Structurally coupled cooperative inversion of ERT and SRT data with sequential cluster analysis for integrated imaging of an Andean rock glacier

Integrating diverse geophysical datasets is paramount for characterizing complex geological formations, such as relict rock glaciers, where inherent heterogeneities pose significant challenges. This study showcases the efficacy of a structurally coupled cooperative inversion (SCCI) framework applied to electrical resistivity (ERT) and seismic refraction tomography (SRT) data, validated through a detailed synthetic modeling phase designed to test the method’s stability, sensitivity to structural information, and practical applicability in heterogeneous near-surface environments. Both forward and inverse modelling phases are conducted using unstructured triangular meshing. This method enables numerical stability for high topographic reliefs and local refinements (e.g., near sensors). For enhanced subsurface interpretation, we implemented Mean shift clustering, facilitating an automated analysis and seamless integration of the ERT and SRT inversion outputs. The subsequent application of SCCI to field data, augmented by the sequential clustering stage, successfully recovered expected subsurface anomalies and revealed intricate layering. Critically, this integrated SCCI approach significantly advanced the complexity of subsurface structures that could be accurately delineated, offering a more profound understanding of the relict rock glacier’s architecture and properties. Together, these components provide a coherent workflow that advances structurally coupled geophysical inversion and offers a reproducible basis for multi-method subsurface characterization.