## 3 Results and Discussion
### Field Data Example  
To further assess the applicability of WPI-SD, we applied it to field data processing in the Tarim Basin, located in western China. These field data were acquired from the Tarim Basin and consist of common-shot gathers, with each acquisition having a size of 3000 samples \(\times\) 240 traces and a sampling rate of 2 milliseconds. We compared the results with those obtained using the F-K filtering method and curvelet transform method, and the specific findings can be seen in Figure 11.  
Figure 11a,b display the original seismic records and their corresponding F-K spectra, respectively. Figure 11c-e illustrate the outcomes of ground roll suppression using the F-K filtering method, along with the difference between the original and the denoised data. Although the F-K filtering method effectively suppresses ground roll, some residual ground roll remains in the denoised profiles. Furthermore, Figure 11d's denoised difference profile reveals the  
Figure 10: Ground roll suppression effect and MSE curve: ((**a–d**) show the ground roll suppression effects of the six-layer skip connection network with L2 loss function, the two-layer skip connection network with \(\lambda_{1}=0.07\) and \(\lambda_{2}=1\), the six-layer skip connection network with \(\lambda_{1}=0.07\) and \(\lambda_{2}=1\), and the six-layer skip connection network with \(\lambda_{1}=0.04\) and \(\lambda_{2}=1\), respectively; (**e–h**) represent the mean square error (MSE) loss function curves within 1000 iterations between the network outputs and clean seismic records without ground roll for (**a–d**)). presence of numerous valid reflection signals. This occurs because F-K filtering is a global filtering method that unavoidably affects valid reflection signals while removing ground roll. Next, Figure 11f-h demonstrate the denoising results obtained using the curvelet transform method, as well as the corresponding difference profiles and F-K spectra. Compared to the F-K filtering method, the curvelet transform method performs better in suppressing ground roll, resulting in minimal residual ground roll interference in the denoised profiles. However, as shown in Figure 11g, some strong energy valid signals still persist in the difference profile of the curvelet transform method. Therefore, further improvement is required to enhance the method's ability to preserve valid signals. Finally, Figure 11i-k showcase the denoising outcomes achieved with the proposed WPI-SD method, alongside the corresponding difference profiles and F-K spectra. The denoising results clearly indicate that this method significantly enhances the denoising effect compared to traditional methods, as it noticeably reduces the leakage of valid signal energy or residual ground roll in the difference profiles. By comparing the difference profiles (Figure 11d,g), it becomes evident that the proposed method minimizes the damage to high-frequency valid signals and strong energy valid signals, enabling the network to better preserve the detailed features of valid signals while suppressing strong ground roll. The post-stack seismic data after ground roll suppression using F-K filtering, the curvelet transform, and the proposed method were obtained through conventional stacking processing without further denoising, as shown in Figure 12. The red box in Figure 12 indicates that compared to F-K filtering and the curvelet transform, the proposed method results in a clearer and more continuous seismic reflector after ground roll suppression. To further validate the suppression effect of seismic ground roll, we employed the quality factor Q [50] to evaluate the analysis method of ground roll effects and conducted a quantitative analysis of the ground roll suppression effect of our approach.