Improving electrical resistivity tomography interpretation with Gray Level Co-Occurrence Matrix textural attributes.

Abstract

Electrical resistivity tomography (ERT) provides valuable subsurface structural information but often relies on inverted resistivity thresholds for interpretation. Inversion inherently produces smooth boundaries that can lead to interpretation ambiguity, especially in delineating clay boundaries critical for groundwater exploration. We demonstrate the application of Gray Level Co-Occurrence Matrix (GLCM) textural attributes to enhance ERT interpretation in heterogeneous sedimentary environments. We analyzed a 100 × 100 m 3D ERT survey using three GLCM attributes: Mean, Variance, and Entropy. GLCM Mean improved clay boundary definition by reducing the smoothing effects inherent in L2-norm inversion, enabling more precise delineation of clay lens morphology. GLCM Variance highlighted a potential recharge zone extending up to the surface that was ambiguous in the original inverted ERT results. GLCM Entropy provided enhanced contrast between clay units and groundwater reservoirs, improving discrimination between aquifer zones and confining layers. The integration of these GLCM attributes with traditional ERT interpretation demonstrates significant potential for improving geophysical interpretation in complex geological environments, particularly for groundwater resource assessment and management. This workflow establishes GLCM's value for extracting additional textural information from electrical resistivity data to aid subsurface characterization.