TU). As the ERTdata on B3 with GeoTom100 are of limited quality and particularly the occurrence of negativeapparent resistivities is not as distinct as for B1, the focus will lie on the ERT data of B1.

Physically modelled data. Figure 6-1 a) to c) shows the resistance over time with differentcross-hole configurations. The small block on the left illustrates the electrode positions on therock samples. The red and green dots represent the current and potential dipoles, respectively(vice versa for reciprocal measurements). Three configurations with the same current dipolebut different potential dipoles were chosen as examples. The plotted apparent resistivity isthe mean between the forward and reciprocal measurements. The standard deviations areillustrated as grey bars, at the scale of the plot, they are mostly small and not visible. Thecyan line represents the swap of the GeoTom100 to GeoTom200 close to day 200 into theexperiment. The impact of this swap is discussed in Section 5-1-3 or visible in Figure B-4, in Appendix B. The second plot is spatial temperature distribution over time includingthe four electrode positions (white-dashed lines for C1, C2, P1 and P2) and the dominanthorizontal fracture zone (see Section 4-3-2, Figure 4-5). This illustrates where the electrodesare positioned compared to the fracture development.

Four phases of measurements can be identified:

(I) Start of the experiment, where the block is still uniform.

(II) The freezing cycle after the summer cycle simulating extreme temperatures.

(III) One measured freezing-thaw cycle after the GeoTom swap.

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54 Distortion of the ERT apparent resistivities due to current channelling

(a) Active layer: The ρa changes its sign over time

(b) Permafrost table: The ρa shows a strong elevation over time

(c) Permafrost: The ρa shows a slight elevation over time

Figure 6-1: Apparent resistivity (ρa) versus time. The current electrodes (red) remain the same,whereas the potential electrodes (green) cover different elevations in the blocks. Theplotted ρa is the mean between forward and reciprocal. The error is smaller than themeasurement point or plotted as small error bars (grey). ρa changes distinctivelyafter a rapid vertical displacement (heave) took place. The green and white dashedlines indicate the dominant fracture zone and the electrode positions, respectively

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55

(IV) And three continuous stable seasonal cycles.

In the first phase (I), the apparent resistivity is around 500 Ωm in the frozen state and smallor close to zero in the thawed state, this is thought to reflect the resistivity-temperature-relationship above and below the freezing point illustrated by Hoekstra et al. (1975) explainedin Section 2-2. The similar range of resistivity through Figure 6-1 suggests that the blocks werestill uniform. In phase II, the blocks experienced a rapid vertical displacement (fracturing)during a slow cooling down after the whole rock sample experienced high temperatures.The resistivity remained close to zero until a sudden step occurred, which correlates witha decreasing temperature to the freezing point at the lower fracture zone level. Afterwardsunexpected high resistivity for a) and b) and a sign change for the resistivity in c) is observed.Phase III and IV illustrate once more the resistivity-temperature relationship in the frozenand unfrozen states, where the resistivity fluctuates with the temperature changes. Plots a)

(a) (b)

(c)

Figure 6-2: Simple numerical permafrost model with Res3DMod. (a) is the uniform frozen statewith resistivity of 20k Ωm, (b) the unfrozen state with a high resistivity contrast.Blue represents a low resistivity of 20 Ωm for unfrozen material and c) is the frozenstate with highly conductive (20 Ωm) pathways.

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Figure 6-3: Apparent resistivities of a simple permafrost model. The same electrode configu-ration as in Figure 6-2 was chosen. The color-plot shows the depth of the highresistance interface (frozen to thawed state). The last data point indicates themeasurements deviation with highly conductive pathways.

and b) show additionally once more the elevation in resistivity for the frozen and thawedstates, where configuration c) displays the sign change. Apart from the sign change thefluctuations are correlated with the temperature variations and are in the same range as priorto the fracturing. These sign changes and sudden elevations of the apparent resistivity for onespecific configuration can not be explained by conventional means (e.g. temperature changes)and needs further investigation.

Numerical model. For this reason, a simple two half-space resistivity model including theblock walls and electrode configuration, representative of the rock samples, was consideredfor proof of concept. This was done in Res3DMod. A range of models with changing interfacedepth were calculated, to analyse the behaviour of the apparent resistivities for individualelectrode configurations. Moreover, highly conductive pathways were modelled from oneelectrodes on one side to the other block side, simulating water filled fractures or fissures ina frozen block sample, which channel the current.

