## 4 Conclusions  
This study proposes a novel network architecture for seismic profile signal reconstruction. In contrast to traditional reconstruction networks and conventional neural architectures, the Swin-ReshoUnet model is meticulously constructed upon the SwinUnet framework through the integration of three key enhancements: (i) hierarchical convolution modules for adaptive multi-scale feature extraction, (ii) the ORCA (Overall Regional Coordinate Attention) mechanism to model long-range structural dependencies, and (iii) residual channel attention modules to emphasize geologically salient features. The network's core advantages lie in the following: significantly enhancing model representation capabilities for complex visual tasks through multi-dimensional global context capture and feature enhancement mechanisms; adopting channel grouping strategies to reduce feature map \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & \multicolumn{3}{c}{**Modules**} & **PSNR \(\uparrow\)** & **SNR \(\uparrow\)** & **RMSE \(\downarrow\)** & **SSIM \(\uparrow\)** \\ \cline{2-7}
**Name** & **H-CNN** & **ORCA** & **RCAM** & & & & \\ \hline A & \(\times\) & \(\times\) & \(\times\) & 25.029 & 13.783 & 1.801 & 0.837 \\ B & \(\surd\) & \(\times\) & \(\times\) & 25.183 & 13.892 & 1.817 \(\uparrow\) & 0.840 \\ C & \(\times\) & \(\surd\) & \(\times\) & 24.982 \(\downarrow\) & 13.815 & 1.793 & 0.839 \\ D & \(\times\) & \(\times\) & \(\surd\) & 25.056 & 13.751 \(\downarrow\) & 1.789 & 0.836 \(\downarrow\) \\ E & \(\surd\) & \(\surd\) & \(\times\) & 25.371 & 13.968 & 1.725 & 0.843 \\ F & \(\surd\) & \(\times\) & \(\surd\) & 25.693 & 14.082 & 1.652 & 0.849 \\ G & \(\times\) & \(\surd\) & \(\surd\) & 25.487 & 13.926 & 1.703 & 0.845 \\ H & \(\surd\) & \(\surd\) & \(\surd\) & 26.993 & 14.326 & 1.135 & 0.883 \\ \hline \hline \end{tabular}  
* An upward arrow indicates that the larger the indicator, the better; a downward arrow indicates that the smaller the indicator, the better. \(\times\) represents none, and \(\surd\) represents yes.  
\end{table} 
Table 8: The influence of each module on the model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Network** & **Parameter** & **GPU** \\ \hline WT & 32.03 M & 125.87 M \\ Unet & 31.04 M & 135.28 M \\ UNet++ & 18.21 M & 209.28 M \\ TUnet & 91.4 M & 1.93 G \\ SUnet & 19.83 M & 1.34 G \\ SRUnet & 18.23 M & 780.56 M \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Parameters of each model and GPU usage. computational complexity while preserving feature diversity; and leveraging residual attention mechanisms to effectively mitigate gradient vanishing issues in deep network training. Experimental results not only present reconstruction outcomes of different networks but also enhance persuasiveness through residual signal analysis and F-K spectral comparisons. Four quantitative metrics--PSNR, SNR, RMSE, and SSIM--are introduced for systematic performance evaluation. Data show that Swin-ReshoUnet demonstrates significant advantages in seismic profile reconstruction, outperforming comparative networks in all metrics. Although all models exhibit performance degradation with increasing sparsity, Swin-ReshoUnet maintains optimal performance at the same sparsity ratio, validating its robust reconstruction capability. Notwithstanding these strengths, the current study has limitations that warrant attention. First, the sparsity ratios tested (0.1, 0.3, 0.5) are relatively moderate; performance under extremely high sparsity (\(>\)0.7) or non-uniform sparse patterns (e.g., clustered missing data) remains unvalidated. Second, evaluation relies primarily on general image quality metrics, lacking in-depth validation against geological interpretation needs (e.g., reservoir continuity, log correlation). Third, the model's robustness in handling seismic data with strong noise or complex fault structures requires further verification.