## 2 Seismic Profile Signal Reconstruction Model  
### Base Network  
This study employs Swin-Unet as the foundational architecture, which deeply integrates the hierarchical feature fusion advantages of classic UNet with the efficient global modeling capability of Swin Transformer. Swin Transformer achieves a balance between local window partitioning and cross-window dependency modeling through the synergistic design of Window-Based Multi-Head Self-Attention (W-MSA) and Shifted Window-Based Multi-Head Self-Attention (SW-MSA), enabling hierarchical capture of multi-scale contextual information [13]. As a classic framework in image signal processing, UNet's symmetric encoder-decoder architecture extracts multi-scale semantic features via convolutional downsampling (encoder), restores resolution through deconvolutional upsampling (decoder), and directly fuses shallow spatial details with deep semantic features via skip connections, establishing a hierarchical feature representation system [14]. Building upon UNet's encoder-decoder framework and skip connection mechanism, Swin-Unet embeds Transformer modules into the feature extraction stage, forming a composite processing pipeline of \"local feature extraction--global contextual modeling--multi-scale feature fusion\". This architecture not only inherits UNet's sensitivity to detailed features but also enhances long-range dependency modeling through Transformer's attention mechanisms, offering a technically sound solution for seismic profile reconstruction that balances accuracy and efficiency. The overall architecture is illustrated in Figure 1 Overall architecture diagram of Swin-Unet. The overall architecture of Swin-Unet in this study consists of four components: encoder, bottleneck, decoder, and three groups of skip connections. The fundamental computational unit is the swin transformer block. In the encoder stage, spatial partitioning of seismic profile images is performed to convert input data into Sequence Embeddings, with a block size set to \(4\times 4\) pixels [15]. This partitioning strategy calculates the feature dimension of a single image block as \(4\times 4\times 3\) (number of channels) = 48 dimensions, thereby transforming the original \(W\times H\times 3\) input data into:  
\[\frac{W}{4}\times\frac{H}{4}\times 48 \tag{1}\]Furthermore, the linear embedding layer is applied to project the input into any dimension:  
\[\frac{W}{4}\times\frac{H}{4}\times C \tag{2}\]  
The transformed patch tokens are processed through multi-stage Swin Transformer modules and Patch Merging layers to generate Hierarchical Feature Representations. The Patch Merging layer achieves feature dimension expansion via downsampling operations, while Swin Transformer modules are responsible for deep feature representation learning. The Transformer decoder, inspired by the U-Net architecture, is composed of Swin Transformer modules and Patch Expanding layers. Cross-scale fusion of Context Features and multi-scale encoder features is achieved via Skip Connections, effectively compensating for spatial information loss during downsampling. In contrast to the downsampling mechanism of Patch Merging, the Patch Expanding layer performs upsampling operations, achieving 2\(\times\) resolution enhancement through neighborhood dimension reshaping. The final Patch Expanding layer completes 4\(\times\) upsampling to restore the input resolution (\(W\times H\)), and pixel-level segmentation predictions are generated via a Linear Projection Layer. The core architectural component of Swin-Unet is the Swin Transformer, a Transformer variant specifically optimized for visual tasks. Its key innovation lies in the synergistic design of Window-Based Multi-Head Self-Attention (W-MSA) and Shifted Window-Based Multi-Head Self-Attention (SW-MSA), which enables efficient global contextual modeling while maintaining linear computational complexity [16]. Specifically, W-MSA partitions  
Figure 1: Overall architecture diagram of Swin-Unet.