Figure 6-2 illustrates three stages. Stage a) is a model of the uniform frozen block with aresistivity of 20 kΩm, b) represents the thawed state of a permafrost rock with an resistivityinterface close to the 4th electrode in a depth of -0.2 m and c) illustrates highly conductivepathways (20 Ωm) in the frozen state from electrode 23 and 31 to the vicinity of electrode 22and 30 on the other block side.

The apparent resistivities obtained for model a) are between 20 and 1500 kΩm and show nounusual values. Model b) with the a large resistivity contrast shows no negative or enhancedvalues, but it displays small apparent resistivities (<100 Ωm) if the current dipole and thepotential dipole are in different resistivity environments (frozen or thawed). This is probablydue to the fact that the injected current only passes the high resistivity interface marginally.The observed potential is therefore reasonably small. In model c) 15% of all configurationsshow a negative apparent resistivity and a high percentage show significantly reduced valuescompared to the total frozen state. Most configurations (90%), that showed a negative valuecontained at least one of the two involved electrodes (23 or 31), but this is not strictly necessaryas the other 10% show. Figure 6-3 illustrates the behaviour of the apparent resistivity forthe three simple models for the highlighted quadrupole configuration in Figure 6-2. The first

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57

modelled data point reflects the resistivity for a fully frozen block. Data point 2 to 16, showsmall apparent resitivities with downward moving resistivity interface and the last data pointshows the occurrence of negative resistivity with highly conductive pathways.

By including those highly conductive pathways the original current pathways were disturbed.In the beginning the electrode configuration was 1-9-23-31 for C1-C2-P1-P2. The distancebetween C1P1 is smaller than C2P1. Adding a newly constructed path for P1 (23) throughthe block close to P2 (30), the distance between P ′1C1 became larger than P ′1C2, thereforethe geometric factor changed. By calculating the apparent resistivity with equation 2-4, aninaccurate geometric factor is used, leading to said distortions. In extreme cases, similar tothe one above, the geometric factor varies not only by a factor but also by sign. Hence,the apparent resistivity might change its sign with development of high conductive pathwaysand current channelling. As a result, this simple model proves the concept that conductivepathways can channel the injected current and distort the apparent resistivities. It alsodemonstrates that the observed negative resistances in the physical experiment are unlikelyto be due to measurement errors.

Nevertheless, further tests were done regarding possible measurement errors, addressing issuessuch as electrode polarisation and high current injection frequency. Those results are shownin Appendix B, Figure B-6 and B-7. According these results, the electrode polarisation waslow or not present and could not cause the distortion of the apparent resistivities. The sameapplies for the injected current frequency. The 1 Hz measurements show, compared to thestandard 25 Hz, smaller reciprocal error, but the measurement sequence for one rock sampletakes approximately 3 hours and is prone to instability. Hence, the 25 Hz frequency wasshown to be an adequate choice.

A conceptual model of the process of how those changes in current pathways occur in the

Figure 6-4: Occurrence of negative apparent resistivity over time on B1. The top plot representsthe mean negative resistivity fraction of the normal and reciprocal measurement,including the error bars. b) is the linear displacement and c) the temperature curvesfor two different depths.

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permafrost experiment is not fully understood yet. Nevertheless, the increased appearanceof negative apparent resistivities after fracture development, is a strong indication that arelationship exists between the appearance of negative apparent resistivities and fracturedevelopment in particular. Figure 6-4 shows this relationship for B1. For the first fewseasonal cycles up to day 150 no negative values occur. But simultaneous with the rapidvertical displacement, the first negative values appear and increase constantly until a level ofroughly 15% is reached. The proportion of negative values is not constant over the seasonalcycles, it rather tracks the distinctive temperature changes.

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Chapter 7

Conclusions and Outlook

Conclusions. The aim of this study was to establish a geophysical methodology that couldassist in improving the understanding of bedrock permafrost processes. A particular objectivewas to assess the suitability of geoelectrical monitoring applied to different rock types and toexamine the geophysical response to changing conditions within permafrost affected rocks. Forthis purpose, theoretical aspects of permafrost, ERT and CRI were reviewed. A laboratoryexperiment was set up to physically model fracture formation in permafrost and seasonalfrost environments. Two different rock types were under investigation. These different rocktypes are a siliceous Tuffeau chalk with high porosity (30 - 40%) and a fine-grained dolomisedlimestone (porosity <5%); representing frost-susceptible lowland rocks and highly resistivemountain rocks, respectively. The measurement techniques and acquisition geometry variedbetween the samples, due to space restrictions and the available equipment. The experimentemulated 28 full seasonal temperature cycles from 20 April 2012 until 30 June 2014.

