# Induced polarization applied to landslides. Part 2: anatomy and water content tomography of a mudflow

André Revil, T Johnson, G Ménard, Z Su, S Qiang, A Ghorbani, P-A

Duvillard, G Martin, M Abdelfattah, J B Nicaise, et al.

► To cite this version:

André Revil, T Johnson, G Ménard, Z Su, S Qiang, et al. Induced polarization applied to landslides. Part 2: anatomy and water content tomography of a mudflow. Geophysical Journal International, 2025, 242, 10.1093/gji/ggaf219. hal-05298124

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# Induced polarization applied to landslides. Part 2: anatomy and water content tomography of a mudflow

C. Casotti,<sup>1,2</sup> A. Revil,<sup>2</sup> T. Johnson,<sup>3</sup> G. Ménard,<sup>2</sup> Z. Su,<sup>4</sup> S. Qiang,<sup>5</sup> A. Ghorbani,<sup>6</sup> P.-A. Duvillard,<sup>6</sup> G. Martin,<sup>1,7</sup> M. Abdelfattah,<sup>2,8</sup> J.B. Nicaise,<sup>9</sup> T. Houtteville<sup>1</sup> and J.M. Côme<sup>1</sup>

Accepted 2025 June 12. Received 2025 May 13; in original form 2025 March 1

## SUMMARY

Induced polarization is a geophysical method that can be applied to determine the water content and CEC (Cation Exchange Capacity) of sediments and rocks. We apply here this technique to image the Harmalière landslide in Isère (East of France). This landslide includes a mudflow of glacio-lacustrine silty clays overlying the Toarcian clayrock formation with local occurrences of compact pebbles from a paleochannel of the Drac river. A petrophysical study is used to characterize the properties of the four main lithofacies occurring in the area with a total of 22 samples. We performed complex conductivity measurements in the frequency range 10 mHz–45 kHz at different salinities (NaCl brines and *in situ* pore waters). We also measured the porosity and CEC of the samples. We calibrate the relationships between surface conductivity, quadrature conductivity and normalized chargeability with both the porosity and CEC. The relationship between the formation factor and the porosity conforms to Archie’s law with a cementation exponent close to 2.0 ± 0.2. In the field, we performed a time-domain induced polarization survey using a 1.26 km long cable (including roll along of the electrodes) with an electrode spacing of 20 m. The landslide is imaged down to a depth of 220 m. The inversion of the data (788 electrodes, 15.7 km of profiling, 13 012 apparent resistivity and 5539 apparent chargeability data) is done with the least-square technique penalizing the roughness of the tomograms using Occam inversion. The resulting 3-D electrical conductivity and normalized chargeability tomograms are analysed in conjunction with the petrophysical data to image the extension of the lithofacies at the field scale. Furthermore, the water content and CEC of the formations are imaged. We demonstrate that the compacted pebbles of the Drac paleochannel form both a mechanical and hydraulic barrier that is locally breached by the mudflow before entering the Monteynard–Avignonet Lake. This study demonstrates the ability of induced polarization to finely characterize the anatomy of such landslide and image its water content.

**Key words:** Electrical properties; Permeability and porosity; Electrical resistivity tomography (ERT); Induced polarization; Hydrogeophysics.

## 1 INTRODUCTION

The role of water and clay minerals in shallow landslides is of paramount importance to explain their occurrence and dynamics including in terms of water pressure and rheology (e.g. Antoine

Intended for publication in Geophysical Journal International, Special issue on induced polarization

<footnote n="1"><sup>1</sup>GINGER CEBTP, 78990 Élancourt, France</footnote>

<footnote n="2"><sup>2</sup>Université Savoie Mont-Blanc, EDYTEM, CNRS, UMR CN5204, 73376 Le Bourget du Lac, France. E-mail: andre.revil@univ-smb.fr</footnote>

<footnote n="3"><sup>3</sup>Pacific Northwest National Laboratory, 3335 Innovation Blvd, Richland, WA 99354, USA</footnote>

<footnote n="4"><sup>4</sup>College of Safety and Engineering, Anhui University of Science and Technology, Huainan 232001, China</footnote>

<footnote n="5"><sup>5</sup>Key Laboratory of Surficial Geochemistry of Ministry of Education, School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China</footnote>

<footnote n="6"><sup>6</sup>Naga Geophysics, 73000 Chambéry, France</footnote>

<footnote n="7"><sup>7</sup>GeoTopia, 92150 Suresnes, France</footnote>

<footnote n="8"><sup>8</sup>Geology Department, Faculty of Science, Port Said University, Port Said 42522, Egypt</footnote>

<footnote n="9"><sup>9</sup>ONF et RTM, 38000 Grenoble, France</footnote>
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Test site located 1 km southeast of Sinard village in Isère, France. (a) Overview of the Harmalière landslide. The light plain line represents the 1.26 km² area of destabilized terrain, known as the Harmalière landslide with the mudflow clearly visible. The area is covered by pine forests and pastoral landscapes. Nearby depressions, such as the Champ du Mouton and Avignonet depressions (North–East and South–West, respectively), also show signs of destabilization. Although their movements are slower, their landslide contexts are very similar to that of Harmalière (Credit Google Earth, © 2025 Airbus, Landsat/Copernicus). (b) Close-up view of one of the main detachment niches. This area has shown significant activity over the past decade, with a major event in 2016. The landslide here exhibits a solid block-tilting structure (Credit C. Casotti). (c) Close-up view of the lower flow zone at the downstream end of the landslide, where it terminates in the artificial Monteynard–Avignonet lake associated with a dam. In this area, pebbles (from a paleochannel of the Drac River) and Toarcian clay-rock outcrop (Credit C. Casotti).</figcaption></figure>

et al. 1981; Giraud et al. 1991; Gallipoli et al. 2000). This is especially important in the context of climate change and the increased occurrence of severe summer storms (Gariano & Guzzetti 2016).

In the previous paper of this series, we have demonstrated how a non-intrusive geophysical technique named induced polarization can be used to image the water content, cation exchange capacity, and permeability of shallow sedimentary formations. We were especially interested by the detection of permeability barriers (seals) that may be responsible for hydraulic pressures increase associated with strong rain events and eventually to gravitational instabilities. Induced polarization is a non-invasive geophysical technique representing an extension of the classical electrical resistivity imaging technique by including the study of the ability of rocks and sediments to reversibly store electrical charges under the action of an applied electrical field (Olhoeft 1981a, b; Flores Orozco et al. 2013). This ability is characterized by a property named normalized chargeability that describes such capacitive electrodiffusive coupling (polarizability) at the grain scale (Lesmes & Frye 2001). While electrical resistivity tomography has become a popular method to study landslides over the last 25 yr (e.g. Schmutz et al. 2000; Lapenna et al. 2003, 2005; Schmutz et al. 2009), more recent works have shown the relevance of induced polarization to landslides (Marescot et al. 2008; Revil et al. 2020; Flores Orozco

et al. 2022). That said, to go further, we need to demonstrate the quantitative applicability of this method to the field in order to derive quantitative hydrological properties of interest.

In the case study investigated in the first paper of this series and corresponding to the landslide of Les Garins in the Bauges area (Eastern France, see Casotti et al. 2025), we imaged the hydraulic properties of the formations down to a depth of 220 m. In turn, it was shown how the hydraulic properties of the subsurface can explain the occurrence of landslides. In this paper, we want to characterize mudflows with the induced polarization method. We are especially interested in being able to image their water content and cation exchange capacity (CEC), both being able to control the rheology of clayey sediments (Locat 1997; Bardou et al. 2007).

To illustrate our approach, we focus our work on the Harmalière mudflow located in the Trièves area (Isère, Western French Alps, France). In this area, glacio-lacustrine clays are known to favour gravitational instabilities occurring over paleotopographical features and many landslides and mudflow occur in this region (e.g. Giraud et al. 1991, and references therein). Observed landslides range from rotational slides to mudflows once the glacio-lacustrine sediments have lost their mechanical cohesion. The Harmalière landslide involves thick Quaternary deposits, up to 200 m deep on the Sinard plateau, composed mainly of glacio-lacustrine clayey
<figure><img src="image_3.png" /></figure>

**Figure 2.** Cross-section of the Harmalière landslide area, adapted from Letourneur & Michel (1971). The remobilized materials mainly consist of laminated clays deposited in the glacial paleo-lake of Trièves. The pebbles correspond to alluvial deposits. During glaciations, the Drac river repeatedly carved new channels at different locations. This history has left paleo-channels, one of which intersects the Harmalière landslide. These channels, and the current Drac river channel, are bordered or filled with pebbles. Downstream of the landslide, the marl-limestone bedrock of the Lower Jurassic Toarcian period is exposed.

materials lying on a bedrock of low-porosity Toarcian and Aalenian clayrocks. The glacio-lacustrine clayey formation contains also some paleochannels of the Drac River with compact pebbles.

Our goal in this paper is to use induced polarization to image the water content of the Harmalière mudflow. In the last decades, the Harmalière mudflow has shown a slow evolution punctuated by phases of sudden accelerations. The Harmalière landslide itself can be divided into two distinct areas. The upfront is characterized by rotational slides of large blocks along listric faults. Below the landslide niches (~30 m in height), a wide depression marks the beginning of the mudflow characterized by significant water content and disturbed forest cover. Once the glacio-lacustrine sediments have lost their cohesion, the landslide becomes a mudflow creeping down into a long, deeply gullied and muddy flow path ending into the Monteynard-Avignonet Lake. Our goal is to use induced polarization to perform a tomogram of this landslide and image its properties. Our goal is to show if 3-D large-scale induced polarization tomography is doable in this situation and how petrophysical data may improve the interpretation of the field data. Our end-goal is to better understand the anatomy of this mudflow in terms of water content and CEC distribution, two ingredients to better understand in turn the rheology of these materials.

