## 3 Experimental Process and Results
### Experimental Results Based on Simulated Seismic Profile Signals  
First, the reconstruction results under a decimation ratio of 0.1 using synthetic seismic profile signals are presented. To enhance the persuasiveness of the results and provide a clearer visualization of the reconstruction effects, residual signals and F-K spectra are also included. Figure 13 Reconstruction results, residual signals, and corresponding F-K spectra under a decimation ratio of 0.1 illustrates the reconstruction results, residual signals, and F-K spectra of various networks at a decimation ratio of 0.1. This image, as well as the subsequent result images, can be enlarged to view the details. Here, WT denotes wavelet transform, while Unet and Unet++ are self-explanatory. For esthetic consistency in result presentation, TranUnet is abbreviated as TUnet, SwinUnet as SUnet, and Swin-ReshoUnet as SRUnet. These abbreviations are used consistently throughout the subsequent analyses. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Mathematical Expression** & **Explanation** \\ \hline SNR & \(\text{SNR}=10\cdot\log_{10}\left(\frac{\sum\limits_{i=1}^{N}s_{i}^{2}}{\sum \limits_{i=1}^{N}(s_{i}-s_{i})^{2}}\right)\) & Ratio of signal strength to noise strength, where \(s_{i}\) is the pre-denoising signal, \(s_{i}\) is the post-denoising signal, and \(N\) is the total signal length. \\ \hline PSNR & \(\text{PSNR}=10\cdot\log_{10}\left(\frac{P_{max}^{2}}{\text{MSE}}\right)\) & Discrepancy between the denoised and original signals, where \(P_{max}\) is the maximum signal value, and \(MSE\) is the mean squared error. \\ \hline RSME & \(\text{RMSE}=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}(s_{i}-s_{i})^{2}}\) & Difference between the denoised and original signals, where \(s_{i}\) is the pre-denoising signal, \(s_{i}\) is the post-denoising signal, and \(N\) is the total signal length. \\ \hline SSIM & \(\text{SSIM}(x,y)=\frac{\left(2\mu_{x}\mu_{y}+C_{1}\right)\left(2\sigma_{xy}+C_ {2}\right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+C_{1}\right)\left(\sigma_{x}^{2}+ \sigma_{y}^{2}+C_{2}\right)}\) & Structural similarity between denoised and original signals, where \(\mu_{x}\) and \(\mu_{y}\) are their mean values, \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) are their variances, \(\sigma_{xy}\) is their covariance, and \(C_{1}\) and \(C_{2}\) are constants for stability. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Explanation of evaluation metrics. Figure 12: The original data slice and its corresponding thinned slice. To enhance the credibility and reliability of experimental conclusions, SSIM is selected as the evaluation metric to quantify the similarity between reconstructed and original images. For the scenario with a decimation ratio of 0.1, the specific data of the four evaluation metrics are presented in Table 2, Evaluation indicators of each model when the decimation ratio is 0.1. Here, the confidence intervals of each index of the model SRUnet used in this study have been added, and they will remain consistent in the subsequent tables. As previously mentioned, this experiment involved over 200 training epochs. To intuitively analyze the performance of different networks, performance curves were plotted for each epoch. For esthetic clarity and to reduce noise, smoothing processing was applied using Origin software (2022) with a window size of 40 points, and the relevant results are shown in Figure 14, Changes in evaluation indicators for each model when the decimation ratio is 0.1. Next, the reconstruction results of seismic profile signals at a decimation ratio of 0.3 are presented. To fully validate the model performance and visually demonstrate the