## 2 Seismic Profile Signal Reconstruction Model
### Swin-ReshoUnet Model with Hierarchical Convolution and Regional Coordinate Attention Enhancement
#### 2.3.2 Overall Regional Coordinate Attention Module  
In the task of seismic profile data reconstruction, the ORCA (Overall Regional Coordinate Attention) module is specifically designed to address the key limitations of traditional  
Figure 7: Hierarchical convolution structure diagram. convolutional neural networks in this domain: their fixed receptive fields struggle to adapt to the multi-scale geological features (e.g., thin layers vs. broad formations) in seismic signals, and their limited capacity to model long-range structural correlations (e.g., fault continuity and stratum trends). Traditional methods often struggle to simultaneously capture global information in both height and width dimensions, restricting feature representation capabilities. To this end, the ORCA module performs global average pooling and max pooling along the height and width directions, respectively, to effectively capture multi-dimensional global information and enhance the comprehensiveness of feature extraction [23]. Moreover, considering the importance of disparities of features across different channels and spatial locations, the ORCA module introduces an attention mechanism. By generating attention maps for height and width dimensions, it weights the input feature maps to enhance key features and suppress trivial ones [24; 25]. To tackle the computational complexity and memory overhead from high-resolution images and large-scale data, the ORCA module employs grouped processing, dividing input feature maps by channel count to reduce per-group computation while maintaining feature diversity. By integrating multi-dimensional global information with attention mechanisms, ORCA not only leverages global context from both height and width but also boosts the model's ability to capture complex geological structures in seismic profiles [26], significantly improving reconstruction accuracy and robustness. This design enables the ORCA module to excel in seismic profile reconstruction, efficiently handling multi-scale features for high-quality results. The architecture of the ORCA module is illustrated in Figure 8 ORCA module structure diagram. The ORCA module enhances feature representation by capturing global information along both height and width dimensions of the feature map, generating corresponding attention maps to weight the input feature maps. This design enables ORCA to focus on critical regions, improving the attention mechanism's effectiveness and ultimately achieving higher-quality feature extraction and model performance [27]. The module consists of five main components, which are denoted by different colors in the structure diagram. The first component is feature map grouping, represented by the yellow box in the diagram. For the input feature map,  
\[\text{X}\in\mathbb{R}^{B\times\text{C}\times H\times W} \tag{12}\]  
The input feature map is divided into \(G\) groups along the channel dimension, with each group containing \(C/G\) channels, where \(B\) denotes the batch size, \(C\) the number of  
Figure 8: ORCA module structure diagram. channels, and \(C\), \(W\) the height and width of the feature map.