## 2 BACKGROUND

The active Harmalière landslide is located in Isère, in the Trièves region (Western French Alps), approximately 40 km south of Grenoble (see Blanchet 1988; Fig. 1). During the last Würm glaciations (−12 000 à −70 000 ya), the Romanche glaciers moved repeatedly South down to the present location of the Sinard village (Coutterand 2010). The glaciers repeatedly dammed the Drac River forming glacial lakes in their vicinity leading in turn to significant sedimentary deposits reaching up to 200–400 m in thickness. These

sediments are laminated silty clay materials with a low cohesion (Blanchet 1988). They are prone to instability when slopes exceed 6° to 8° and their fine fraction (<2 µm) is predominantly composed of illite and a minor fraction of smectite. This is why approximately 15 per cent of the Trièves area is prone to landsliding (Jongmans et al. 2009).

Successive glaciations periodically filled the Drac river channel with alluvial deposits, forcing the river to incise new channels through both the unconsolidated sediments and the underlying Toarcian and Aalenian marl-limestone bedrock at the end of each Würm glaciation. The study area contains one of this paleochannel filled with alluvial deposits, composed of pebbles, gravels and sands, which are locally more or less cemented (Fig. 2). The area affected by the landslide corresponds to an uninhabited valley (named Harmalière) largely covered by forests including ‘drunken forests’ with trees sliding down with the sediments. The progression of the landslide through backward erosion in the direction of the village of Sinard (Figs 1 and 2) raises questions about the evolution of the risk in this area. This phenomenon is identical to that of the nearby Mas d’Avignonet and Champ du Mouton landslides affecting two neighbouring valleys located immediately to the North-East and South-West parts of the investigated area (Fig. 1), respectively.

Like similar landslides, the Harmalière landslide exhibits two distinct behaviors. In its upper part, decameter-sized blocks of the glacio-lacustrine quaternary sediments slide along listric slip surfaces (Fig. 3). Then, these blocks crumble few hundred meters below the head scarp. The dislocation of these blocks through the rotational sliding allows the porosity to increase (the cation exchange capacity remains probably the same). Then, the remolded very porous clay material creeps toward the Monteynard-Avignonet lake. Such resulting mudflow is highly visible on aerial pictures and satellite data (Fig. 1).
<figure><img src="image_4.png" /><figcaption>**Figure 3.** Geomorphological map of the Harmalière landslide with the position of the geophysical profiles. (a) Map showing sampling locations for laboratory spectral induced polarization (SIP) and CEC tests. The HC samples represent remobilized clayey silt from the landslide, except for HC4, which is an intact sample of laminated clay. HT samples correspond to Toarcian formation, while the HA sample represent the pebbles. The boundary of the DEM is indicated by a black dashed line. It covers an area of ~1.465 km². (b) Typical 'drunken forest' effect observed on the site, where much of the landslide-affected area disrupts forest zones, causing noticeable tree deformation due to gravitational instabilities. (c) Horst and graben structures, characteristic of the rotational sliding behaviour of decametric blocks along listric faults. The picture shows a fresh outcrop of laminated glacio-lacustrine clays displaying a brittle fracture pattern.</figcaption></figure>

The Harmalière landslide is currently very active with major collapses and sliding events occurring in different places over the past few years. For instance, in March 1981 and June 2016, major retrogression events were reported by Fiolleau *et al.* (2021). Over the last 70 yr, the headscarp has moved ~700 m in the direction of the Sinard village (see position in Fig. 2) with a total mass transfer of ~6 × 10⁶ m³ of glacio-lacustrine sediments toward the Monteynard-Avignonet lake. Monitoring this landslide is becoming therefore a priority.

# 3 PETROPHYSICS OF INDUCED POLARIZATION

## 3.1. Basics

Electrical resistivity tomography consists in imaging the electrical resistivity/conductivity of the subsurface materials using a resistivity-meter (e.g. Loke *et al.* 2013; Zhou 2018). It is a galvanometric technique requiring electrodes for the injection/retrieval of an electrical current in the ground on a pair of AB electrodes. The resulting voltage differences are measured on pairs of electrodes MN (Lapenna *et al.* 2003, 2005; Loke *et al.* 2013; Perrone *et al.* 2014). In terms of petrophysics, the electrical conductivity of

a water-saturated rock or sediment has two contributions. One is associated with the saturated pore network since the pore water is conductive with a conductivity depending on its ionic strength or TDS (total dissolved solid) of the pore water and temperature. The second contribution is associated with the electrical double layer coating the surface of minerals and especially coating clay minerals (Waxman & Smits 1968; Clavier *et al.* 1977; Ruffet *et al.* 1995). It is called surface (or interfacial) conductivity and depends on the CEC of the minerals and temperature (see Waxman & Smits 1968; De Lima & Sharma 1990).

A rock or sediment does not behave just like a resistor. It can also reversibly store electrical charges when submitted to an electrical current at low frequencies (below 10 kHz, see Olhoeff 1981a, b). The low-frequency electrical properties of a sediment can be therefore described by an equivalent circuit formed of a combination of resistors and capacitors (e.g. Vinegar & Waxman 1984). In absence of metallic particles, the ability to store electrical charges under an imposed electrical field or current is associated with the electrical double layer coating the surface of the clay minerals (Olhoeff 1981a, b; Vinegar & Waxman 1984; Revil 2012; Revil *et al.* 2017). This double layer is formed by the Stern and diffuse layers forming respectively the inner and outer parts of the electrical double layer (Chapman 1913; Grosse 2002). At the scale
<table><caption>**Table 1.** Petrophysical properties of the 22 samples used in this study. The CEC is expressed in meq/100 g (1 meq/100 g = 963.20 C kg⁻¹ in SI units) and is obtained using the cobalthexamine method, which is a classical titration method. The surface conductivities and formation factors are determined by plotting, for each sample, the (in-phase) conductivity of the material (at a frequency of 1 Hz) as a function of the conductivity of the pore water (in a log/log space). The main clay mineral is illite (Fiolleau 2020).</caption><thead><tr><th>Full name</th><th>Lithology</th><th>Porosityφ (−)</th><th>Grain densityρ<sub>g</sub> (g cm<sup>-3</sup>)</th><th>CEC/MEq/100g</th><th>F(−)</th><th>σ<sub>s</sub>(10<sup>-3</sup> S m<sup>-1</sup>)</th></tr></thead><tbody><tr><td>HCl (1)</td><td>Lacustrine clay</td><td>0.78</td><td>2.73</td><td>9.1</td><td>1.56</td><td>13.0</td></tr><tr><td>HC2 (1)</td><td>Lacustrine clay</td><td>0.79</td><td>2.73</td><td>11.1</td><td>1.42</td><td>7.69</td></tr><tr><td>HC3 (1)</td><td>Lacustrine clay</td><td>0.81</td><td>2.73</td><td>10.7</td><td>1.62</td><td>3.24</td></tr><tr><td>HL5 (2)</td><td>Lacustrine clay</td><td>0.51</td><td>2.73</td><td>11.7</td><td>2.58</td><td>42.9</td></tr><tr><td>HTA (3)</td><td>Toarcian clayrock</td><td>0.09</td><td>2.73</td><td>5.1</td><td>41.7</td><td>15.9</td></tr><tr><td>HTB (3)</td><td>Toarcian clayrock</td><td>0.10</td><td>2.73</td><td>5.8</td><td>79.2</td><td>5.62</td></tr><tr><td>HA (4)</td><td>Pebbles</td><td>0.28</td><td>2.65</td><td>4.2 (6)</td><td>9.2</td><td>13.2</td></tr><tr><td>T01 (5)</td><td>Toarcian clayrock</td><td>0.10</td><td>2.86</td><td>8.6</td><td>149.6</td><td>25.7</td></tr><tr><td>T02 (5)</td><td>Toarcian clayrock</td><td>0.12</td><td>2.89</td><td>8.6</td><td>41.6</td><td>17.6</td></tr><tr><td>T04 (5)</td><td>Toarcian clayrock</td><td>0.11</td><td>2.86</td><td>8.6</td><td>179</td><td>25.1</td></tr><tr><td>T05 (5)</td><td>Toarcian clayrock</td><td>0.08</td><td>2.74</td><td>8.6</td><td>119</td><td>22.1</td></tr><tr><td>T06 (5)</td><td>Toarcian clayrock</td><td>0.05</td><td>2.73</td><td>8.6</td><td>118</td><td>20.1</td></tr><tr><td>T07 (5)</td><td>Toarcian clayrock</td><td>0.10</td><td>2.71</td><td>8.6</td><td>152</td><td>23.1</td></tr><tr><td>T08 (5)</td><td>Toarcian clayrock</td><td>0.10</td><td>2.78</td><td>8.6</td><td>142</td><td>25.9</td></tr><tr><td>T09 (5)</td><td>Toarcian clayrock</td><td>0.11</td><td>2.78</td><td>10.7</td><td>64</td><td>23.0</td></tr><tr><td>T11 (5)</td><td>Toarcian clayrock</td><td>0.11</td><td>2.82</td><td>8.6</td><td>112</td><td>22.8</td></tr><tr><td>T12 (5)</td><td>Toarcian clayrock</td><td>0.10</td><td>2.84</td><td>8.6</td><td>131</td><td>25.4</td></tr><tr><td>T13 (5)</td><td>Toarcian clayrock</td><td>0.10</td><td>2.77</td><td>8.6</td><td>104</td><td>21.1</td></tr><tr><td>T14 (5)</td><td>Toarcian clayrock</td><td>0.10</td><td>2.64</td><td>8.6</td><td>94</td><td>23.9</td></tr><tr><td>T16 (5)</td><td>Toarcian clayrock</td><td>0.11</td><td>2.71</td><td>8.6</td><td>75</td><td>22.8</td></tr><tr><td>T17 (5)</td><td>Toarcian clayrock</td><td>0.10</td><td>2.78</td><td>8.6</td><td>59</td><td>21.1</td></tr><tr><td>T18 (5)</td><td>Toarcian clayrock</td><td>0.09</td><td>2.84</td><td>10.3</td><td>131</td><td>22.7</td></tr></tbody></table>

(1) Materials from the mudflow, suspension of glacio-lacustrine clays.

(2) Pristine glacio-lacustrine silty clay.

