S U M M A R YTwo strategies are presented for obtaining the maximum spatial resolution in electrical resistivity tomography surveys using a limited number of four-electrode measurement configurations. Both methods use a linearized estimate of the model resolution matrix to assess the effects of including a given electrode configuration in the measurement set. The algorithms are described in detail, and their execution times are analysed in terms of the number of cells in the inverse model. One strategy directly compares the model resolution matrices to optimize the spatial resolution. The other uses approximations based on the distribution and linear independence of the Jacobian matrix elements. The first strategy produces results that are nearer to optimal, however the second is several orders of magnitude faster. Significantly however, both offer better optimization performance than a similar, previously published, method. Realistic examples are used to compare the results of each algorithm. Synthetic data are generated for each optimized set of electrodes using simple forward models containing resistive and/or conductive prisms. By inverting the data, it is demonstrated that the linearized model resolution matrix yields a good estimate of the actual resolution obtained in the inverted image. Furthermore, comparison of the inversion results confirms that the spatial distribution of the estimated model resolution is a reliable indicator of tomographic image quality.

Landslides pose significant risks to communities and infrastructure, and mitigating these risks relies on understanding landslide causes and triggering processes. It has been shown that geophysical surveys can significantly contribute to the characterization of unstable slopes. However, hydrological processes can be temporally and spatially heterogeneous, requiring their related properties to be monitored over time. Geoelectrical monitoring can provide temporal and volumetric distributions of electrical resistivity, which are directly related to moisture content. To date, studies demonstrating this capability have been restricted to 2‐D sections, which are insufficient to capture the full degree of spatial heterogeneity. This study is the first to employ 4‐D (i.e., 3‐D time lapse) resistivity imaging on an active landslide, providing long‐term data (3 years) highlighting the evolution of moisture content prior to landslide reactivation and showing its decline post reactivation. Crucially, the time‐lapse inversion methodology employed here incorporates movements of the electrodes on the unstable surface. Although seasonal characteristics dominate the shallow moisture dynamics during the first 2 years with surficial drying in summer and wetting in winter, in the months preceding reactivation, moisture content increased by more than 45% throughout the slope. This is in agreement with independent data showing a significant rise in piezometric heads and shallow soil moisture contents as a result of prolonged and intense rainfall. Based on these results, remediation measures could be designed and early‐warning systems implemented. Thus, resistivity monitoring that can allow for moving electrodes provides a new means for the effective mitigation of landslide risk.

Capacitive resistivity (CR) is an emerging geophysical technique designed to extend the scope of the conventional methodology of DC resistivity to environments where galvanic coupling is notoriously difficult to achieve, for example across engineered structures (roads, pavements), hard rock, dry soil or frozen ground. Conceptually, CR is based on a four-point array capacitively coupled to the ground. Under A parametric study of the complex quasi-static transfer impedance reveals the existence of a restricted range of practical parameters that permits successful operation of CR instruments at low induction numbers. Theory predicts that emulation of the DC measurement is compromised if low-induction-number operation is not maintained throughout a survey area, for example in a zone of high conductivity.

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The internal moisture dynamics of an aged (> 100 years old) railway earthwork embankment, which is still in use, are investigated using 2D and 3D resistivity monitoring. A methodology was employed that included automated 3D ERT data capture and telemetric transfer with on-site power generation, the correction of resistivity models for seasonal temperature changes and the translation of subsurface resistivity distributions into moisture content based on petrophysical relationships developed for the embankment material. Visualization of the data as 2D sections, 3D tomograms and time series plots for different zones of the embankment enabled the development of seasonal wetting fronts within the embankment to be monitored at a high-spatial resolution and the respective distributions of moisture in the flanks, crest and toes of the embankment to be assessed. Although the embankment considered here is at no immediate risk of failure, the approach developed for this study is equally applicable to other more high-risk earthworks and natural slopes.Geophysical ground imaging techniques offer the potential to complement existing approaches by spatially characterizing and monitoring the internal conditions of earthworks to provide highresolution information of subsurface property changes and hence precursors to slope failure. Resistivity imaging, or electrical resistivity tomography (ERT), holds particular promise due to its sensitivity to both lithological variations (e.g., Shevnin et al. 2007) and changes in soil moisture, which can be imaged by applying appropriate petrophysical relationships linking resistivity and saturation (e.g., Cassiani et al. 2009;Brunet et al. 2010). Twodimensional ERT is now a well-established technique for investigating natural slopes with numerous recent examples of the use of the technique for structural characterization and hydrogeological investigations (e.g

