## 4 Experimental Results and Analysis
### Ablation Studies  
In this subsection, we first investigate the impact of iteration \(T\) in our FID-N network. As shown in Table 4, we found consistent performance improvements as the \(T\) value increased for synthetic data and forward model data. Considering the trade-off between accuracy and speed, we adopted \(T=9\) to construct the FID-N, which was used in the experiments. Second, we investigated the impact of our two-path information distillation block (IDB) and single-scale feature extraction. As shown in Table 5, we can see that our two-path IDB worked consistently better than single scaling. Furthermore, we evaluated the impact of the NSCT decomposition level on our FID-N's performance. Since there were more high-frequency levels of decomposition and more directions, the overhead of the network increased accordingly. Therefore, we only discussed the decomposition of high-frequency levels within three. Considering the network overhead, we selected the results of NSCT decomposition at three levels. Table 6 shows the PSNR values corresponding to different high-frequency levels of NSCT decomposition. Obviously, with the increase in the decomposition level and the number of decomposition directions, the learned detailed information was richer, and the PSNR values increased accordingly.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Noise** \\ **Level** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Noisy** \\ **Data** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Wavelet** \\ **[47]** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Wavelet** \\ **[47]** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Wavelet** \\ **[49]** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **VDSR** \\ **[49]** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **MSRN** \\ **[16]** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **IDN** \\ **[17]** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **[18]** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **[18]** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **[22]** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **[25]** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **[41]** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Ours** \\ **(dB)** \\ \end{tabular} } \\ \hline \(1\times 10^{-6}\) & 68.5969 & 72.6654 & 75.3775 & 76.2573 & 79.1136 & 80.6623 & 80.5472 & 81.4377 & 81.2451 & 83.5513 & **84.0452** \\ \(5\times 10^{-6}\) & 62.4360 & 66.2186 & 69.5388 & 70.6695 & 74.0563 & 75.5080 & 75.9531 & 76.7624 & 76.7322 & 78.6542 & **80.1026** \\ \(1\times 10^{-5}\) & 54.7663 & 57.7956 & 59.8267 & 60.7754 & 65.7964 & 67.6554 & 67.9622 & 69.1425 & 68.9633 & 70.8774 & **72.7694** \\ \(5\times 10^{-5}\) & 43.6748 & 45.9952 & 48.1205 & 48.9943 & 54.2779 & 57.0286 & 56.8696 & 58.0532 & 58.3166 & 60.5588 & **62.4617** \\ \(1\times 10^{-4}\) & 32.5772 & 34.4826 & 36.5253 & 37.2517 & 43.7844 & 46.7963 & 46.9957 & 48.2935 & 47.9532 & 49.7836 & **50.3967** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of PSNR values for different methods on synthetic seismic data.  
Figure 10: Comparison of results processed by IDN and our method for seismic migrated profile data.