Electrical Resistivity Tomography, Induced Polarization and Unconventional Self-Potential Techniques Applied to Landslide Imaging

This study describes an integrated landslide monitoring program in the landslide-prone Tizzano Val Parma region (Italy) using traditional and innovative geoelectrical techniques, namely, electrical resistivity tomography (ERT), induced polarization (IP) and self-potential (SP) methods. Both the conventional fixed-base and the unconventional sparse gradient array configurations were adopted. The use of analytic signal amplitude (ASA) technique enabled for a better recognition of primary SP anomaly sources, for the sparse gradient arrays, providing useful insights in delineating areas of interest. The region faces recurrent landslides due to geological and geomorphological factors, leading to high hydrogeological instabilities and environmental risks. Borehole stratigraphy reveals a complex lithology of sandstones, clayey marl, and coarse materials. The ERT–IP survey provides insights into various landslide types, identifying distinct domains including complex quiescent, active and undetermined landslides. Active fault evolution is observed, indicating potential risk zones. Sparse gradient SP monitoring captures short-term electrokinetic anomalies and stable long-term variations between 2022 and 2023. Both fixed-base and sparse gradient SP monitoring highlight displacement anomalies towards the valley, suggesting potential landslide movements. Interpretation of SP maps allowed to identify preferential water flow directions which denote probable accentuating risks during intense rainfall events. This study emphasizes the significance of integrated geoelectrical monitoring for early landslide detection. Non-conventional and conventional SP arrays provide insights into anomaly repeatability and stability. Comparison of ERT–IP results with borehole information enables the extrapolation of geological characteristics, yielding a holistic understanding of subsurface structures and potential risk zones. Integration of these methodologies contributes to effective landslide management, underscoring the dynamic nature of landslide-prone regions and the necessity for ongoing risk assessment and monitoring.