Application of Self-Potential Monitoring in Landslide Early Warning: A Physical Simulation Study

Despite the widespread deployment of inclinometers and GPS, an engineering gap remains for a low-cost, seepage-sensitive landslide early-warning technique. To explore the application of self-potential (SP) in landslide monitoring and early warning, a series of physical simulations were conducted, focusing on slope rainfall and slope cracking conditions. The self-potential signals were monitored using a custom-built STM32-based acquisition system, which provided continuous, real-time data with minimal noise. The relationship between self-potential signals and internal changes within the landslide body was analyzed, revealing that SP signals are highly sensitive to seepage, saturation, and structural changes within the slope. During slope rainfall simulations, the self-potential signals responded rapidly to changes in rainfall intensity, capturing the dynamic nature of seepage and saturation changes. A dynamic early-warning model was developed based on statistical methods, including sliding t-tests/Pettitt mutation tests and Mahalanobis distance test, to detect early signs of landslide instability. The model successfully identified significant changes in SP signals that corresponded to the onset of landslide movement, demonstrating the potential of self-potential for real-time landslide monitoring and early warning. This study highlights the effectiveness of self-potential monitoring in detecting early signs of landslide instability and suggests that SP signals can be a valuable addition to existing landslide monitoring systems.