Neglected flow direction detection in landfill water cycle: precise characterization of leachate distribution through joint inversion of electrical resistivity and self-potential data

Abstract

The leakage of leachate is a crucial but often overlooked hydrological process in the landfill water cycle. The concealed leakage of leachate not only leads to soil and groundwater pollution in surrounding areas but also affects the distribution of water and the metabolism of organic matter within the landfill. To accurately quantify this concealed hydrological process, we propose a method for detecting leachate leakage based on joint inversion of multi-source geophysical exploration data. By integrating multiple geophysical exploration data (resistivity and self-potential information), we reconcile the spatial differences in different exploration data to improve the accuracy of leachate and its pollution plume imaging. Additionally, we introduce an efficient alternating iteration technique within the joint inversion framework to ensure convergence of separately inverted models toward similar spatial structures. Simulation results indicate that: (1) for the early detection of small-scale high-concentration leachate pollution, the proposed confidence-induced joint inversion framework (CI-JIF) improves precision by 15.6% compared to separate inversions. (2) for the detection of leachate long-term diffusion, CI-JIF accurately delineates the distribution of widespread high-concentration leachate, improving precision by 17.4% compared to separate inversions. Further on-site experiments demonstrate that CI-JIF can more accurately reconstruct the distribution of leachate.