## 2 Seismic Profile Signal Reconstruction Model
### Swin-ReshoUnet Model with Hierarchical Convolution and Regional Coordinate Attention Enhancement
#### 2.3.3 Residual Channel Attention Mechanism Module  
After incorporating hierarchical convolution in the encoder and replacing it with the ORCA module in the decoder, we observed gradient vanishing. To address this, a Residual Channel Attention Mechanism (RCAM) was introduced into the skip connections of Swin-Unet [28]. The residual structure resolves gradient vanishing via identity mapping, enabling the network to learn input-output differences rather than directly fitting complex transformations. In seismic signal reconstruction, this enhances high-frequency detail capture and mitigates performance degradation in deep networks. The standard residual block is formulated as  
\[\mathrm{y}=\mathrm{x}+\mathcal{F}(\mathrm{x};\Theta) \tag{25}\]  
where \(\mathrm{x}\) denotes the input feature map, \(\mathrm{y}\) the output feature map, and \(\mathcal{F}(\mathrm{x};\Theta)\) represents the residual function with \(\Theta\) as its parameters. Channel attention mechanism enhances feature representation by learning importance weights for each channel of the feature map, suppressing irrelevant channels and enhancing target channels [29]. In seismic profile reconstruction, this mechanism adaptively weights signal channels with different frequencies and phases, improving the signal-to-noise ratio of reconstructed signals. Traditional channel attention typically employs only global average pooling (GAP) to aggregate channel information, whereas RCAM introduces global max pooling (GMP) to form a dual-channel structure, capturing both global statistical features and salient characteristics of channels [30; 31; 32]. Specifically, global average pooling calculates the mean value of all spatial locations within a channel, reflecting the overall response intensity of the channel:  
\[z_{c}^{\mathrm{avg}}=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}x_{c}(i,j) \tag{26}\]  
Global max pooling highlights local peaks within channels by extracting the maximum values. \[z_{c}^{\mathrm{max}}=\max_{i=1}^{H}\max_{j=1}^{W}x_{c}(i,j) \tag{27}\]  
where \(x_{c}(i,j)\) denotes the feature value of the \(c\)-th channel at position \((i,j)\), and \(H\times W\) represents the spatial dimensions of the feature map. Finally, the dual-channel outputs are concatenated and processed through a Multi-Layer Perceptron (MLP) to generate channel weights:  
\[\mathrm{z}=[z^{\mathrm{avg}},z^{\mathrm{max}}]\in\mathbb{R}^{2\mathrm{C}} \tag{28}\]  
\[\mathrm{a}=\sigma(W_{1}(W_{0}\mathrm{z}))\in\mathbb{R}^{C} \tag{29}\]  
Here \(W_{0}\in\mathbb{R}^{\frac{C}{\tau}\times 2\mathrm{C}}\) and \(W_{1}\in\mathbb{R}^{C\times\frac{C}{\tau}}\) are MLP weight matrices, \(r\) is the dimensionality reduction ratio, and \(\sigma\) is the Sigmoid activation function that normalizes the weights to [0, 1]. The final channel attention output is  
\[\mathrm{x}_{\mathrm{attn}}=\mathrm{a}\odot\mathrm{x} \tag{30}\]  
where \(\odot\) denotes channel-wise multiplication. The schematic diagram of the channel attention mechanism is shown in Figure 9, Schematic diagram of channel attention mechanism. In the skip connections of Swin UNet, RCAM (Residual Channel Attention Mechanism) can be embedded either inside the residual blocks or within the skip paths, whose core structure is  
\[\mathrm{y}=\mathrm{x}+\mathcal{A}(\mathcal{F}(\mathrm{x};\Theta)) \tag{31}\]  
where \(\mathcal{F}(\mathrm{x};\Theta)\) is the residual function, and \(\mathcal{A}(\cdot)\) is the channel attention module that performs channel-wise weighting on residual features.