The conventional investigation methods (temperature, moisture and heave) showed mostlya high measurement quality, apart form the TDR measurements which became unreliableafter the 3rd seasonal cycle and could not be used for any further analysis. The temperaturemeasurements with the small sensor spacing and high measurement frequency produced highquality temperature-depth distributions. This made it possible to capture the bi-directionalityof the freezing front in the permafrost regime. Moreover, it was shown that the ERT andCRI data were able to track and resolve these temperature distributions.

The experimental results of using conventional ERT and multi-sensor CRI techniques showedsignificant changes in the resistivity for the frost-susceptible lowland rock TU and the highlyresistive mountain rock WS, where the WS has resistivities one order of magnitude higherthan those of TU. Additionally, significant changes in apparent resistivities of up to 850%and 600% for ERT and CRI, respectively, were found from thawed to frozen state. Theseresults suggest that both geoelectric measurement techniques are capable of monitoring spatialresistivity variations in the samples caused by deviations in temperature below 0C.

Moreover, commonality between the two measurement techniques was highlighted by directcomparison of the related apparent resistivities over multiple freeze and thaw cycles. The

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60 Conclusions and Outlook

temperature-resistivity relationship was strongly correlated between the CRI and ERT mea-surements, albeit with a varying offset for each quadrupole configuration.

Monitoring resistivity with ERT was found to be impaired by high contact resistances, possi-ble current leakage around the rock samples and the distortion of the current paths throughfracture development. The CRI measurements were limited by the lack of reciprocal measure-ments and poorer reciprocity compared to ERT, the broader apparent resistivity distribution,the varying phase angle of the complex signal and the sparse measurements. Furthermore,the CRI data quality was limited by practical restrictions of the prototype instrumentation.

Statistical analyses of the geoelectric measurements show a high correlation of the temperatureand the apparent resistivity, with similar behaviour for the two measurement techniques anddifferent rock types. It was found that the highly resistive mountain rock has a strongerincrease in resistivity than the fros-susceptible lowland rock.

Nevertheless, the overall results emphasise the feasibility to monitor permafrost processes andproperties in finite rock samples using a capacitively coupled system. Particular advantageswere observed in highly resistive environments, such as the highly resistive mountain rocksample.

Densely measured ERT apparent resistivities were used to derive resistivity-temperature cal-ibration curves for freeze and thaw periods. The results showed a clear deviation of thehysteresis curves for the two cycles, this suggests that different calibration curves for thetransformation of the resistivity models into temperature models are required for the differ-ent phase changes.

The findings of this study showed that CRI can be employed to obtain valuable resistivityand temperature data. In particular, additional capability can be achieved for highly resistivemountain rocks, where the application of ERT is demonstrably very difficult. Such informa-tion would be anticipated to add value to permafrost research and monitoring, potentiallyincreasing the information content for assessing risk in areas of degrading permafrost.

Outlook. Future work should focus on additional modelling to improve the understandingof the conceptual understanding of fracturing processes as well as practical improvementsof the CRI measurement techniques. Regarding the experimental setup for this study, ahigher measurement frequency and denser CRI sensor network should be applied to increasethe resolution of intended inversions, in particular during changes in temperature conditions.Furthermore, enhancements of data quality should be achieved by improving the current BGSresearch prototype. Hence, continuing of the system development is required to enable thesystem to acquire reciprocal measurements, contact impedance measurements and usage ofthe transmitter or receivers from the same transmitter/receiver card.

Further experiments should be done for improving the understanding of CRI performanceon different rock types. In particular, it is important to understand the influence of thedielectric constant on the real and imaginary part, and thus the phase angle of the complexsignal. In addition, more testing could be done to distinguish influences on the system fromthe surrounding environment, such as simultaneously running measurements, changes in thepower supply or measurement wiring.

Finally, the CRI system should be tested in field experiments in permafrost areas and compareit to conventionally measured resistivities and temperatures. In this way,, the combined

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61

experience from laboratory and field experiment will hopefully add valuable information tothe assessment of risk of degrading permafrost.

