## 1 Introduction  
Rapid industrialization has rendered soil and groundwater contamination a global environmental challenge. Non-aqueous phase liquids (NAPLs), characterized by their persistent degradation resistance, high toxicity, and complex migration patterns, pose severe threats to ecosystems and human health [1, 2]. In industrial sites, NAPLs frequently infiltrate subsurface environments through storage tank leaks, aging pipelines, or historical contamination incidents, forming concealed contaminant plumes [3]. Particularly in low-permeability clay sites, retarded contaminant migration leads to prolonged retention within the vadose zone, resulting in ambiguous delineation of pollution boundaries and significantly increased remediation difficulties [4]. Conventional investigation methods rely on borehole sampling, laboratory chemical analyses, and laboratory analysis. While these approaches have been widely used, they present several limitations when applied to complex geological settings, particularly low-permeability clay formations [5]. Direct sampling and laboratory analysis offer high accuracy but are invasive and labor-intensive and provide limited spatial coverage, often failing to capture the full extent of heterogeneous contamination plumes [6; 7]. Geophysical methods, though non-invasive, often suffer from low sensitivity to weak electrical anomalies associated with NAPLs, especially in claye soils where low contrast in resistivity and high background noise reduce detection reliability [8; 9]. Additionally, traditional inversion techniques typically require manual interpretation, which is subjective, time-consuming, and prone to inconsistencies. These issues become more pronounced in contaminated sites with complex stratigraphy, high heterogeneity, or shallow water tables [10]. As a cornerstone of electrical geophysical methods, ERT offers novel pathways for contaminant identification through analysis of subsurface electrical property variations [11; 12; 13; 14; 15]. Its physical foundation lies in resistivity contrasts between contaminants and background media: petroleum-based NAPLs typically exhibit significantly higher resistivity than groundwater (light oil resistivity ranges from 10\({}^{2}\)-10\({}^{4}\)\(\Omega\cdot\)m), while pore fluid composition changes induced by contamination (e.g., salt dissolution or organic enrichment) also generate distinctive resistivity responses [16; 17]. Recent studies have highlighted the importance of incorporating micro-scale mechanical behavior into resistivity modeling. For example, discrete element method (DEM) simulations offer valuable insights into the structural evolution of cohesive soils under different conditions. Work by [PERSON] et al. [18] provides a useful reference for understanding how cohesion impacts the rearrangement of soil particles, which in turn affects porosity and electrical transport mechanisms. Integrating such understanding supports a more comprehensive interpretation of resistivity changes in NAPL-contaminated sites. Compared to conventional electrical profiling, modern ERT systems employing multi-electrode arrays and automated data acquisition enable rapid acquisition of high-resolution 2D/3D resistivity profiles. These datasets, when reconstructed through inversion algorithms, facilitate visual characterizations of contaminant spatial distribution [19; 20]. Recent integration of intelligent algorithms and artificial intelligence (AI) technologies [21; 22] has substantially enhanced the efficiency and accuracy of resistivity data interpretation, driving the transformation of environmental geophysics toward intelligent and automated solutions [23]. The electrical response mechanisms of NAPL contamination in low-permeability sites present unique characteristics. While the low hydraulic conductivity of clayeyd media retards contaminant migration, it simultaneously enhances the correlation between electrical anomalies and contaminant concentration [24].