## 3 Experimental Process and Results
### Experimental Results Based on Office Seismic Profile Signals
Over 200 training epochs on real data enabled performance curve construction from epoch records. Curves were smoothed with a 40-point window in Origin to retain trends and clarity, shown in Figure 22, Changes in evaluation indicators for each model when the decimation ratio is 0.3. Using the official seismic dataset, extreme sparse data reconstruction (decimation ratio 0.5) was evaluated via a multidimensional framework: seismic profiles assess stratigraphic interface accuracy and structural feature restoration; residual maps quantify pixel-wise amplitude errors to characterize distributions; F-K spectra analyze energy retention and noise suppression in the frequency-wavenumber domain. Model abbreviations align with synthetic experiments for cross-dataset comparability. This extreme sparsity test rigorously validates the model's data loss resilience and high-frequency recovery efficiency in real exploration, with network comparisons shown in Figure 23, Reconstruction results, residual signals, and corresponding F-K spectra under a decimation ratio of 0.5. \begin{table}
\begin{tabular}{c c c c c} \hline
**Network** & **PSNR** & **SNR** & **RMSE** & **SSIM** \\ \hline WT & 22.137 & 11.425 & 4.137 & 0.820 \\ Unet & 24.193 & 13.452 & 2.516 & 0.849 \\ UNet++ & 23.527 & 13.933 & 2.673 & 0.832 \\ TUnet & 25.924 & 14.562 & 1.772 & 0.874 \\ SUnet & 26.103 & 14.273 & 1.803 & 0.883 \\
**SRUnet** & **28.101**\(\pm\) 0.128 & **15.135**\(\pm\) 0.141 & **0.957**\(\pm\) 0.032 & **0.910**\(\pm\) 0.005 \\
[27.973, 28.229] & [14.994, 15.276] & [0.925, 0.989] & [0.905, 0.915] \\ \hline \end{tabular}
\end{table} 
Table 6: Evaluation indicators of each model when the decimation ratio is 0.3. Figure 23: Reconstruction results, residual signals, and corresponding F-K spectra under a decimation ratio of 0.5. Figure 22: Changes in evaluation indicators for each model when the decimation ratio is 0.3. To solidify empirical validity, extreme sparse reconstruction (decimation ratio 0.5) was conducted using the official seismic dataset, employing gold-standard metrics--PSNR, SNR, RMSE, SSIM--to quantify similarity to original profiles. Measured data are shown in Table 7, Evaluation indicators of each model when the decimation ratio is 0.5. Over 200 real-data epochs constructed performance curves from training records, characterizing dynamics under high sparsity; curves were smoothed with a 40-point window in Origin to retain trends and reduce noise, with results shown in Figure 24, Changes in evaluation indicators for each model when the decimation ratio is 0.5. Model naming followed synthetic experiment conventions for cross-comparison, and this design systematically validated high-frequency information capture and noise robustness via metric analysis under extreme data loss. To verify the impact of each model in the improved network on the overall network, we conducted an ablation experiment in the official dataset when the dilution ratio was 0.5. It can be seen that when individual modules are added, the performance will undergo certain changes, which are marked with arrows in the table. When two or more modules are added, the performance begins to optimize. The table of the ablation experiment is shown in Table 8.  
Thus, we completed the seismic profile signal reconstruction research using the improved Swin-ReshoUnet on both synthetic and official datasets, and the following elaborates on the research results. The study focuses on modifying the encoder and decoder structures of SwinUnet: hierarchical convolutions are inserted before the W-MSA attention module in the encoder to capture more multi-scale features through convolutional operations; in the decoder, the innovative ORCA attention mechanism replaces the original W-MSA and SW-MSA modules, enhancing feature extraction efficiency while significantly reducing model parameters. To demonstrate that the proposed model not only enhances performance but also maintains high efficiency, Table 9 provides the parameter count and GPU usage of each model.