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62 Conclusions and Outlook

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Bibliography

Archie, G. (1942). The electrical resistivity log as an aid in determining some reservoircharacteristics. Petroleum Transactions of AIME.

Benderitter, Y., Jolivet, A., Mounir, A., and Tabbagh, A. (1994). Application of the elec-trostatic quadripole to sounding in the hectometric depth range. Journal of Applied Geo-physics, 31:1–6.

BGS (2012). Capacitive Resistivity Imaging, The Electronics. British Geological Survey.

Calvert, H. (2002). Capacitive-coupled resistivity survey of ice-bearing sediments, mackenziedelta, canada. In SEG Annual Meeting, 696-698, Salt Lake City, Utah.

Davies, M., Hamza, O., and Harris, C. (2001). The effect of rise in mean annual temper-ature on the stability of rock slopes containing ice-filled discontinuities. Permafrost andPericlacial Process, 12.

Fogiel, M., Research, and Association, E. (1976). The Physics Problem Solver. REA’S problemsolver. REA.

French, H. (2007). The periglacial environment. Third Edition. John Wiley and Sons Ltd.

Grard, R. (1990). A quadrupolar array for measuring the complex permittivitz of the ground:application to earth prospection and planetarz exploration. Measurement Science andTechnology, 1.

Grard, R. and Tabbagh, A. (1991). A mobile four-electrode array and its application to theelectrical survey of planetary grounds at shallow depth. Journal of Geophysical Research,96(2):4117–4123.

Haeberli, W. and Gruber, S. (2008). Mountain permafrost - research challenge for steepand cold terrain. Proceedings of the 9th International Conference on Permafrost 2008,Fairbanks, Alaska, USA, pages 597–605.

August 8, 2014

64 Bibliography

Harris, C., Arenson, L., Christiansen, H., Etzelmuller, H., Frauenfelder, R., Gruber, S., Hae-berli, W., Hauck, C., Holzle, M., Humlum, O., Isaksen, K., Kaab, A., Kern-Lutschg, M.,Lehning, M., Matsuoka, N., Murton, J., Notzli, J., Phillips, M., Ross, N., Seppala, M.,Springmann, S., and Muhll, D. V. (2009). Permafrost and climate in europe: Monitor-ing and modelling thermal, geomorphological and geotechnical responses. Earth-ScienceReviews, 92(3-4):117–177.

Hauck, C. (2002). Frozen ground monitoring using dc resistivity tomography. GeophysicalResearch Letters, 29(21):10–13.

Hauck, C. and Kneisel, C. (2006). Application of capacitively-coupled and dc electrical re-sistivity imaging for mountain permafrost studies. Permafrost and Periglacial Processes,17(2):169–177.

Hauck, C., Muhll, D. V., and Maurer, H. (2003). Using dc resistivity tomography to detectand characterize mountain permafrost. Geophysical Prospecting, 51(4):273–284.

Hoekstra, P., Sellmann, P., and Delaney, A. (1974). Airborne resistivity mapping of per-mafrost near fairbanks, alaska. Technical report, Cold Regions Research and EngineeringLaboratory, Hanover, New Hampshire.

Hoekstra, P., Sellmann, P., and Delaney, A. (1975). Ground and airborne resistivity surveysof permafrost near fairbanks, alaska. Geophysics, 40.

Keller, G. and Frischknecht, F. (1966). Electrical methods in geophysical prospecting. Perg-amon Press, Inc., Oxford.

King, M., Zimmerman, R., and Corwin, R. (1988). Seismic and electrical properties of un-consolidated permafrost. Geophysical Prospecting, 36:349–364.

Krautblatter, M. (2008). Rock permafrost geophysics and its explanatory power forpermafrost-induced rockfalls and rock creep: A perspective. paper presented at 9th In-ternational Conference on Permafrost, Fairbankds, Alaska.

Krautblatter, M., Funk, D., and Guenzel, F. (2012). Why permafrost rocks become unstable:A rock-ice-mechanical model in time and space. Earth Surface Processes and Landforms.

Krautblatter, M. and Hauck, C. (2007). Electrical resistivity tomography monitoring ofpermafrost in solid rock walls. Journal of Geophysical Research, 112(F2):1–14.

