Abstract
Abstract This study aims to enhance the interpretation of subsurface features through the self-potential method, a geophysical approach that provides insight into geological processes essential for effective fault detection. Although prior studies emphasize quantitative self-potential analyses, there is limited qualitative modelling to simulate realistic fault responses. This study employs forward modelling analysis using synthetic data to evaluate subsurface model responses close to field data. By modelling three simple anomaly forms: sphere, cylinder, and plate, it has been observed that parameter variations affect the resulting self-potential anomaly response. Results indicate that increasing polarization angles and dipole moments enhance self-potential responses across models, while depth variations show an inverse relationship, with shallower sources producing stronger signals. The modelling results can be used as a reference to validate field data interpretation, where high anomaly values correlate with extended, deeper faults or high-permeability zones.
Published Version
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