(3) Toarcian formation: HTA and HTB represent groups of three subsamples each, derived from a single parent sample (two parent samples produced a total of six subsamples). The presented data correspond to the average values of the three subsamples within each group.

(4) Alluvials deposits.

(5) Toarcian clayrock from Tournemire (Aveyron, France).

(6) CEC of the fine fraction collected in between the pebbles.
<table><caption>**Table 2.** Petrophysical properties of the 22 samples used in this study. In-phase conductivity, quadrature conductivity and normalized chargeability values at 25 °C. The normalized chargeability $M_n$ is computed from the difference of the in-phase conductivity between 10 mHz and 10 kHz. $\sigma_w$ represents the pore water conductivity (NaCl, here reported at 25 °C).</caption><thead><tr><th>Full name</th><th>σ' 10 mHz<br>(10<sup>-3</sup> S m<sup>-1</sup>)</th><th>σ' 10 kHz<br>(10<sup>-3</sup> S m<sup>-1</sup>)</th><th>M<sub>n</sub><br>(10<sup>-3</sup> S m<sup>-1</sup>)</th><th>σ' 32 Hz<br>(10<sup>-3</sup> S m<sup>-1</sup>)</th><th>σ' 32 Hz<br>(10<sup>-5</sup> S m<sup>-1</sup>)</th><th>σ<sub>w</sub> (25 °C)<br>(S m<sup>-1</sup>)</th></tr></thead><tbody><tr><td>HC1</td><td>195.1</td><td>198.1</td><td>3.05</td><td>197</td><td>52.7</td><td>0.271</td></tr><tr><td>HC2</td><td>80.4</td><td>81.2</td><td>0.83</td><td>79.2</td><td>32.8</td><td>0.13</td></tr><tr><td>HC3</td><td>62.9</td><td>65.3</td><td>2.4</td><td>62.8</td><td>29.32</td><td>0.094</td></tr><tr><td>HL5</td><td>66.1</td><td>74.4</td><td>6.7</td><td>72.0</td><td>46.2</td><td>0.071</td></tr><tr><td>HT1</td><td>15.9</td><td>40</td><td>24</td><td>20.6</td><td>265.1</td><td>0.093</td></tr><tr><td>HT2</td><td>14.3</td><td>39.2</td><td>25</td><td>19.3</td><td>280.1</td><td>0.093</td></tr><tr><td>HT4</td><td>5.06</td><td>9</td><td>3.95</td><td>5.71</td><td>36.51</td><td>0.093</td></tr><tr><td>HT5</td><td>7.24</td><td>13</td><td>5.74</td><td>8.63</td><td>45.72</td><td>0.093</td></tr><tr><td>HT6</td><td>6.9</td><td>15.4</td><td>8.5</td><td>8.55</td><td>87.76</td><td>0.093</td></tr><tr><td>HT7</td><td>17.3</td><td>33.1</td><td>15.9</td><td>20.3</td><td>130.6</td><td>0.093</td></tr><tr><td>HTA (1)</td><td>15.8</td><td>37.4</td><td>21.6</td><td>20.1</td><td>225.3</td><td>0.093</td></tr><tr><td>HTB (1)</td><td>6.4</td><td>12.5</td><td>6.06</td><td>7.63</td><td>56.66</td><td>0.093</td></tr><tr><td>HA</td><td>19.7</td><td>20.1</td><td>0.423</td><td>19.9</td><td>3.24</td><td>0.066</td></tr><tr><td>T01</td><td>26.2</td><td>28.1</td><td>1.9</td><td>26.9</td><td>18</td><td>0.22</td></tr><tr><td>T02</td><td>24.7</td><td>26.3</td><td>1.6</td><td>25.1</td><td>18.7</td><td>0.22</td></tr><tr><td>T04</td><td>25.7</td><td>28</td><td>2.3</td><td>26.7</td><td>18</td><td>0.22</td></tr><tr><td>T05</td><td>23.4</td><td>24.9</td><td>1.5</td><td>23.9</td><td>14.7</td><td>0.22</td></tr><tr><td>T06</td><td>21.7</td><td>23.4</td><td>1.6</td><td>22.3</td><td>15</td><td>0.22</td></tr><tr><td>T07</td><td>24.1</td><td>25.7</td><td>1.6</td><td>24.6</td><td>15.2</td><td>0.22</td></tr><tr><td>T08</td><td>26.7</td><td>28.3</td><td>1.6</td><td>27.1</td><td>18.2</td><td>0.22</td></tr><tr><td>T09</td><td>27.5</td><td>29</td><td>1.5</td><td>27.8</td><td>17.4</td><td>0.22</td></tr><tr><td>T11</td><td>25.4</td><td>26.9</td><td>1.5</td><td>25.8</td><td>16.2</td><td>0.22</td></tr><tr><td>T12</td><td>26.6</td><td>28.5</td><td>1.9</td><td>27.3</td><td>18.6</td><td>0.22</td></tr><tr><td>T13</td><td>23.9</td><td>25.3</td><td>1.4</td><td>24.2</td><td>16</td><td>0.22</td></tr><tr><td>T14</td><td>25.9</td><td>27.8</td><td>2</td><td>26.6</td><td>18.1</td><td>0.22</td></tr><tr><td>T16</td><td>25.7</td><td>27.3</td><td>1.6</td><td>26.1</td><td>18.1</td><td>0.22</td></tr><tr><td>T17</td><td>26</td><td>27.7</td><td>1.8</td><td>26.5</td><td>16.5</td><td>0.22</td></tr><tr><td>T18</td><td>24.1</td><td>25.9</td><td>1.8</td><td>24.7</td><td>16.4</td><td>0.22</td></tr></tbody></table>

(1) HTA groups (subsamples T1, T2 and T7). HTB groups (subsamples T4, T5 and T6).

<table><caption>**Table 3.** Petrophysical properties of the 22 samples used in this study. In-phase conductivity, quadrature conductivity and normalized chargeability values at 25 °C. The normalized chargeability $M_n$ is computed from the difference of the in-phase conductivity between 1 Hz and 1 kHz.</caption><thead><tr><th>Full name</th><th>σ' 1 Hz<br>(10<sup>-3</sup> S m<sup>-1</sup>)</th><th>σ' 1 kHz<br>(10<sup>-3</sup> S m<sup>-1</sup>)</th><th>M<sub>n</sub><br>(10<sup>-3</sup> S m<sup>-1</sup>)</th><th>σ' 32 Hz<br>(10<sup>-3</sup> S m<sup>-1</sup>)</th><th>σ' 32 Hz<br>(10<sup>-5</sup> S m<sup>-1</sup>)</th></tr></thead><tbody><tr><td>HC1</td><td>195.5</td><td>197.2</td><td>1.69</td><td>197</td><td>52.7</td></tr><tr><td>HC2</td><td>78.9</td><td>79.4</td><td>0.43</td><td>79.2</td><td>32.8</td></tr><tr><td>HC3</td><td>62.6</td><td>63</td><td>0.43</td><td>62.8</td><td>29.32</td></tr><tr><td>HL5</td><td>70.1</td><td>72.0</td><td>2.90</td><td>72.0</td><td>46.2</td></tr><tr><td>HT1</td><td>17.1</td><td>22.8</td><td>5.65</td><td>20.6</td><td>265.1</td></tr><tr><td>HT2</td><td>15.6</td><td>21.6</td><td>6.04</td><td>19.3</td><td>280.1</td></tr><tr><td>HT4</td><td>5.27</td><td>6.01</td><td>0.74</td><td>5.71</td><td>36.51</td></tr><tr><td>HT5</td><td>8.04</td><td>9</td><td>0.96</td><td>8.63</td><td>45.72</td></tr><tr><td>HT6</td><td>7.44</td><td>9.27</td><td>1.83</td><td>8.55</td><td>87.76</td></tr><tr><td>HT7</td><td>18.7</td><td>21.4</td><td>2.74</td><td>20.3</td><td>130.6</td></tr><tr><td>HTA (1)</td><td>17.1</td><td>21.9</td><td>4.81</td><td>20.1</td><td>225.3</td></tr><tr><td>HTB (1)</td><td>6.91</td><td>8.09</td><td>1.18</td><td>7.63</td><td>56.66</td></tr><tr><td>HA</td><td>19.83</td><td>19.91</td><td>0.07</td><td>19.89</td><td>3.24</td></tr><tr><td>T01</td><td>26.6</td><td>27.5</td><td>0.9</td><td>26.9</td><td>18</td></tr><tr><td>T02</td><td>24.8</td><td>25.7</td><td>0.8</td><td>25.1</td><td>18.7</td></tr><tr><td>T04</td><td>26.3</td><td>27.3</td><td>1</td><td>26.7</td><td>18</td></tr><tr><td>T05</td><td>23.6</td><td>24.4</td><td>0.7</td><td>23.9</td><td>14.7</td></tr><tr><td>T06</td><td>22.1</td><td>22.8</td><td>0.7</td><td>22.3</td><td>15</td></tr><tr><td>T07</td><td>24.3</td><td>25.1</td><td>0.8</td><td>24.6</td><td>15.2</td></tr><tr><td>T08</td><td>26.8</td><td>27.7</td><td>0.8</td><td>27.1</td><td>18.2</td></tr><tr><td>T09</td><td>27.6</td><td>28.4</td><td>0.8</td><td>27.8</td><td>17.4</td></tr><tr><td>T11</td><td>25.5</td><td>26.3</td><td>0.8</td><td>25.8</td><td>16.2</td></tr><tr><td>T12</td><td>26.9</td><td>27.9</td><td>0.9</td><td>27.3</td><td>18.6</td></tr><tr><td>T13</td><td>24</td><td>24.7</td><td>0.7</td><td>24.2</td><td>16</td></tr><tr><td>T14</td><td>26.2</td><td>27.2</td><td>0.9</td><td>26.6</td><td>18.1</td></tr><tr><td>T16</td><td>25.8</td><td>26.7</td><td>0.8</td><td>26.1</td><td>18.1</td></tr><tr><td>T17</td><td>26.2</td><td>27</td><td>0.8</td><td>26.5</td><td>16.5</td></tr><tr><td>T18</td><td>24.4</td><td>25.2</td><td>0.8</td><td>24.7</td><td>16.4</td></tr></tbody></table>

(1) HTA groups (subsamples T1, T2 and T7). HTB groups (subsamples T4, T5 and T6).
<figure><img src="image_5.png" /><figcaption>**Figure 4.** Picture of the various samples collected on-site. The sampling includes the low-porosity Toarcian (HT) samples, undisturbed laminated (glaciolacustrine) silty clays at the top-center (HC2) and remobilized (glaciolacustrine) silty clays clay at the bottom-right. The pebbles (HA) is from an outcrop of the Drac paleochannel (outcropping at the bottom of the landslide).</figcaption></figure>

of a representative elementary volume, the ability of a sediment to reversibly store electrical charges is quantified by its normalized chargeability (corresponding to the frequency-dispersion of the electrical conductivity) or the imaginary part of the complex conductivity (Keller 1959; Lesmes & Frye 2001).

