## 4 Construction of Deep Learning Database
### Contamination Zone Analysis  
Figure 9 presents the measured apparent resistivity cross-section and calculated apparent resistivity cross-section obtained from high-density resistivity testing at a specific survey point. Figure 9a displays the original apparent resistivity contour map with resistivity values ranging from 5 to 25 \(\Omega\cdot\)m, exhibiting significant lateral heterogeneity. A distinct low-resistivity zone (<10 \(\Omega\cdot\)m) is observed in the central portion of the profile (horizontal position: 10-15 m; depth: 0-5 m), potentially caused by loose sediments or aquifers. In contrast, high-resistivity zones (>20 \(\Omega\cdot\)m), identified at both sides (positions: 0-5 m and 20-25 m), may correspond to dry compacted soil layers or bedrock outcrops. Figure 9b shows the computed apparent resistivity distribution, where data processing enhances the spatial characteristics of the low-resistivity anomaly (depth: 3-7 m). Compared with Figure 9a, these results minimize topographic and measurement configuration artifacts, more accurately reflecting the true electrical property variations of subsurface media. The inversion of the apparent resistivity test results from Figure 9 using the Surfer software 15 yielded the inversion-imaged chromatogram shown in Figure 10. After four iterations with an absolute error of 2.3%, the results clearly characterize the subsurface electrical structure of the study area. The results show a medium-resistivity (15-20 \(\Omega\cdot\)m) surface overburden layer at 0-1.5 m in depth. A distinct low-resistivity anomaly zone (5-10 \(\Omega\cdot\)m) is developed at 1.5-3.5 m in depth, which is most prominent at horizontal positions at 10-15 m along the survey line. Below 3.5 m in depth, the resistivity gradually increases to above 25 \(\Omega\cdot\)m, reflecting the undulating characteristics of the bedrock surface. Particularly noteworthy are two pronounced high-resistivity anomalies (>35 \(\Omega\cdot\)m) developed within the depth range of 1.5-4 m at horizontal positions 5-8 m and 18-22 m, with resistivity values significantly higher (approximately 2-3 times) than the surrounding media. The inversion results demonstrate excellent consistency with the apparent resistivity profile in Figure 10 while providing higher-resolution electrical structure details, offering reliable evidence for identifying potential water-bearing structures and delineating bedrock weathering interfaces. Inversion Image Clustering Based on K-Means Algorithm and Binary Contamination Area Label Map Generation  
First, the color intensity or values of the inversion image in Figure 10 were mapped to a single grayscale level to generate a single-channel grayscale image, as shown in Figure 11.  
Figure 9: Detection results; (**a**) apparent resistivity contour map of ERT profile; (**b**) orthorectified apparent resistivity contour map of ERT profile. In the grayscale image, the intensity of each pixel is represented by a single value ranging from 0 (black) to 255 (white) for an 8-bit grayscale image, reflecting the brightness of that pixel. The grayscale inversion image is subsequently input into the K-means algorithm [46] to achieve clustering of different soil regions. The algorithm primarily consists of three steps:  
(1) K-value selection: Determine the number of clusters (K) for classification. (2) Centroid initialization: Randomly select K data points (pixels) as initial centroids. (3) Iterative optimization: First assign each data point (pixel) to its nearest centroid. Then, calculate new centroids for each cluster. Repeat these steps until the centroid positions no longer change significantly or the preset maximum number of iterations is reached. Finally, examine the clustering results, and manually adjust the K-value to obtain optimal clustering outcomes. The clustering results for the example image in Figure 11 are shown in Figure 12. It can be clearly observed that multiple connected regions have formed in the resistivity inversion image, which contain potential target contamination zones.