PyMERRY: a Python solution for improved interpretation of electrical resistivity tomography images

Abstract

Electrical Resistivity Tomography (ERT) is a widely used geophysical method for studying geological hazards, civil engineering, and environmental remediation. It provides information about the subsurface’s resistivity distribution by analyzing electrical data collected at the surface or in boreholes. However, interpreting ERT images can be complex due to the ambiguities in their resolution. To address this issue, we propose a post-processing method called PyMERRY (for Python iMprovement of Electrical Resistivity tomography ReliabilitY) to improve the reliability of ERT images. The PyMERRY code can be applied to any 2D resistivity model obtained from ERT inversion software. It computes a coverage mask that defines the domain well-constrained by both the data and the inversion process. It also evaluates the resistivity uncertainties in the ERT models. In addition to the sensitivity approaches, PyMERRY provides low and high resistivity values for all covered cells. Synthetic tests show that the approach is efficient and highlight the importance of resistivity contrasts, mesh selection, electrode spacing, and profile length in the reliability of ERT images. Compared to previous studies, using PyMERRY in south-central Bhutan allows a more accurate interpretation of ERT images. It confirms a high resistivity contrast across the Topographic Frontal Thrust and reveals the existence of small-scale variations.