Geo-constrained clustering of resistivity data revealing the heterogeneous lithological architectures and the distinctive geoelectrical signature of shallow deposits

For all applications, subsurface models should be consistent with all available geological and geophysical knowledge. Current practices for synergistic interpretation of geological and geophysical approaches often rely on purely qualitative comparisons, resulting sometimes in inconsistent findings. This study introduces a procedure for a statistical and geo-constrained clustering of electrical resistivity data derived from Electrical Resistivity Tomography (ERT) to address this gap, providing a quantitative parameterization for site-specific geoelectrical signatures of litho-stratigraphic architectures. Seventeen boreholes and three ERT surface profiles were employed to link geophysical inversion results to geological criteria. Core samples allowed grain size analyses, while geological-statistical clustering of electrical resistivity, driven by the observation of stratigraphic contacts in drilled boreholes, established a parametric relationship between geology and geophysics. The iterative clustering procedure, utilizing a classification algorithm, geological boundary constraints, and granulometric analyses, discriminated six distinct lithological clusters, capturing the lateral and vertical heterogeneity of shallow deposits. Subsequent spatial grouping of anthropogenic materials delineated lithological structures and facilitated the classification and identification of filling materials, silty sands, clayey sands, and clays and silts, each exhibiting distinct resistivity variations. The geo-driven geophysical clustering revealed complex lithological structures, especially paleo-channels, capturing their unique geoelectric footprints. The iterative clustering of geo-constrained resistivity data emerges as a powerful tool for subsurface exploration, contributing significantly to understanding lithological heterogeneity, quantifying statistically-based geoelectrical parametrization of shallow sediments, and evaluating the resistivity signature of different deposits. By bridging the gap between geology and geophysics, this data-driven approach establishes a benchmark for future applications. For instance, in the context of contaminated sites, it can be applied to identify pollutants versus geological heterogeneities.