In this paper, we are interested by the induced polarization response of clay-rich sediments including consolidated (low-porosity) clay-rocks (Toarcian) and unconsolidated (high porosity) glaciolacustrine silty clays. The understanding of the underlying physics of the induced polarization response of such clay-rich materials is of paramount importance and is currently a subject of intensive research (see El Alam et al. 2023, 2024; Leroy et al. 2024; Maineult et al. 2025, and references therein).

## 3.2. Fundamental petrophysical relationships

The underlying physics of induced polarization is discussed in more details in the companion paper (Casotti et al. 2025, see also Revil 2012, 2013) and will not be repeated here. Only some key petrophysical relationships are recalled and provided below because required to calibrate the petrophysical model. The complex conductivity is written as $\sigma^*(\omega) = \sigma'(\omega) - i\sigma''(\omega)$ where $\sigma'(\omega)$ and $\sigma''(\omega)$ denotes the in-phase and quadrature conductivities, respectively, and where $\omega$ denotes the angular frequency (rad s$^{-1}$). The three relationships of interest are the expressions of the conductivity, the normalized chargeability, and the relationship between the normalized

chargeability and the quadrature conductivity (see Revil 2013; Revil et al. 2017; Moser et al. 2025):

$$ \sigma' \approx \frac{1}{F}\sigma_w + \frac{1}{F\phi}B\rho_g\text{CEC}, \quad (1) $$

$$ \sigma''(\sqrt{f_1f_2}) \approx \frac{M_n(f_1, f_2)}{\alpha}, \quad (2) $$

$$ M_n(0, +\infty) \approx \frac{1}{F\phi}\lambda\rho_g\text{CEC}, \quad (3) $$

where $\alpha \approx (2/\pi) \ln A$ and $A$ denotes the ratio between $f_2(> f_1)$ and $f_1$, $F$ (dimensionless) is the formation factor, $\phi$ (dimensionless) denotes the connected porosity, $B$ (in m$^2$s$^{-1}$V$^{-1}$) is an effective mobility of the counterions in the electrical double layer coating the surface of the clay minerals, and $\lambda=R B$ (in m$^2$s$^{-1}$V$^{-1}$) denotes another effective mobilities of the counterions in the double layer coating the surface of the grains ($R$ is a dimensionless number close to 0.10 for clay-rich media and corresponds to the ratio between the normalized chargeability and the surface conductivity, see Revil et al. 2017), $\rho_g$ the mass density of the grains (~2700 kg m$^{-3}$ for clay-rich siliciclastic materials), and CEC (in C kg$^{-1}$ or meq/100 g with 1 meq/100 g = 963.20 C kg$^{-1}$) denotes the Cation Exchange Capacity of the material (mostly associated with clay minerals). Combining eqs (2) and (3) yields an approximate equation for the quadrature conductivity

$$ \sigma'' \approx \frac{1}{\alpha F\phi} \lambda \rho_g \text{CEC}. \quad (4) $$
<figure><img src="image_6.png" /><figcaption>**Figure 5.** Selected examples of complex conductivity spectra. (a) In-phase conductivity of the muddy glacio-lacustrine clay and Toarcian clayrock. (b) In-phase conductivity of the pristine glacio-lacustrine clay and pebbles. (c) Quadrature conductivity of the muddy glacio-lacustrine clay and Toarcian clayrock. (d) Quadrature conductivity of the pristine glacio-lacustrine clay and pebbles. One of the main goal of our work is to the electrical properties associated with induced polarization to separate the different types of clay-rich materials at Harmalière. We observe that the electrical properties of the clay-rich sediments are not simply related to their water content since the water content of the Toarcian clay-rock (porosity 0.10) is much smaller than for the glacio-lacustrine sediments (porosity 0.50 to 0.79). Pore water conductivity used in the experiments (NaCl, 25 °C): Sample HC1: 0.271 S m⁻¹, HC2: 0.13 S m⁻¹, HT2: 0.093 S m⁻¹, HT4: 0.093 S m⁻¹, HA: 0.066 S m⁻¹, and HL5: 0.071 S m⁻¹.</figcaption></figure>

Finally, the formation factor *F* and the porosity are connected by Archie's law (Archie 1942),

$$F = \phi^{-m}, \qquad (5)$$

where *m* (>1) (dimensionless) is called the cementation or porosity exponent or first Archie's exponent in the literature. Eqs (1) to (5) constitute a simple petrophysical model able to describe the in-phase and quadrature conductivity neglecting their frequency dependence as done in Vinegar & Waxman (1984) (for a more complete model, see discussions in Revil et al. 2017). If the material is partially saturated, the porosity can be replaced by the water content θ = *s*<sub>w</sub>φ where *s*<sub>w</sub> denotes the saturation. As discussed in details in the companion paper (Casotti et al. 2025), using eqs (1)

and (3), we can determine the water content and the CEC if the conductivity of the pore water is known.

### 3.3. Description of the samples

Four types of samples are investigated in the present study (see Fig. 4). The properties of the core samples are described in Tables 1–3. The two first samples correspond to Quaternary (Würm) glacio-lacustrine clayey materials (see Monjuvent 1973; Antoine et al. 1981) forming the main component of the landslide itself as mentioned in Section 2. The low plasticity indexes of these clays explain their rapid transformation into a visco-plastic material (Giraud et al. 1991). This is why we have studied this clayey material in two states: (i) A pristine state has been sampled to provide cubic
<figure><img src="image_7.png" /><figcaption>**Figure 6.** Samples conductivity versus pore water conductivities (plotted in a log log space). (a) Samples HC (muddy glacio-lacustrine silty clay, porosity 0.79). (b) Samples HL (pristine glacio-lacustrine silty clay, porosity 0.51). (c) Samples HT (Toarcian clay-rock, porosity 0.10). (d) Samples HA (pebbles of the Drac paleochannel, porosity 0.28). The fit of the data with a linear conductivity model is used to determine the formation factor *F* and the surface conductivity *σ*<sub>s</sub>. The yellow-coloured area corresponds to the range of pore water conductivity measured in the field (surface waters and springs).</figcaption></figure>

samples with a minimum of disturbances (porosity ~0.40–0.50).
(ii) Their liquefied (muddy) form involving high porosity (~0.80) pastes or muds. DRX (X-Ray Diffraction) analysis on total powder shows that the proportion of clay mineral in the glacio-lacustrine clay is >50 per cent. Analyses of the fraction smaller than 2 µm show that the clay mineralogy consists mainly of illite (42 to 47 per cent), chlorite (14 to 16 per cent), quartz (14 to 16 per cent), calcite (15 to 19 per cent), feldspars (6 to 10 per cent) and montmorillonite (<5 per cent) (Fiolleau 2020). In some cases, the amount of smectite can be higher and reach 9 per cent (Carrière *et al.* 2018). The illites of the glacio-lacustrine clayey materials derive mainly from the erosion of the much older Callovo-Oxfordian and Dogger formation (Monjuvent *et al.* 1982).

The third formation corresponds to the infilling material of a
former Drac river paleochannel with alluvial fills formed by more
and less cemented pebbles that are forming a rather compact assem-
blage. One sample formed by uncemented pebbles has been studied
in the laboratory (Sample HA, see Table 1). There are few outcrops

of this formation in the lower part of the Harmalière landslide and
they will be described in the last part of our paper. There are also
visible in the Avignonet landslide nearby and where outcrops show
a rather cemented material.

The final samples to be studied belong to the Toarcian clay-
rock formation that is a low-porosity (0.09–0.10) rock forming the
substratum below the shallow sediments present at Harmalière. In-
tact core samples of the Toarcian clay-rock were also taken from
the Tournemire test site (South Eastern France) that have not been
submitted to the weathering of outcrops like the samples collected
at Harmalière in the vicinity of the Monteynard–Avignonet lake.
The Toarcian argillaceous sequence was formed as part of the
Grands Causses Basin, which is related to the opening of the Tethys
Ocean and the Toarcian compacted shale formation deposited in
a deep marine environment. Cosenza et al. (2008) measured the
porosity and CEC of two Toarcian core samples. Their porosities
are 0.082 and 0.084 and their CEC values are 9.6 meq/100 g and 9.5
meq/100 g. This is in agreement with the values reported in Table 1
<figure><img src="image_8.png" /><figcaption>**Figure 7.** Formation factor versus porosity and comparison with Archie's law expressed as a power-law between the formation factor *F* and the porosity *φ*. The literature data are from Revil *et al.* (2005), Jougnot *et al.* (2009, 2010, 2021, 2023b) and Descostes *et al.* (2008). COx stands for Callovo-Oxfordian clayrocks (filled blue squares).</figcaption></figure>

in the present study. Although the Toarcian formation can exhibit facies that differ significantly in terms of clay content and, more importantly, clay assemblage, we decided to include the Tournehire Toarcian samples in this study given their similar porosity and cation exchange capacity with respect to the values obtained for the core samples from the site (see Table 1).

