A new approach to quantify the reliability of Electrical Resistivity Tomography (ERT) images

Anthropogenic and natural hazards assessments need a good knowledge of the structures. A classical approach based on geological observations or soil mechanics investigations is often insufficient to characterize both the structures and the nature of subsurface materials. For several decades, near-surface geophysical methods have been integrated into a multidisciplinary strategy to improve the characterization of small-scale features of the subsurface. Electrical Resistivity Tomography (ERT) is a standard approach among these methods. This technique has several advantages, including easy deployment in the field and sensitivity to lithology, fluid contents, or chemistry. With this method, it is possible to detect and characterize the geometry of sliding surfaces on landslides and actives faults. It is also possible to set a permanent survey and obtain time-lapse images to describe temporal changes of resistivity within the subsurface and investigate dynamic processes, such as groundwater flows or soil moisture variations.
The ERT method consists of recording apparent resistivity data and inverting them to map the resistivity distribution at depth and to capture possible time changes. Many softwares, such as  Res2DInv, R2, or PyGimli, are now available to carry out the inversion. However, the quality assessment of the obtained models remains an open and challenging question. Indeed,  the robustness of the ERT results depends on factors such as the acquisition geometry, data error,  the resistivity contrast in the subsurface, the inversion procedure, and its parametrization. 
To overcome these limitations and allow a more accurate interpretation of the ERT models, we propose a new approach for assessing the reliability of ERT images. We propose a new algorithm called PySAM (Python Sensitivity Approach iMprovement) based on the open-source library PyGimli. This new tool provides relative and absolute error assessment on resistivity images from any ERT inversion software. We first illustrate the relevance of this new tool from synthetic tests associated with a well-contrained resisvity model. Next, we revisit the ERT image of the Topographic Frontal Thrust (TFT), a major active fault located in South Central Bhutan, and discuss its geometry which is a crucial parameter to discuss strain accommodation, and improve the seismic hazard assessment in Nepal, Bhutan, and northern India, one of the most densely populated regions.