## 5 Establishment, Training, and Testing of Deep Learning Model  
UNet [34], proposed by [PERSON] et al., is a deep learning model widely applied in image segmentation tasks. Its core architecture consists of symmetrical encoder and decoder components, with the network structure illustrated in Figure 15a. Figure 15b details the structural components of UNet, where Conv represents the convolution operation, max pooling denotes the max pooling operation, BN stands for batch normalization, and ReLU and Sigmoid are activation functions. The encoder (primarily composed of Blocks 2-5) progressively extracts high-level semantic features through successive convolution and downsampling operations (max pooling), while reducing the spatial resolution to decrease the computational load. Blocks 2-5 are essentially convolutional blocks, each containing two or three convolutional layers. The convolution operation, characterized by weight sharing and translation invariance, can effectively extract local features of contamination data regardless of the spatial distribution of pollutants. The max pooling operation within  
Figure 14: Inversion images and label maps of four representative contaminated soil samples.  
Figure 13: Binary contamination zone label map.  
the convolutional blocks reduces feature map dimensions, gradually expanding the model's receptive field and generating a series of soil contamination feature maps at different scales. This expansion of the network's receptive field enhances its understanding of long-range dependencies in contamination data. The decoder (mainly comprising Blocks 7-10) gradually restores the image resolution through upsampling operations and achieves a refined fusion of multi-scale features by incorporating skip connections (red dashed arrows) that integrate features from different stages of the encoder. Blocks 7-10 progressively restore feature maps to their original dimensions through interpolation operations, followed by channel-wise concatenation with multi-scale feature maps extracted from the encoder and subsequent convolution operations.  
The entire network adopts a lightweight design, with an encoder identical to VGG [47] (specifically, VGG-16, as shown in Figure 15), featuring a relatively small parameter scale that maintains high computational efficiency and inference speed. Meanwhile, UNet ensures high segmentation accuracy by directly concatenating and fusing the encoder's low-level high-resolution features with the decoder's high-level low-resolution features through skip connections. Another important reason for selecting UNet in this study is its exceptional performance in small-sample learning scenarios, which stems from two key advantages: First, the skip connections and symmetrical architecture enable full utilization of multi-scale information from limited annotated data, reducing reliance on large-scale training samples through feature reuse--particularly suitable for the contaminated soil resistivity image data in this study. Second, UNet directly optimizes pixel-level predictions through end-to-end training, and when combined with data augmentation strategies, can further enhance the model's robustness to sample scarcity.