The CEC was obtained with the cobalthexamine titration method (Ciesielski *et al.* 1997; Aran *et al.* 2008) and the porosity by measuring the volume of the core samples, and both their dry and saturated weights.

## 3.4. Tests of the petrophysical relationships

Complex conductivity measurements are performed with the ZELSIP04-V02 impedance meter developed by Egon Zimmermann in Germany (Zimmermann *et al.* 2008). Some selected complex conductivity spectra are shown in Fig. 5 in the frequency range 10 mHz–45 kHz. The uncompacted lacustrine clay (shallow formation) is slightly more conductive and polarizable than the low-porosity Toarcian samples forming the substratum. This may appear as an advantage in the field to separate the two formations using the type of clustering approach recently applied by Revil *et al.* (2024). The pebbles are less conductive and polarizable by comparison with the clayey sediments.

In order to analyse the complex conductivity spectra in terms of fundamental parameters and their relationship to porosity and CEC, we first plot the conductivity of the core samples (at 1 Hz) versus the conductivity of the pore water at various salinities (NaCl solutions). The results are shown in Fig. 6. Then, we fit the data using with the relationship $\sigma' \approx \sigma_w/F + \sigma_S$ (e.g. Waxman & Smits 1968) where $\sigma_S$ denotes the surface conductivity and $F$ denotes the (intrinsic) formation factor of each sample. This procedure is done for all the samples of our data set and the resulting surface conductivity and formation factors are reported in Table 1. In Fig. 6, we also reported the range of pore water conductivities obtained in the field by sampling various springs and surface waters occurring in the landslide. The results of Fig. 6 indicate the strong importance of surface conductivity in controlling the resistivity of the clayey materials in the field. This indicates that both the water content and the CEC control the resistivity of such clayey materials. Our results strongly disagree with the statements made by Jongmans *et al.* (2009) who wrote that the resistivity of the remobilized clayey material forming the mudflow of Harmalière is controlled only by its water content.

Now that we have the formation factor and surface conductivity data, we can connect the formation factor to the porosity. Similarly, we can connect the surface conductivity to both the
<figure><img src="image_9.png" /><figcaption>**Figure 8.** Comparison between the surface conductivity and the ratio between the CEC scaled by the tortuosity of the pore space (product of the formation factor times the connected porosity). The trend is consistent with the Stern layer model used to comprehend the induced polarization of clay-rich sediments. COx stands for Callovo-Oxfordian clay-rock.</figcaption></figure>

porosity and the CEC. Fig. 7 shows the formation factor versus the porosity data. We see that the overall dataset is consistent with Archie's law $F = \phi^{-m}$ with $m = 2.0 \pm 0.2$. In Fig. 8, we plot the surface conductivity versus the CEC normalized by the tortuosity of the pore space given by the product of the formation factor and the porosity checking the relationship (see eq. 1) $\sigma_s \sim \text{CEC}/F\phi$. Such relationship between the surface conductivity and the normalized chargeability was also envisioned by Weller et al. (2013). Fig. 8 confirms such proportionality between the two parameters.

In Fig. 9, we checked eq. (2) validating the proportionality between the normalized chargeability and the quadrature conductivity (see Keller 1959; Lesmes & Frye 2001; Revil et al. 2017). Given a range of frequencies, we check the value of the proportional-ity constant $\alpha$. Finally, both the normalized chargeability and the

surface conductivity are proportional to the ratio CEC/Fφ (cation exchange capacity normalized by the tortuosity of the bulk pore space). Therefore the normalized chargeability and the surface conductivity are expected to be proportional to each other (their ratio defined the dimensionless number R defined by Revil, 2012). This is checked by Fig. 10.

Therefore the materials from the Harmalière landslide conform to the dynamic Stern layer model used to describe the conductivity and normalized chargeability as described in Casotti et al. (2025). Making an estimate of the pore water conductivity (from the springs found in the transition zone of the mudflow), we can determine both the water content (equal to the porosity assuming saturation) and the CEC from the conductivity and normalized chargeability values. This is precisely what will be done below in Section 4 using the field data collected through the landslide.
<figure><img src="image_10.png" /><figcaption>**Figure 9.** Comparison between the normalized chargeability (determined as the difference of the in-phase conductivity between 10 kHz and 10 mHz) and the quadrature conductivity determined at the geometrical mean of the two frequencies (i.e. 32 Hz). The theory based on the constant phase model leads to a predicted coefficient $\alpha \approx 8.8$ using $\alpha \approx (2/\pi) \ln A$ and $A$ denotes the number of decades between $f_1$ and $f_2$ ($A = \log(f_1/f_2) = 10^6$ for the six decades between the two frequencies $f_2 = 10$ mHz and $f_1 = 10$ kHz). The literature data are from Revil *et al.* (2017).</figcaption></figure>

# 4 FIELD SURVEY AND INTERPRETATION

## 4.1. Field investigations

Eight Time-Domain Induced Polarization (TDIP) profiles (labelled P1 to P8, see positions in Fig. 11) with a 20 m electrode spacing were acquired using ABEM LS2 and SAS4000 between April 2023 and May 2024. The profiles were acquired with a 1.26 km cable using roll-alongs of the electrodes to reach the desired total length for each profile (see Revil *et al.* 2023a, for similar surveys on volcanoes with very long cables). In parallel, a longitudinal profile on the upper part of the landslide, with a total length of 560 m, was acquired with a 5 m-spacing but is not presented hereinafter. Furthermore, a small profile (electrode spacing of 2 m) was conducted on the lower part of the landslide covering both the outcropping pebbles and the Toarcian formation. In total, the geophysical survey comprises 788 electrodes, 15.7 km, 13 012 apparent resistivity data (after filtering), and 5539 apparent chargeability data (after filtering). The data were filtered using standard deviations and deviations from neighboring measurements in the pseudo-sections of the apparent resistivities

and chargeabilities. Additionally, for the apparent chargeability decay curves, measurements were discarded when the quality of the exponential fit, assessed using the coefficient of determination $r^2$, was below a critical threshold. This additional filtering implies that the number of chargeability data points is lower than that of resistivity data.

In addition to the geophysical data, we sampled the ground water thanks to the occurrences of few springs plus the surface waters in few rivers (see position in Fig. 11). The electrical conductivity of these waters was measured using a Hanna Dist-3 field conductivity-meter, with temperature correction (values reported at 25 °C). Water samples were collected from nine sites. Measured conductivities ranged from 542 µS cm⁻¹ (surface water) to 1285 µS cm⁻¹ (spring water), and vacuum-saturated samples were prepared with water at a conductivity of 927 µS cm⁻¹ (25 °C). We observed the presence of centimeter-sized gypsum crystals in the glacio-lacustrine clays suggesting sulphate-rich pore waters. Inductively coupled plasma mass spectrometry (ICP-MS) additional water analyses conducted at EDYTEM shows a distinct geochemical contrast between spring and surface waters, reflecting significant mineral dissolution
<figure><img src="image_11.png" /><figcaption>**Figure 10.** Comparison between surface conductivity and normalized chargeability. The ratio between the two quantities defines the value of the dimensionless number *R* ( = 0.10 for clay minerals, see Revil *et al.* 2017). The literature data are from Panwar *et al.* (2021). The data reported in Casotti *et al.* (2025) correspond to the Garins landslide.</figcaption></figure>

and ion exchange processes within the glacio-lacustrine clays (see Table 4). The conductivity of the lake water itself was measured. The conductivity profile of the lake water shows an increase of the conductivity with depth (corrected at 25 °C) from 250 µS cm⁻¹ close to the lake surface to 600 µS cm⁻¹ (25 °C) at the bottom of the Monteynard–Avignonet lake (STE 2022). Fig. 6 shows that surface conductivity dominates the overall conductivity response of the 4 electrofacies present at the site when we plot the range of in situ pore water conductivities. Consequently, Archie’s law cannot be used as a conductivity equation.

Coming back now to the field data, some characteristic examples of the apparent chargeability and normalized chargeability decays curves are shown in Fig. 12. These apparent normalized chargeability curves are obtained by dividing the apparent chargeability curves by the apparent resistivity measured with the same ABMN quadrupole (AB current electrodes, MN voltage electrodes). These apparent normalized chargeability curves are more sensitive to the four electrofacies than the apparent chargeability curves themselves (see Fig. 12).

## 4.2. Tomograms and their interpretation

The finite element method is used to solve the classical elliptic equation for the electrical potential (forward modelling). We assume

that the conductivity and normalized chargeability of the facies can be considered as isotropic (see for instance Rücker *et al.* 2006). We consider the positions of all the electrodes and we first solve the electrical potential distribution for each electrode used as a current source. Then, using the superposition principle, the potential distribution is obtained for each bipole AB (forming a source/sink current pair). The 3-D inversion is conducted in parallels using a custom research code in a parallel environment using 789 processing cores on a distributed memory supercomputer located at the Pacific Northwest National Laboratory, Richland WA, USA (see Johnson *et al.* 2010, for details).

We first invert the transfer resistance data by minimizing a cost function composed of the data misfit term and a model regularization term, which imposes a smoothly varying spatial distribution, subject to fitting the data. We use a data covariance matrix to weight the data based on the data error and the L₂ norm. An Occam inversion scheme seeking the smoothest model among the models that fits the data within an expected tolerance is used (Constable *et al.* 1987). The value of the regularization parameter is determined using a cooling approach and the objective function is minimized with the parallelized conjugate gradient least squares approach (Johnson *et al.* 2010). The topography is used to build the mesh for the forward and inverse models. The resistivity survey utilized 788 unique electrode locations to collect 13 012 apparent resistivity data
<figure><img src="image_12.png" /><figcaption>**Figure 11.** Map showing the locations of the water samples used to get an idea of the distribution of the pore water conductivity at the field scale. The range of pore water conductivity in the field is 0.05–0.12 S m⁻¹ at 25 °C. The map shows also the 8 profiles (labelled P1 to P8) performed over the area (electrode spacing of 20 m) and used to reconstruct the 3-D distribution of the electrical conductivity and normalized chargeability.</figcaption></figure>

<table><caption>**Table 4.** Major cations for the surface water collected at the intersection of profiles P1 and P2 and pore sample water sample. The pore water was obtained by equilibrating surface water with a large quantity of clay for one month. LQ denotes the Limit of Quantification. The chemical analyses were performed by ICP-MS in EDYTEM</caption><thead><tr><th>Water sample</th><th>Na(ppm)</th><th>Mg(ppm)</th><th>K(ppm)</th><th>Ca(ppm)</th></tr></thead><tbody><tr><td>Pore-Water (HC1)</td><td>43.7</td><td>230.9</td><td>10.6</td><td>946.8</td></tr><tr><td>Surface-Water</td><td>18.4</td><td>36.1</td><td>&lt;LQ</td><td>498.5</td></tr></tbody></table>

(after filtering) and 5539 apparent chargeability data used in the inversion. The unstructured tetrahedral finite element computational mesh consists of 579 374 nodes and 3395 892 tetrahedral elements. Such high number of elements is required to account for the rough topography of the landslide (see Figs 1 and 3), and to

refine near the large number of electrodes to accurately simulate the large potential gradients generated during current injection.

