## 3 Experimental Process and Results
### Experimental Results Based on Simulated Seismic Profile Signals
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **PSNR** & **SNR** & **RMSE** & **SSIM** \\ \hline WT & 25.013 & 12.656 & 3.352 & 0.874 \\ Unet & 26.821 & 14.074 & 1.937 & 0.905 \\ UNet++ & 26.933 & 14.534 & 1.826 & 0.897 \\ TUnet & 28.451 & 15.342 & 1.102 & 0.914 \\ SUnet & 28.172 & 15.462 & 1.035 & 0.911 \\
**SRUnet** & **30.495**\(\pm\) 0.108 & **16.436**\(\pm\) 0.123 & **0.894**\(\pm\) 0.024 & **0.937**\(\pm\) 0.003 \\  & [30.387, 30.603] & [16.313, 16.559] & [0.870, 0.918] & [0.934, 0.940] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation indicators of each model when the decimation ratio is 0.1. Figure 14: Changes in evaluation indicators for each model when the decimation ratio is 0.1. Figure 13: Reconstruction results, residual signals, and corresponding F-K spectra under a decimation ratio of 0.1.  
reconstruction quality, in addition to presenting the reconstruction results, residual signals and F-K spectral analysis are simultaneously introduced. The reconstruction results, residual signal distributions, and F-K spectral characteristics of each network at a decimation ratio of 0.3 are shown in Figure 15, Reconstruction results, residual signals, and corresponding F-K spectra under a decimation ratio of 0.3. For the experimental scenario with a decimation ratio of 0.3, detailed data of the four evaluation metrics are presented in Table 3, Evaluation indicators of each model when the decimation ratio is 0.3. As previously described, this experiment completed over 200 rounds of iterative training (epoch). To clearly demonstrate the dynamic performance of different networks, performance change curves were plotted by recording training data of each epoch. To optimize visualization, enhance data readability, and reduce fluctuation interference, the curves were subjected to smoothing filtering using Origin software with a window size of 40 points. The relevant results are shown in Figure 16, Changes in evaluation indicators for each model when the decimation ratio is 0.3. For the extreme sampling scenario with a decimation ratio as high as 0.5, the seismic profile reconstruction performance of each network is presented below. To comprehensively evaluate the model's recovery capability under severe data loss conditions, the results include three visualization dimensions--the intuitive restoration effect of the reconstructed profile, the point-wise deviation distribution of residual signals, and the energy aggregation characteristics of F-K spectra--as shown in Figure 17 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 \hline
**Network** & **PSNR** & **SNR** & **RMSE** & **SSIM** \\ \hline WT & 21.351 & 11.362 & 3.954 & 0.835 \\ Unet & 23.375 & 13.466 & 2.374 & 0.872 \\ UNet++ & 24.035 & 13.526 & 2.742 & 0.881 \\ TUnet & 26.053 & 14.462 & 1.543 & 0.894 \\ SUnet & 26.351 & 14.363 & 1.337 & 0.898 \\
**SRUnet** & **29.232**\(\pm\) 0.115 & **15.301**  
Notably, performance differentiation among different networks becomes more pronounced under high decimation ratios. Through the joint analysis of residual maps and spectral plots, the differences in structural preservation capability among various models can be clearly observed. For the rigorous experimental scenario with a decimation ratio of 0.5, detailed data of the four evaluation metrics are compiled in Table 4, Evaluation indicators of each model when the decimation ratio is 0.5. As previously stated, this experiment involved over 200 rounds of iterative training (epoch). To multi-dimensionally present the dynamic performance evolution of different networks, performance fluctuation curves were constructed by collecting training data from each epoch. Considering the intensified data fluctuations under high decimation ratios, a 40-point window moving average filtering was applied to the curves using Origin software. This processing effectively suppresses random noise while preserving trend characteristics,