S U M M A R YIf electrodes move during geoelectrical resistivity monitoring and their new positions are not incorporated in the inversion, then the resulting tomographic images exhibit artefacts that can obscure genuine time-lapse resistivity changes in the subsurface. The effects of electrode movements on time-lapse resistivity tomography are investigated using a simple analytical model and real data. The correspondence between the model and the data is sufficiently good to be able to predict the effects of electrode movements with reasonable accuracy. For the linear electrode arrays and 2-D inversions under consideration, the data are much more sensitive to longitudinal than transverse or vertical movements. Consequently the model can be used to invert the longitudinal offsets of the electrodes from their known baseline positions using only the time-lapse ratios of the apparent resistivity data. The example data sets are taken from a permanently installed electrode array on an active lobe of a landslide. Using two sets with different levels of noise and subsurface resistivity changes, it is found that the electrode positions can be recovered to an accuracy of 4 per cent of the baseline electrode spacing. This is sufficient to correct the artefacts in the resistivity images, and provides for the possibility of monitoring the movement of the landslide and its internal hydraulic processes simultaneously using electrical resistivity tomography only.

. Electrical resistivity tomography applied to geological, hydrogeological and engineering investigations at a former waste disposal site. Geophysics, Vol. 71, B231-B239. ABSTRACTA former dolerite quarry and landfill site was investigated using 2D and 3D electrical resistivity tomography (ERT), with the aims of determining buried quarry geometry, mapping bedrock contamination arising from the landfill, and characterizing site geology. Resistivity data were collected from a network of intersecting survey lines using a Wenner-based array configuration. Inversion of the data was carried out using 2D and 3D regularized least-squares optimization methods with robust (L1-norm) model constraints. For this site, where high resistivity contrasts were present, robust model constraints produced a more accurate recovery of subsurface structures when compared to the use of smooth (L2-norm) constraints. Integrated 3D spatial analysis of the ERT and conventional site investigation data was shown in this case to provide a highly effective means of characterizing the landfill and its environs. The 3D resistivity model was successfully used to confirm the position of the landfill boundaries, which appeared as electrically well-defined features that corresponded extremely closely to both historic maps and intrusive site investigation data. A potential zone of leachate migration from the landfill was identified from the electrical models; the location of this zone was consistent with the predicted direction of groundwater flow across the site. Unquarried areas of a dolerite sill were imaged as a resistive sheet-like feature, whilst the fault zone appeared in the 2D resistivity model as a dipping structure defined by contrasting bedrock resistivities.3

SUMMARY The use of optimized resistivity tomography surveys to acquire field data imposes extra constraints on the design strategy beyond maximizing the quality of the resulting tomographic image. In this paper, methods are presented to (1) minimize electrode polarization effects (2) make efficient use of parallel measurement channels and (3) incorporate data noise estimates in the optimization process. (1) A simulated annealing algorithm is used to rearrange the optimized measurement sequences to minimize polarization errors. The method is developed using random survey designs and is demonstrated to be effective for use with single and multichannel optimized surveys. (2) An optimization algorithm is developed to design surveys by successive addition of multichannel groups of measurements rather than individual electrode configurations. The multichannel surveys are shown to produce results nearly as close to optimal as equivalent single channel surveys, while reducing data collection times by an order of magnitude. (3) Random errors in the data are accounted for by weighting the electrode configurations in the optimization process according to a simple error model incorporating background and voltage‐dependent noise. The use of data weighting produces optimized surveys that are more robust in the presence of noise, while maintaining as much of the image resolution of the noise‐free designs as possible. All the new methods described in this paper are demonstrated using both synthetic and real data, the latter having been measured on an active landslide using a permanently installed geoelectrical monitoring system.