Self-potential signal analysis to recognize sources of primary anomaly in a landslide: a novel approach

Abstract

Self-potential (SP) is a passive geophysical method highly sensitive to subsurface fluid flow, but its application has been traditionally limited by interpretation challenges and instrumentation constraints. In this study, we present a novel methodology for retrieving and processing SP signals during a 3D Electrical Resistivity Tomography (3D-ERT) survey over an active landslide in the Carnic Alps (Italy). Using a non-traditional sparse-gradient array and stainless-steel electrodes connected to new-generation FullWaver georesistivimeters, we demonstrate the feasibility of acquiring stable SP signals without non-polarizing electrodes. The SP data, recorded simultaneously across 23 autonomous units, were processed with custom MATLAB tools to produce time-lapse SP maps and identify groundwater flow patterns. The results highlight the presence of consistent SP anomalies, including “hat-shaped” features indicative of infiltration, and suggest the correlation of SP signals with geological structures and topography. We also applied the 2D Analytical Signal Amplitude (ASA) technique to delineate SP source zones. This approach enhances the utility of SP in landslide monitoring and hydrogeological investigations, particularly as a first-pass qualitative tool when quantitative instrumentation is unavailable. Our findings demonstrate the untapped potential of SP signals typically discarded in resistivity surveys.