A Comparison of Array Configurations in Python-Based Software for ERT Data in Shallow Hazard Detection

Abstract

Electrical Resistivity Tomography (ERT) is a widely used geophysical technique for imaging subsurface resistivity variations, providing critical insights for geological engineering and hazard assessment applications. While open-source inversion tools such as BERT and PyGIMLi offer accessible solutions for geoelectrical modeling, their comparative performance across different electrode configurations and noise conditions remains underexplored. This study evaluates the effectiveness of these software packages in reconstructing subsurface anomalies related to cavity detection and landslide assessment. Four commonly used electrode configurations—dipole–dipole, Schlumberger, Wenner-Alpha, and Wenner-Beta—were tested on two synthetic models designed to simulate real geological conditions: one representing cavity detection and the other simulating a landslide scenario. Inversions were conducted under both ideal conditions and with synthetic noise to assess their robustness against measurement uncertainties. Results indicate that while all configurations successfully identified major subsurface features, the dipole–dipole array provided the highest resolution for detecting small-scale anomalies. BERT demonstrated superior accuracy under ideal conditions, while PyGIMLi showed consistent performance across multiple configurations, particularly in resolving smaller features under noisy conditions. These findings emphasize the importance of selecting appropriate electrode configurations to enhance imaging accuracy and ensure reliable geo-electrical data interpretation. This study highlights the robustness of open-source geophysical software for subsurface investigations and provides practical insights into optimizing geoelectrical survey configurations for shallow hazard detection.