Measurement sensors permanently installed on landslides will inevitably change their position over time due to mass movements. To interpret and correct the recorded data, these movements have to be determined. This is especially important in the case of geoelectrical monitoring, where incorrect sensor positions produce strong artefacts in the resulting resistivity models. They may obscure real changes, which could indicate triggering mechanisms for landslide failure or reactivation. In this paper we introduce a methodology to interpolate movements from a small set of sparsely distributed reference points to a larger set of electrode locations. Within this methodology we compare three interpolation techniques, i.e., a piecewise planar, bi-linear spline, and a kriging based interpolation scheme. The performance of these techniques is tested on a synthetic and a real-data example, showing a recovery rate of true movements to about 1% and 10% of the electrode spacing, respectively. The significance for applying the proposed methodology is demonstrated by inverse modelling of 4D electrical resistivity tomography data, where it is shown that by correcting for sensor movements corresponding artefacts can virtually be removed and true resistivity changes be imaged.

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.