Krautblatter, M., Verleysdonk, S., Flores-Orozco, A., and Kemna, A. (2010). Temperature-calibrated imaging of seasonal changes in permafrost rock walls by quantitative electricalresistivity tomography (zugspitze, german/austrian alps). Journal of Geophysical Research,115(F2):1–15.

Kuras, O. (2002). The capacitive resistivity technique for electrical imaging of shallow sub-surface. PhD thesis, University of Nottingham.

Kuras, O., Beamish, D., Meldrum, P., and Ogilvy, R. (2006). Fundamentals of the capacitiveresistivity technique. Geophysics, 71(3):G135–G152.

August 8, 2014

Bibliography 65

Luthin, J. and Guymon, G. (1974). Soil moisture-vegetation-temperature relationship incentral alaska. Journal of Hydrology, 23:233–46.

McGinnis, L., Nakao, K., and Clark, C. (1974). Geophysical identification of frozen andunfrozen ground, antarctica. In 2nd International Conference on Permafrost, pages 136–145.

McNeill, J. (1980). Electromagnetic terrain conductivity measurement at low induction num-bers. Technical report, Geonic Limited.

Muller, S. (1943). Permafrost or permanently frozen ground and related engineering problems.U.S. Engineers Office, Intelligence Branch, Strategic Engineering Study Special Report 62.

Muller, S., French, H., Nelson, F., and of Civil Engineers. Technical Committee on ColdRegions Engineering, A. S. (2008). Frozen in Time: Permafrost and Engineering Problems.American Society of Civil Engineers.

Murton, J., Coutard, J.-P., Lautridou, J.-P., Ozouf, J.-C., Robinson, D., and Williams, R.(2001). Physical modelling of bedrock brecciation by ice segregation in permafrost. Per-mafrost and Periglacial Processes, 12(3):255–266.

Murton, J., Peterson, R., and Ozouf, J.-C. (2006a). Supporting online material for: Bedrockfracture by ice segregation in cold regions. Technical report, University of Sussex.

Murton, J. B., Coutard, J.-P., Lautridou, J.-P., Ozouf, J.-C., Robinson, D., Williams, R.,Guillemet, G., and Simmons, P. (2000). Experimental design for a pilot study on bedrockweathering near the permafrost table. Earth Surface Processes and Landforms, 25:1281–1294.

Murton, J. B., Peterson, R., and Ozouf, J.-C. (2006b). Bedrock fracture by ice segregationin cold regions. Science (New Yor, N.Y.), 314(5802):1127–9.

Parasnis, D. (1988). Reciprocity theorems in geoelectric and geoelectromagnetic work. Geo-exploration, 25.

Pascale, G. D., Pollard, G., and Williams, K. (2008). Geophysical mapping of ground ice usinga combination of capacitive coupled resistivity and ground-penetrating radar, northwestterritories, canada. Journal of Geophysical Research, 113(F2).

Reynolds, J. M. (1997). An Introduction to Applied and Environmental Geophysics. JohnWiley and Sons.

Scofield, J. H. (1994). A frequency-domain description of a lock-in amplifier. AmericanJournal of Physics, 62(2):129–133.

Slater, L., Binley, A., Daily, W., and Johnson, R. (2000). Cross-hole electrical imaging of acontrolled saline tracer injection. Journal Applied Geophysics, 44.

Timofeev, V. (1974). The employment of capacitively-coupled sensors in engineering andgeological studies (in Russian). PhD thesis, University of Moscow.

August 8, 2014

66 Bibliography

Topp, C., Davis, J., and Annan, A. (1980). Electromagnetic determination of soil watercontent: Measurements in coaxial transmission lines. Water Resources Research, 16(3):574–582.

Uhlemann, S. (2012). On the suitability of capacitive resistivity imaging (cri) for permafrostmonitoring. Master’s thesis, ETH Zrich, Institute of Geophysics and British GeologicalSurvey.

Uhlemann, S. and Kuras, O. (2014). Numerical simulations of capacitive resistivity imaging.Near Surface Geophysics, 12.

Wait, J. (1995). Comments on: ’application of the electrostatic quadripole to sounding in thehectometric depth range’, benderitter et al. Journal Geophysics, 34.

Walder, J. and Hallet, B. (1986). The physical basis of frost weathering: toward a morefundamental and unified perspective. Arctic and Alpine Research, 18.

