## 4 Results  
The six test results of the 2D cWGAN, 3D cWGAN, and 3D U-Net models used in this study are shown in Figures 7-9. Figures 7 and 8 are the test results in the crossline direction, and Figure 9 is the test results in the inline direction for data with regularly 75% missing traces. By comparing the prediction results in the crossline direction of each model through Figures 7 and 8, it can be seen that the difference between the label data and the prediction result of the 3D cWGAN is smaller compared to that of the 2D cWGAN and 3D U-Net and that it shows a smoother prediction result compared to the prediction results of the two models. On the other hand, in the case of the 3D U-Net, as the missing rate of the data increases, it is possible to see that most of the data are not predicted. In the case of Figure 9a, which represents the input data and label data in the inline direction, it can be confirmed that there are no data in the inline direction due to the missing data in the crossline direction in the input data. By comparing the prediction results in the inline direction in Figure 9b, it can be seen that the difference between the label data and the prediction result is smaller in the 3D cWGAN than in the 2D cWGAN and 3D U-Net. Additional accuracy verification of the interpolation result was performed by converting it into an f-k spectrum for comparison. The f-k spectrum is a frequency-wavenumber domain where the frequency (f) and wavenumber (k) are used as independent variables, which are the results of a two-dimensional Fourier transform of seismic exploration records or sections. It is mainly used in the analysis of seismic direction and apparent velocity and in the design of velocity filters in seismic data analysis. Data that exceeds the Nyquist frequency (\(f_{N}\)) determined by discrete space sampling can spread around and be mixed with signals, and the Nyquist wavenumber (\(K_{N}\)) proceeds from right \(+K_{N}\) to left \(-K_{N}\). The aliasing observed in the f-k spectrum is due to the interval of the receiver or missing data, resulting in spatial aliasing [27]. Figure 6: Label data and corresponding test input data for six cases of missing data in crossline. Figure 7: _Cont_. Figure 8: _Cont._  
Figure 7: The test results in crossline direction for input data with regularly missing data. (**a–c**) Interpolation results and corresponding residuals (difference between interpolation result and label) for data with 50, 67, and 75% missing traces, respectively. Figure 8: The test results in crossline direction for input data with irregularly missing data. (**a**–**c**) Interpolation results and corresponding residuals (difference between interpolation result and label) for data with 50, 67, and 75% missing traces, respectively. The results of the interpolation in the crossline direction shown in Figure 7c, where 75% of the trace is regularly missed, are presented in Figure 10 for the f-k spectrum of the data. Figure 10a shows the input data and label data for the corresponding data, and Figure 10b shows the interpolation results of each model and the f-k spectrum of the residuals corresponding to each result. When comparing the interpolation results shown in Figure 10b, the 3D U-Net showed large residuals compared to other models, while the 3D cWGAN showed relatively small residuals and highly accurate prediction results. In addition, similar f-k spectra were observed for other cases. Overall, our results confirmed that the 3D cWGAN approach achieves higher accuracy in crossline interpolation compared to other models. Figure 9: The test results for data with regularly 75% missing traces in the inline direction. (**a**) Input data and label data; (**b**) interpolation results and corresponding residuals (difference between interpolation result and label).