In Situ Identification of Primary Groundwater Seepage and Transport Pathways in Fracture Networks in Karst Regions Using Self-Potential Probability Tomography

Karst aquifers supply drinking water for about a quarter of the global population. Despite their often shallow occurrence, delineating these aquifers remains challenging due to the fine-scale nature of conduits and pore spaces in the shallow karst zone, compounded by highly complex spatial distributions. This study integrates self-potential probability tomography (SPPT) with electrical resistivity tomography (ERT) and borehole data to identify and characterize preferential flow paths within a heterogeneous karst aquifer in Guangxi, China. Seasonal surface and borehole self-potential datasets acquired in March, August, and December effectively captured the spatiotemporal hydrological dynamics. Results indicate that as the groundwater level drops from 13.7 m to 25.5 m, low-potential anomaly zones and the maximum charge occurrence probability (COP) shift toward the center of the depression. Concurrently, self-potential intensity increases with depth, particularly within areas of well-developed karst fracturing. By synthesizing SPPT, ERT, and borehole constraints, a 3D preferential flow channel model was constructed, enabling the in situ identification of groundwater flow pathways in shallow karst aquifers. This work provides a reliable technical framework for the precise mapping and sustainable management of groundwater resources in karst regions.