Washburn, A. (1979). Geocryology. A Survey of Periglacial Processes and Environments.Edward Arnold, London.

Williams, P. J. and Smith., M. W. (1989). The Frozen Earth. Cambridge University Press.

Woo, M. (2012). Permafrost Hydrology. Springer.

Zonge, K. and Hughes, L. (1986). Effect of electrode contact resistance on electric fieldmeasurements. Geophysics, 51(2).

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Appendix A

Seasonal cycles

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68 Seasonal cycles

Table A-1: All annual cycles including duration, mean air temperatures and if ERT and CRI wasmeasured. (part 1)

Total Freezing period Thawing periodCycle Start Finish date Duration Duration Mean air SD Mean air SD ERT CRI

date date [days] [days] temp [C] [C] temp [C] [C]

1 20. Apr 12 02. Mai 12 12 12 -9.6 1.6 p f02. Mai 12 17. Mai 12 15 27 15.6 3.1 p f

2 17. Mai 12 07. Jun 12 21 48 -9.7 1 p f07. Jun 12 20. Jun 12 13 61 16.9 3.6 p p

3 20. Jun 12 30. Jul 12 40 101 -9.7 1 p p30. Jul 12 16. Nov 12 109 210 10.7 6.6 p p

4 16. Nov 12 23. Nov 12 7 217 -9.7 2 f f23. Nov 12 17. Dez 12 24 241 7.7 4.5 f f

5 17. Dez 12 21. Dez 12 4 245 -10 0.9 f f21. Dez 12 09. Jan 13 19 264 9.5 0.8 n f

6 09. Jan 13 22. Jan 13 13 277 -10.1 0.9 n p22. Jan 13 04. Feb 13 13 290 10.2 1.8 p p

7 04. Feb 13 12. Feb 13 8 298 -10 0.9 f p12. Feb 13 20. Feb 13 8 306 12 1.9 f f

8 20. Feb 13 26. Feb 13 6 312 -10 0.9 f f26. Feb 13 05. Mrz 13 7 319 10.4 1.9 f f

9 05. Mrz 13 11. Mrz 13 6 325 -9.8 1.1 f f11. Mrz 13 19. Mrz 13 8 333 11.7 1.2 f n

10 19. Mrz 13 26. Mrz 13 7 340 -9.9 1.1 n n26. Mrz 13 05. Apr 13 10 350 11.1 1.3 n n

11 05. Apr 13 10. Apr 13 5 355 -9.9 1 n n10. Apr 13 19. Apr 13 9 364 13 2.1 n p

12 19. Apr 13 30. Apr 13 11 375 -9.8 1 n p30. Apr 13 07. Mai 13 7 382 14.4 2.2 n n

13 07. Mai 13 15. Mai 13 8 390 -9.7 1.1 n n15. Mai 13 23. Mai 13 8 398 13.4 1.7 n n

14 23. Mai 13 30. Mai 13 7 405 -9.8 1.1 n n30. Mai 13 06. Jun 13 7 412 14 2.7 n n

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69

Table A-2: All annual cycles including duration, mean air temperatures and if ERT and CRI wasmeasured. (part 2)

Total Freezing period Thawing periodCycle Start Finish date Duration Duration Mean air SD Mean air SD ERT CRI

date date [days] [days] temp [C] [C] temp [C] [C]

15 06. Jun 13 13. Jun 13 7 419 -9.7 1.1 n n13. Jun 13 09. Jul 13 26 445 10.4 1.3 n n

16 09. Jul 13 17. Jul 13 8 453 -9.5 1.3 n n17. Jul 13 02. Aug 13 16 469 11.6 1.6 n n

17 02. Aug 13 15. Aug 13 13 482 -9.7 1.1 n n15. Aug 13 06. Sep 13 22 504 10.2 1.4 p n

18 06. Sep 13 17. Sep 13 15 519 -9.7 1.5 f n17. Sep 13 20. Sep 13 3 522 5.9 2.3 f n

19 20. Sep 13 24. Sep 13 4 526 -9.4 1.8 n n24. Sep 13 26. Sep 13 2 528 3.1 8.3 n n

20 26. Sep 13 01. Okt 13 5 533 -9.5 1.6 n n01. Okt 13 11. Okt 13 10 543 9.6 2.1 n n