Each iteration for the DC (Direct Current) conductivity inversion (13 012 measurements) required approximately 5 min and the solution converged in 11 Gauss–Newton iterations. Convergence is reached based on the level of data error: when the L₂ norm of the data misfit reduces to a value consistent with the estimated data noise level, the inverse solution is assumed to have converged to the right solution (normalized chi-squared error of 0.98 at the 11th iteration). The inverted conductivities typically range from 0.1 to 1 × 10⁻³ S m⁻¹. The normalized chargeability inversion is conducted using the classical approach in which the first inversion inversion is executed for the observed DC-potential and the second for the instantaneous potential. Conditional regularization constraints are applied to latter inversion to ensure the bulk conductivity for the instantaneous potential is greater than or
<figure><img src="image_13.png" /><figcaption>**Figure 12.** Typical field decay curves for the four lithologies met in the field including the pebbles, the glacio-lacustrine laminated (pristine) clay, the disturbed glacio-lacustrine clay, and the Toarcian clayrock. (a) Apparent chargeabilities. (b) Apparent normalized chargeability (obtained by dividing the apparent chargeability by the apparent resistivity). Note that the apparent normalized chargeabilities are better lithological indicators than the apparent chargeabilities as expected from the theory.</figcaption></figure>

equal to the DC conductivity. The normalized chargeability is then given as the DC conductivity subtracted from the instantaneous conductivity (see details in Oldenburg & Li 2000).

The results of the inversion are shown in Figs 13 and 14 for the conductivity and normalized chargeability with two different views. We see clearly the mudflow breaking through the paleochannel of the Drac river formed of pebbles that are mechanically competent. Figs 13 and 14 show that the mudflow is 30 to 50 m thick and could have been blocked by the pebbles of the NW-SE-oriented paleochannel of the Drac river. Because the pebbles of this paleochannel are partially cemented and compact, this paleochannel acts as a buttress that could mechanically prevent the glacio-lacustrine clays

from sliding in some places. Nevertheless, it is clear from Figs 13 and 14 that the mudflow found a path through this paleochannel to reach the Monteynard-Avignonet lake.

Two cross-sections are analysed in details (Figs 15–18). A longitudinal cross-section and a transverse cross-section are shown in Figs 15 and 17, respectively, in terms of both electrical conductivity and normalized chargeability tomography. Once the conductivity and normalized chargeability tomograms have been obtained, a first-order lithological interpretation can be proposed based solely on these two parameters. Domains combining both high conductivity and high normalized chargeability are interpreted as remobilized glacio-lacustrine clays, while slightly lower values correspond to
<figure><img src="image_14.png" /><figcaption>**Figure 13.** 3-D tomograms (view from the South-West). (a) 3-D-electrical conductivity tomogram of the Harmalière landslide. We clearly see the mudflow appearing more conductive than the pebbles. The tomogram demonstrates that the mudflow found or force a path through the pebbles corresponding to a paleochannel of the Drac River. The altitude is given in meters above sea level (masl). The glacio-lacustrine (laminated) clays have a conductivity of 0.03 S m⁻¹ in their pristine state and 0.1 S m⁻¹ in the mudflow. (b) 3-D-normalized chargeability tomogram.</figcaption></figure>

the undisturbed laminated clays. Very low values of both parame-
ters are indicative of compact alluvial pebbles. Intermediate values
correspond to the Toarcian claystone, which is supported by out-
crops. This classification provides a robust first-order discrimination
of the lithofacies. We can then go further by converting conductiv-
ity and normalized chargeability tomograms into water content and
CEC tomograms according to the scheme proposed in the compan-
ion paper (Casotti et al. 2025; see also Moser et al. 2025, for a
recent application to an active rock glacier). We assumed a pore
water conductivity of 900 µm cm⁻¹ (25 °C). The resulting water
content and CEC tomograms are shown in Figs 16 and 18 for both
the longitudinal and transverse profiles indicate that the mud flow
has a porosity of 0.60 in fair agreement with the values obtained

in the laboratory (between 0.50 to 0.70 with an average around
0.60). The water content of the pebbles is around 0.20 due to the
compactness of this alluvial formation. The CEC resulting from
the tomogram is between 1 to 5 meq/100 g for the pebbles, 10 to
20 meq/100 g for the remobilized glacio-lacustrine sediment and
around 30 meq/100 g for the Toarcian clayrock. This can be com-
pared to the values obtained from the laboratory measurements (see
Table 1) indicating a CEC of 4.2 meq/100 g for the pebbles and
around 10 ± 5 meq/100 g for the remobilized glacio-lacustrine sed-
iment and the Toarcian clayrock (see Table 1). Consequently, there
is a good agreement between the laboratory data and the field data.

In Fig. 19, we performed a cross-plot of the normalized charge-
ability versus the inverted conductivity from the 2-D sections of
<figure><img src="image_15.png" /><figcaption>**Figure 14.** 3-D-tomogram. (a) 3-D electrical resistivity tomogram along a longitudinal section of the mudflow (view from the South-East). We can follow the position of the mudflow creeping in the direction of the Monteynard-Avignonet lake. The conductivity of the mudflow is around 0.1 S m⁻¹, which is totally consistent with the data reported in Fig. 6(a) for the muddy glacio-lacustrine clays at the pore water conductivity found at the springs found along the landslide. (b) Normalized chargeability tomogram.</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>**Figure 15.** Tomograms of the electrical conductivity and normalized chargeability from the geophysical survey along the profile that following the main axis of the landslide from the Sinard plateau to the Monteynard-Avignonet lake. (a) Electrical conductivity tomogram. (b) Normalized chargeability tomogram obtained from the chargeability tomogram and the conductivity by performing a multiplication cell-to-cell of the two quantities. The tomograms show the finite element mesh used for both the forward and inverse modelling.</figcaption></figure>

TDIP profiles at Harmalière. The colour scale represents the data density on this plot estimated using a kernel density estimation. Three references profiles with 2 m electrode spacing were conducted in addition to the P1 to P8 profiles in order to characterize the compacted pebbles, the Toarcian formations, and the mudflow where they outcrops at the bottom of the landslide (not shown here). The results are shown as 90 per cent confidence ellipses in the cross-plot of Fig. 19. Laboratory measurements of conductivity/normalized chargeability of the samples in the frequency- and time-domain (see the stars and squares, respectively). This approach

provides an overview of the geoelectrical data distribution across the landslide facilitating the identification of the lithofacies as done in the companion paper (Casotti et al. 2025). The frequency-measured properties exhibit higher normalized chargeability values than those obtained with TDIP in the field (using a period of 1 s) as explained in the previous paper. A correction factor of 10 is used in the estimation of the cation exchange capacity as explained in Casotti et al. (2025). Toarcian and glacio-lacustrine samples form consistent groups, whereas the saturated alluvium sample appears offset from its confidence ellipses, raising questions about its representativity.
<figure><img src="image_17.png" /><figcaption>**Figure 16.** Tomograms of the water content and CEC. (a) Water content tomogram. (b) CEC tomogram. Note that the Toarcian is characterized by high CEC (10–20 meq/100 g) and low porosity (0.10) consistent with that observed on the rock samples (CEC 8–12 meq/100 g) and porosity of 0.09 ± 0.02.</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>**Figure 17.** Tomograms of the electrical conductivity and normalized chargeability (Profile 5). (a) Electrical conductivity tomogram. (b) Normalized chargeability tomogram. Here, porosity is assumed to correspond to water content, considering fully saturated conditions.</figcaption></figure>

Fig. 20 shows a picture of the mudflow indicating where the mudflow crosses through the paleochannel of the Drac river, which is characterized by a topographical feature associated with the hardness of the pebbles. Fig. 21 summarizes the architecture of the Harmalière landslide with the main features characterizing the root and toe of the mudflow. We distinguish the three phases of the landslide with (i) the rotation of block of glacio-lacustrine clayey material, (2) their fragmentation (transition zone), and (ii) the mudflow creeping down to the lake after breaching the paleochannel of the Drac river made of pebbles. Further work will have to use the data collected in this paper and rheological lows for the glacio-lacustrine clays to forward model the complete hydromechanical behavior observed at Harmalière. Note that both the landslide rough topography and dense vegetation limit the feasibility of extensive drilling. Targeted boreholes (especially near the toe or lateral margins) may help calibrate geophysical data and support future coupled modeling.

