RANSAC-based regression approach for outlier detection in electrical resistivity tomography data

Electrical Resistivity Tomography (ERT) is a widely used geophysical method for subsurface investigations, applied in engineering, geology, and environmental studies. ERT data often contain outliers caused by measurement errors, electrode malfunctions, or environmental noise, which can significantly affect the inversion results and lead to misinterpretations of subsurface structures. Traditional outlier detection techniques, such as manual data filtering or statistical thresholding, often lack robustness and adaptability to complex datasets. In this study, we propose a novel automated approach utilizing the RANdom SAmple Consensus (RANSAC) algorithm to identify and correct erroneous measurements in ERT datasets. The proposed approach applies RANSAC with polynomial regression to each depth horizon independently, allowing for improved detection of local anomalies while preserving the overall resistivity distribution. To validate the methodology, we generate synthetic ERT data using the pyGIMLi library and introduce controlled noise to simulate realistic measurement errors. Additionally, the method is tested on the field dataset, enabling an evaluation of its practical performance under actual survey conditions. The performance of the proposed method is assessed by comparing resistivity distributions obtained from filtered datasets with the original synthetic model free of errors. The results demonstrate that RANSAC-based filtering enhances the quality of ERT data, leading to more accurate inversion outcomes. The proposed approach provides a robust and automated solution for geophysical data processing, reducing the influence of erroneous measurements without requiring manual intervention. The methodology is highly adaptable and can be applied to various geophysical techniques and other geological datasets where outlier detection and correction are crucial.