21 11. Okt 13 15. Okt 13 4 547 -9.6 1.8 n n15. Okt 13 01. Nov 13 17 564 9.8 1.9 n n

22 01. Nov 13 11. Nov 13 10 574 -9.8 1.4 p n11. Nov 13 26. Nov 13 15 589 9.3 1.9 p p

23 26. Nov 13 04. Dez 13 8 597 -9.8 1.4 f n04. Dez 13 05. Jan 14 32 629 9.7 1.3 n n

24 05. Jan 14 17. Jan 14 12 641 -9.9 1 p n17. Jan 14 26. Feb 14 40 681 10.4 1.3 n p

25 26. Feb 14 05. Mrz 14 7 688 -10 0.9 n f05. Mrz 14 09. Apr 14 35 723 11 0.9 n f

26 09. Apr 14 11. Apr 14 2 725 -9.3 2.1 n f11. Apr 14 29. Apr 14 18 743 10.1 8.6 n f

27 29. Apr 14 07. Mai 14 8 751 -9.8 1.3 n f07. Mai 14 05. Jun 14 29 780 11.7 2.3 p f

28 05. Jun 14 16. Jun 14 11 791 -9.6 1.2 f f16. Jun 14 f p

mean cycle duration 24±26 days -9.75 1.1 10.4 1.9 p p

Freezing period begins when first air temperature probe falls below 0C, mean duration of 8±7 days none(n),full(f)Thawing period begins when first air temperature probe rises above 0C, mean duration of 15±20 days partly(p)

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70 Seasonal cycles

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Appendix B

Additional plots

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72 Additional plots

B-1 Ground coupling

(a) B1-TU: Permafrost

(b) B4-TU: Seasonal frost

Figure B-1: ERT contact resistance for B1 and B4. On the right, the rock sample are the thawedstate, where the left side represents the frozen state.

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B-2 Reciprocity 73

B-2 Reciprocity

(a) B1-TU: Permafrost in thawed state.R2 = 0.99, lin. regression y = 1.01x

(b) B1-TU: Permafrost in frozen state.R2 = 0.99, lin. regression y = 0.99x

(c) B2-WS: Permafrost in thawed state.R2 = 0.85, lin. regression y = 1.04x

(d) B2-WS: Permafrost in frozen state.R2 = 0.73, lin. regression y = 0.73x

(e) B4-TU: Seasonal frost in thawed state.R2 = 0.99, lin. regression y = 1.01x

(f) B4-TU: Seasonal frost in frozen state.R2 = 0.99, lin. regression y = 0.99x

Figure B-2: ERT: Cross-plot forward versus reciprocal. a),c) and e) represent data sets measuredat the thawed samples. b), d) and f) were measured at the frozen samples. Notethe different axes.

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74 Additional plots

B-3 Effect of equipment swap

Figure B-3: ERT-B1-TU: Permafrost

Figure B-4: Comparison of GeoTom change from GeoTom100 to GeoTom200

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B-4 Apparent resistivity as a function of rock type and sample condition 75

B-4 Apparent resistivity as a function of rock type and samplecondition

(a) ERT-B1-TU: Permafrost

(b) ERT-B2-WS: Permafrost

(c) ERT-B4-TU: Seasonal frost

Figure B-5: Distribution of the ERT apparent resistivity for B1, B2 and B4 in frozen (blue) andthawed (red) thermal states. Note the different axes.

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76 Additional plots

B-5 Electrode polarisation

Figure B-6: X-plot initial versus new ERT measurement sequence. Blue pluses represent individ-ual electrode configuration, the red line is the linear regression with R2 = 0.99 andan systematic increase of the resistivity data from initial to new sorting of roughly30%. Grey bars indicate the error of normal and reciprocal measurements. Thesmall plot in the left corner highlights the negative values.

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B-6 ERT current injection frequency 77

B-6 ERT current injection frequency

(a) X-plot 25 Hz vs. 1Hz in thaw state. (b) X-plot 25 Hz vs. 1Hz in frozen state.

Figure B-7: Blue pluses represent individual electrode configuration, the red line is the linearregression with R2 = 0.98 and R2 = 0.94 for unfrozen and frozen states, respec-tively. A systematic increase of the frozen resistivity data of approximately 13% isdetectable. Grey bars indicate the error of normal and reciprocal measurements.The small plot in the left corner is to highlight the negative values.

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78 Additional plots

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