## 5 CONCLUSIONS

A large-scale 3-D induced polarization survey was undertaken to explore the anatomy of the Harmalière landslide with a particular emphasis on a large mudflow of glacio-lacustrine silty clay nested in this valley. This field survey was accompanied by a petrophysical study with a total of 22 samples. This collection includes samples of four lithofacies (i) pristine glacio-lacustrine silty clay, (ii) glacio-lacustrine uncompacted mud, (iii) low-porosity Toarcian clayrock and (iv) pebbles from a former Drac river paleochannel. The petrophysical model is used to connect the conductivity and normalized chargeability to the water content and CEC of the porous media. A 3-D tomogram of the electrical conductivity and normalized chargeability is performed using the least-squares method penalizing the roughness of the tomograms of the conductivity and normalized chargeability. This geophysical survey shows clearly the different lithofacies present at the site and is used to get tomograms of the water content and cation exchange capacity of the area. Then, the
<figure><img src="image_19.png" /><figcaption>**Figure 18.** Tomograms of the water content and CEC along a transverse East–West section (Profile 5). (a) Water content tomogram. The Toarcian and pebbles are characterized by a low water content in comparison with the glacio-lacustrine clayey material. (b) CEC tomogram. The pebbles are characterized by a low CEC in comparison with the Toarcian and glacio-lacustrine (laminated) clayey materials.</figcaption></figure>

conductivity and normalized chargeability are combined to produce tomograms of the water content and CEC, two parameters that are crucial to understand the rheology of such glacio-lacustrine materials as demonstrated in the literature. This large-scale 3-D investigation necessarily involves trade-offs in resolution due to the extent of the site, the challenging topography, and the impact of electromagnetic noise from a nearby high-voltage power line. Although some areas are less resolved, the overall dataset still provides consistent and interpretable features. These factors, along with the inherent limitations and uncertainties of the inversion plus the uncertainty in the petrophysical model, should be kept in mind when assessing the level of details provided by such an approach. Now that we have better documented the anatomy of this landslide and especially the mudflow, the next step will be to gain a better understanding of its rheology especially the mechanical behavior of the glacio-lacustrine clays during gravitational instabilities and particularly to the solid-to-fluid transition. In turn, this will allow a better understanding of

the dynamics of the mudflow that could be numerically modelled coupled to its monitoring. Deep learning could be used to predict its evolution in association with the occurrence of extreme events in the context of climate change.

## DATA AVAILABILITY

The data used in this manuscript are available upon request to the corresponding author. The forward modelling and inversion code is available in open source at https://e4d.pnnl.gov.

## ACKNOWLEDGMENTS

We thank the group GINGER for its support. The PhD grant of C. Casotti was supported by a CIFRE grant with ANRT. AR thanks the CNRS for its support over the past years. The equipment used
<figure><img src="image_20.png" /><figcaption>**Figure 19.** Cross-plot between the normalized chargeability and the electrical conductivity. The dashed line corresponds to a slope given by *R* = 0.10 (value for clay-rich materials). When the data are close to this line, their conductivity is dominated by the surface conductivity. The points represent inverted data from field surveys. The squares correspond to time-domain induced polarization measurements (TDIP) performed on the same samples as the SIP measurements (stars). The ellipses indicate a 90 per cent confidence interval for references measurements conducted on outcrops of Toarcian formations, pebbles paleochannel, and the mudflow deposit at the landslide toe, using a 2 m electrode spacing for time-domain induced polarization data.</figcaption></figure>
<figure><img src="image_21.png" /><figcaption>**Figure 20.** Picture of the Harmalière landslide and mudflow taken from the scarp. The paleochannel of the Drac river (characterized by the presence of alluvial materials including more or less cemented pebbles and sands) has some geomorphological expression in the landscape. Within the landslide, numerous evidences of instabilities can be observed like scarps, fissures, gullies and mudflows.</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>**Figure 21.** Simplified sketch of the anatomy of the Harmalière landslide and mudflow. The upper part of the landslide is formed by the rotational slide of large blocks along listric faults. The main scarp can reach 30 m in height. The middle part of the landslide is characterized by a transition zone where the blocks break down and the glacio-lacustrine sediments gradually lost their cohesion. The landslide becomes a mudflow, which is creeping down into a long, deeply gullied, muddy flow that ends in the Monteynard-Avignonet Lake and a path through the compact, locally alluvial infilling of a former Drac paleochannel.</figcaption></figure>
in this project was obtained thanks to the funding of the Auvergne-Rhône-Alpes region and FEDER through the IRICE project with the help of IRISK. IRISK is thanked for its strong support and fruitful discussions regarding gravitational instabilities over the years and especially M. Bernard and F. Nicot for their strong encourage- ments and works over the years. We also thank the PALLAS project (Banque de Développement) to fund some of the methodology used in this work. We thank W. Bouits, L. Fabre, L. Gervais, F. Lemot, J. Richard, X. Zhao, (EDYTEM), L. Alves, M. Boyer, S. Franco, I. Gaston-Kirby, L. Joly, and J. Vacheresse, (GINGER CEBTP), J. Agziou and P. Schoeneich (PACTE), E. Benoist, R. Dauphin, G. Duloy, K. Le Bastard, M. Morel, and Y. Robert, (ONF/RTM), H. Toyb (INDURA), G. Bièvre (ISTERRE), E. Boulat, F. Casotti and Y. Oytana, for their help in the field and/or fruitful discussions. We thank E. Zimmermann for the construction of the ZELSIP04-V02 impedance meter in Germany and P. Dick for the Toarcian samples from the Tournemire site and the PALLAS project to have permit- ted their investigations. We thank the Editor, Dr Tina Martin, and two anonymous referees for their useful comments and their time. This paper is dedicated to both Vincenzo Lapenna and Myriam Schmutz for their pioneering work regarding the use of geophysical investigations for landslides.

REFERENCES

Antoine, P., Giraud, A. & Monjuvent, G., 1981, Les argiles litées du Trièves (Isère); conditions de gisement et exemples de propriétés géotechniques, *Bull. Soc. Géol. Fr.*, **23**(2), 115-127.

Aran, D., Maul, A. & Masfaraud, J., 2008. A spectrophotometric measure-
ment of soil cation exchange capacity based on cobaltihexamine chloride
absorbance, *C. R. Geosci.*, **340**(12), 865–871.

Archie, G.E., 1942. The electrical resistivity log as an aid in determining some reservoir characteristics, *Trans. AIME*, **146**, 54–62.

Bardou, E., Bowen, P., Boivin, P. & Banfill, P., 2007. Impact of small amounts of swelling clays on the physical properties of debris-flow-like granular materials. Implications for the study of alpine debris flow, *Earth Surf. Process Landform*, **32**(5), 698–710.

Blanchet, F., 1988. *Etude Géomécanique de Glissements de Terrain dans Les Argiles Glaciolacustres de la Vallée Du Drac, PhD Thesis*, Grenoble University, 157pp.

Carrière, S.R. et al. 2018. Rheological properties of clayey soils originating from flow-like landslides, *Landslides*, **15**, 1615–1630.

Casotti, C. et al., 2025. Induced polarization applied to landslides. 1. Imaging hydraulic barriers, in press in *Geophysical Journal International*.

Chapman, D.L., 1913. A contribution to the theory of electrocapillarity, *Philos. Mag.* **25**(6), 475-481.

Ciesielski, H., Sterckeman, T., Santerne, M. & Willery, J.P., 1997. A comparison between three methods for the determination of cation exchange capacity and exchangeable cations in soils, *Agronomie*, **17**, 9-16.

Clavier, C., Coates, G. & Dumanoir, J., 1977, The theoretical and experi-
mental basis for the “Dual Water” model for interpretation of shaly sands,
*52nd Ann. Fall Tech. and Exhib., Soc. Petr. Eng.*, **01**, 317–322.

Constable, S.C., Parker, R.L. & Constable, C.G., 1987. Occam's inversion: a practical algorithm for generating smooth models from electromagnetic sounding data, *Geophysics*, **52**(3), 289–300.

Cosenza, P., Ghorbani, A., Revil, A., Zamora, M., Schmutz, M., Jougnot, D. & Florsch, N., 2008. A physical model of the low-frequency electrical polarization of clay rocks, *J. geophys. Res.* **113**, B08204.

Coutterand, S., 2010. *Etude Géomorphologique Des Flux Glaciaires dans les Alpes Nord-occidentales au Pleistocène Récent. Du Maximum de la Dernière Glaciation aux Premières Étapes de la Déglaciation, PhD thesis*, Université de Savoie Mont-Blanc, Chambéry, 468pp.

De Lima, O.A.L. & Sharma, M.M., 1990, A grain conductivity approach to shaly sandstones, *Geophysics*, **55** (10), 1347–1358.

Descostes, M., Blin, V., Bazer-Bachi, F., Meier, P., Grenut, B., Radwan, J. & Tevissen, E., 2008. Diffusion of anionic species in Callovo-Oxfordian argillites and Oxfordian limestones (Meuse/Haute-Marne, France), *Appl. Geochem.*, **23**(4), 655–677.

El Alam, J., Revil, A. & Dick, P., 2023. Induced polarization of clay-rich ma-
terials. 4. Water content and temperature effects in bentonite, *Geophysics*,
**90**(2), MR39-MR54.

El Alam, J., Revil, A. & Dick, P., 2024. Induced polarization of clay-rich materials. 5. Influence of hyperalkalinity in bentonite, *Geophysics*, **89**(6), E267-E279.

Fiolleau, F., 2020. *Etude des Mécanismes de Déformation d'un Glissement-coulée par une Approche Multi-méthodes, PhD thesis*, Université Grenoble Alpes, France, 315pp. https://theses.fr/2020GRALU025.

Fiolleau, F., Jongmans, D., Bièvre, G., Chambon, G., Lacroix, P. et al. 2021. Multi-method investigation of mass transfer mechanisms in a retrogressive clayey landslide (Harmalière, French Alps), *Landslides*, **18**(6), 1981-2000.

Flores Orozco, A., Williams, K.H. & Kemna, A., 2013. Time-lapse spectral induced polarization imaging of stimulated uranium bioremediation, *Near Surf. Geophys.*, **11**, 531-544.

Flores Orozco, A.F., Steiner, M., Katona, T., Roser, N., Moser, C., Stumvoll, M.J. & Glade, T., 2022. Application of induced polarization imaging across different scales to understand surface and groundwater flow at the Hofermuehle landslide, *Catena*, **219**, 106612.

