## 6 Conclusions  
Focusing on the requirements for precise identification and dynamic monitoring of non-aqueous phase liquid (NAPL) contamination in low-permeability clay sites, this study systematically addresses the limitations of traditional detection methods in complex  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & _mIoU (\%)_ & _f1 (\%)_ \\ \hline UNet & 86.58 & 76.23 \\ DeepLabV3 & 84.77 & 73.53 \\ DeepLabV3+ & 84.85 & 73.91 \\ PSPNet & 84.38 & 74.70 \\ LinkNet & 85.66 & 75.71 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of four representative semantic segmentation models.  
Figure 18: Visualization of UNet segmentation and localization results.  
geological environments through methodological innovation, technical integration, and engineering validation. The findings offer innovative solutions for precise identification and remediation of contamination in complex geological settings. The core findings are summarized as follows:  
(1) A unified technical system integrating high-density ERT and an improved UNet deep learning model was developed, forming a complete workflow from data acquisition to intelligent interpretation. This effectively resolves the challenges of weak electrical anomalies and high noise in clay-rich environments, enabling an accurate reconstruction of contamination plume morphology.  
(2) The improved UNet model, incorporating multi-scale feature fusion and skip connections, attained an mIoU of 86.58%, an Acc of 99.42%, a Pre of 75.72%, an Rec of 76.80%, and an f1 of 76.23% in contamination zone segmentation. Compared to manual interpretation in traditional geophysical exploration, this approach enhances efficiency by over 80% while significantly reducing misjudgment rates.  
(3) Time-lapse resistivity imaging and case validation at a contaminated site demonstrate the method's capability to dynamically track contaminant migration with positioning errors below 0.5 m. This provides spatial decision-making support for remediation, reducing costs by 60% compared to conventional borehole sampling and driving the transition toward non-destructive, intelligent environmental detection.  
Investigation, writing--original draft preparation, data curation, methodology, software, and formal analysis: M.G.; conceptualization, review and editing, supervision, and validation: X.Z. and Y.X. All authors have read and agreed to the published version of the manuscript.  
Much of the work described in this paper was supported by the National Natural Science Foundation of China under grant no. 42372335 and the AI-Driven Research Paradigm Reform and Disciplinary Advancement Program of the Shanghai Municipal Education Commission under grant no. kz002302050157.  
Not applicable.  
The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author.  
The authors thank all reviewers for their great help with this article.  
Authors [PERSON] and [PERSON] was employed by the company Baowu Group Environmental Resources Technology Limited Company. Authors [PERSON] was employed by the company Shanghai Baofa Environmental Science Technology Limited Company. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.