## 2 Seismic Profile Signal Reconstruction Model
### Technical Workflow for Seismic Profile Signal Reconstruction  
This study proposes a seismic profile signal reconstruction method based on an improved Swin-Unet, validated through a systematic technical roadmap to demonstrate its scientific validity and engineering application value. The roadmap comprises a complete closed loop from data processing to model construction, training optimization, and performance evaluation:  
1. To verify model generalization, an official dataset and simulated datasets are combined to construct a multi-source validation framework. The preprocessing stage simulates seismic trace loss in real acquisition scenarios via decimation, enhances model robustness through slicing and data augmentation, and finally divides the dataset into training/validation/testing sets at an 8:1:1 ratio to ensure data independence and representativeness.
2. Systematic improvements are implemented on the Swin-Unet framework to propose an innovative network model: (a) Encoder Enhancement: In the Swin-Transformer module, a hierarchical convolution structure is innovatively proposed to the front end of W-MSA operations. Composed of parallel branches with different-scale convolution kernels, this structure captures local geological features at multiple scales through multi-receptive-field feature extraction, significantly enriching feature representation and multi-scale characterization capabilities. (b) Decoder Optimization: The traditional W-MSA/SW-MSA mechanisms in Swin-Transformer modules are replaced by an ORCA (Overall Regional Coordinate Attention) module. By constructing cross-window long-range dependencies via regional coordinate mapping matrices, this module reduces computational complexity while enhancing the spatial localization accuracy of deep semantic features, providing high-resolution feature representations for seismic profile reconstruction. (c) Gradient Degradation Mitigation: A dual-channel Residual Channel Attention Mechanism (RCAM) is integrated into skip connections, and the traditional ReLU activation function is replaced with SiLU to enhance nonlinear expressiveness. This design maintains feature extraction capability while alleviating gradient degradation, significantly improving reconstruction performance in complex signal scenarios.
3. Following data standardization and 8:1:1 dataset division, the model is optimized using a combined MSE-SSIM loss function, distributed training strategies, and hyperparameter tuning. Specifically, the batch size is set to 16; this choice balances GPU memory constraints and training stability, as larger batches caused memory overflow, while smaller batches led to unstable gradient updates for seismic signal batches. For regularization, we adopted L2 regularization with a weight decay of \(1\times 10^{-5}\) in the Adam optimizer to mitigate overfitting--critical for avoiding memorization of noise in seismic signals. Early termination criteria were implemented: training stops if the validation loss does not improve for 15 consecutive epochs, balancing training sufficiency and computational efficiency. Cross-validation is employed to enhance generalization, with final performance evaluation conducted on an independent test set.
4. Model performance is evaluated via a combination of qualitative visualization and quantitative metrics: four indicators (PSNR, SNR, RMSE, SSIM) are introduced to quantify reconstruction quality. Comparative experiments show that the proposed model significantly outperforms traditional methods in reconstructing complex geological structures, particularly in noise robustness and structural preservation capabilities. The technical roadmap is illustrated in Figure 4 Technical roadmap flowchart for seismic profile signal reconstruction.