Gallipoli, M., Lapenna, V, Lorenzo, P, Mucciarelli, M., Perrone, A., Piscitelli, S. & Sdao, F., 2000. Comparison of geological and geophysical prospecting techniques in the study of a landslide in southern Italy, *Eur. J. Environ. Eng. Geophys.*, **4**, 117-128.

Gariano, S.L. & Guzzetti, F., 2016. Landslides in a changing climate, *Earth Sci. Rev.*, **162**, 227-252.

Giraud, A., Antoine, P., van Asch, T.W.J. & Nieuwenhuis, J.D., 1991. Geotechnical problems caused by glacio-lacustrine clays in the French Alps, *Eng. Geol.*, **31**, 185-195.

Grosse, C., 2002, Relaxation mechanisms of homogeneous particles and cells suspended in aqueous electrolyte solutions, in *Interfacial Electrokinetics and Electrophoresis, Surfactant Science Series, Vol. 106*, 991pp., ed., Delgado, A. V., Marcel Dekker, Basel.

Johnson, T.C., Versteeg, R.J., Ward, A., Day-Lewis, F.D. & Revil, A., 2010. Improved hydrogeophysical characterization and monitoring through high performance electrical geophysical modeling and inversion, *Geophysics*, **75**(4), WA27-WA41.

Jongmans, D., Bièvre, G., Renalier, F., Schwartz, S., Beaurez, N. & Orego, Y., 2009. Geophysical investigation of a large landslide in glacio-lacustrine clays in the Trièves area (French Alps), *Eng. Geol.* **109**, 45-56.

Jougnot, D., Ghorbani, A., Revil, A., Leroy, P. & Cosenza, P., 2010, Spec-
tral induced polarization of partially saturated clay-rocks: a mechanistic
approach, *Geophys. J. Int.*, **180**(1), 210–224.

Jougnot, D., Revil, A. & Leroy, P., 2009, Diffusion of ionic tracers in the Callovo-Oxfordian clay-rock using the Donnan equilibrium model and the electrical formation factor, *Geochimica Cosmochimica Acta*, **73**, 2712-2726.

Keller, G.V., 1959. Analysis of some electrical transient measurements on igneous, sedimentary, and metamorphic rocks, in *Overvoltage Research and Geophysical Applications*, pp. 92-111, ed., Wait, J.R., Pergamon, Tarrytown, N.Y.

Lapenna, V., Lorenzo, P., Perrone, A., Piscitelli, S., Rizzo, E. & Sdao, F., 2005. 2D electrical resistivity imaging of some complex landslides in Lucanian Apennine chain, southern Italy, *Geophysics*, **70**(3), B11-B18.

Lapenna, V., Lorenzo, P., Perrone, A., Piscitelli, S., Sdao, F. & Rizzo, E., 2003. High-resolution geoelectrical tomographies in the study of Giarossa landslide (southern Italy), *Bull. Eng. Geol. Environ.*, **62**(3), 259-268.

Leroy, P., Maineult, A., Mendieta, A. & Jougnot, D., 2024. A mechanistic model for the complex conductivity of clay materials. I. Theory, *Geophys. J. Int.*, **241**(1), 86–109, https://doi.org/10.1093/gji/gga411.

Lesmes, D.P. & Frye, K.M., 2001. The influence of pore fluid chemistry on the complex conductivity and induced-polarization responses of Berea Sandstone, *J. Geophys. Res.*, **106**(B3), 4079–4090.
Letourneur, J. & Michel, R., 1971. *Géologie Du Génie Civil, Revue de Géographie Alpine*, Collection U, Geological series edited by par P. Bel-air, *Vol. 59*(3), 728 pp., ed., Colin, A.

Locat, J., 1997. Normalized rheological behavior of fine muds and their flow properties in a pseudoplastic regime, in *Debris-flow hazards mitigation: mechanics prediction and assessment*. pp. 260–269, ed., Chen, Cheng-lung, Water Res. Div., Western Region, Menlo Park, CA, American Society of Civil Engineers, New York.

Loke, M.H., Chambers, J.E., Rucker, D.F., Kuras, O. & Wilkinson, P.B., 2013. Recent developments in the direct-current geoelectrical imaging method, *J. Appl. Geophys.*, **95**, 135–156.

Maineult, A., Leroy, P., Mendieta, A. & Jougnot, D., 2025. A mechanistic model for the complex conductivity of clay materials. II. Comparison to experimental data, *Geophys. J. Int.*, **241**(1), 110–132.

Marescot, L., Monnet, R. & Chapellier, D., 2008. Resistivity and induced polarization surveys for slope instability studies in the Swiss Alps, *Eng. Geol.*, **98**(1–2), 18–28.

Monjuvent, G., 1973. La transfluence Durance-Isère. Essais de synthèse du Quaternaire du bassin du Drac, *Géologie Alpine*, **49**, 57–118.

Monjuvent, G., Reynaud, C., Jail, M. & Thomas, A., 1982. Formations quarternaires du Grésivaudan : les argiles de Saint-Ismier, *Revue de géographie alpine*, **70**(4), 367–390.

Moser, C., Morra di Cella, U., Hauck, C. & Flores Orozco, A., 2025. Spectral induced polarization survey for the estimation of hydrogeological parameters in an active rock glacier, *The Cryosphere*, **19**, 143–171.

Olhoeft, G.R., 1981a. Electrical properties of granite with implications for the lower crust, *J. Geophys. Res.*, **86**(B2), 931–936.

Olhoeft, G.R., 1981b. Electrical properties of rocks, in *Physical Properties of Rocks and Minerals*, pp. 257–297, eds, Touloukian, Y. S., Judd, W. R. & Roy, R. F., McGraw Hill.

Panwar, N. et al. 2021. Induced polarization of carbonates, *J. Geophys. Res. Solid Earth*, **126**, e2021JB022029.

Perrone, A., Lapenna, V. & Piscitelli, S., 2014. Electrical resistivity tomography technique for landslide investigation: a review, *Earth Sci. Rev.*, **135**, 65–82.

Revil, A., 2012. Spectral induced polarization of shaly sands: influence of the electrical double layer, *Water Resour. Res.*, **48**, W02517, https://doi.org/10.1029/2011WR011260.

Revil, A., 2013. Effective conductivity and permittivity of unsaturated porous materials in the frequency range 1 mHz–1 GHz, *Water Resour. Res.*, **49**, 306–327.

Revil, A. et al. 2017. Complex conductivity of soils, *Water Resour. Res.*, **53**(8), 7121–7147.

Revil, A. et al. 2023a. The thermal plumbing system of Stromboli volcano, Aeolian Islands (Italy) inferred from electrical conductivity and induced polarization tomography, *J. Geophys. Res. Solid Earth*, **128**, e2023JB026475.

Revil, A. et al. 2024. Field investigation of serpentinite with induced polarization and the K-means clustering technique, *Geophys. Res. Lett.*, **51**(18), e2024GL108920.

Revil, A., Ghorbani, A., Jougnot, D. & Yven, B., 2023b. Induced polarization of clay-rich materials. Part 1. The effect of desiccation, *Geophysics*, **88**(4), MR195–MR210.

Revil, A., Leroy, P. & Titov, K., 2005. Characterization of transport properties of argillaceous sediments: application to the Callovo-Oxfordian argillite, *J. Geophys. Res. Solid Earth*, **110**(B6), https://doi.org/10.1029/2004JB003442.

& Revil, A., Schmutz, M., Abdulsamad, F., Balde, A., Beck, C., Ghorbani, A. & Hubbard, S.S., 2021. Field-scale estimation of soil properties from spectral induced polarization tomography, *Geoderma*, **403**, 115380.

Revil, A., Soueid Ahmed, A., Coperey, A., Ravanel, L., Sharma, R. & Panwar, N., 2020. Induced polarization as a tool to characterize shallow landslides, *J. Hydrol.*, **589**, 125369.

Rücker, C., Günther, T. & Spitzer, K., 2006. Three-dimensional modelling and inversion of dc resistivity data incorporating topography-I. Modelling, *Geophys. J. Int.*, **166**(2), 495–505.

Ruffet, C., Darot, M. & Guéguen, Y., 1995. Surface conductivity in rocks: a review, Surveys in, *Geophysics*, **16**, 83–105.

Schmutz, M., Albouy, Y., Guérin, R., Maquaire, O., Vassal, J., Schott, J.J. & Descloître, M., 2000. Joint electrical and time domain electromagnetism (TDEM) data inversion applied to the Super Sauze earthflow (France), *Surv. Geophys.*, **21**(4), 371–390.

Schmutz, M., Guérin, R., Andrieux, P. & Maquaire, O., 2009. Determination of the 3D structure of an earthflow by geophysical methods: the case of Super Sauze, in the French southern Alps, *J. Appl. Geophys.*, **68**(4), 60–67.

STE (Sciences et Techniques de l'Environnement), 2022. *Étude Des Plans D'eau Du Programme De Surveillance Des Bassins Rhône-Méditerranée et Corse—Lot n°2 Centre : Rapport de Données Brutes et Interprétation—Retenue De Monteynard, Suivi Annuel 2021*, Rapport n° 20-8342, 62 pp., Savoie Technolac, Le Bourget-du-Lac, France.

Vinegar, H.J. & Waxman, M.H., 1984. Induced polarization of shaly sands, *Geophysics*, **49**, 1267–1287.

Waxman, M.H. & Smits, L., 1968. Electrical conductivities in oil-bearing shaly sands, *Soc. Pet. Eng. J.*, **8**(2), 107–122.

Weller, A., Slater, L. & Nordsiek, S., 2013. On the relationship between induced polarization and surface conductivity: implications for petrophysical interpretation of electrical measurements, *Geophysics*, **78**, D315–D325.

Zhou, B., 2018. Electrical resistivity tomography: a subsurface-imaging technique, *Applied Geophysics with Case Studies on Environmental, Exploration and Engineering Geophysics, IntechOpen*.

Zimmermann, E., Kemna, A., Berwix, J., Glaas, W., Münch, H.M. & Huisman, J.A., 2008. A high-accuracy impedance spectrometer for measuring sediments with low polarizability, *Meas. Sc. Tech.*, **19**(10), 105603.