## 5 Discussion and Conclusions  
In this study, we aimed to interpolate 3D seismic data using three deep learning models, namely, 2D cWGAN, 3D cWGAN, and 3D U-Net, and evaluate their prediction accuracy by comparing the interpolation results. To accomplish this goal, we used the Sleipner CO2 4D seismic survey dataset and assessed the performance of each model using MSE, PSNR, and SSIM metrics.  
Our analysis revealed that the 3D U-Net exhibited a high accuracy only when the training and test data were generated using similar patterns of missing traces, such as  
Figure 12: Comparison of (**a**) MSE, (**b**) PSNR and (**c**) SSIM by 2D cWGAN, 3D cWGAN and 3D U-Net.  
regularly 50% missing traces in both sets. However, its performance significantly decreased when applied to other types of missing data. In contrast, the 3D cWGAN demonstrated high-accuracy predictions not only for the test data generated with similar patterns to the training data but also for other types of missing data. Moreover, when comparing the prediction results in the inline direction, the 3D cWGAN showed smaller residuals compared to the other two models. Therefore, among the three models, the 3D cWGAN performed data prediction in the inline and crossline directions with relatively high accuracy.  
Although the 3D cWGAN exhibited the best performance in this study, it is important to note that there may be other deep learning models or optimization techniques that could further improve the accuracy of the interpolation of the data. Nevertheless, our findings suggest that the 3D cWGAN is a promising approach to use for interpolating 3D seismic data as it can achieve highly accurate predictions even when dealing with data patterns that are not similar to the training data. This could have significant implications for the field of geophysics and seismic exploration, as the accurate interpolation of 3D seismic data is critical for identifying and characterizing subsurface geological structures.  
In conclusion, our study demonstrates that deep learning models, such as the 3D cWGAN, have the potential to significantly improve the accuracy of interpolating 3D seismic data. Our results also highlight the need to conduct further research on the development of advanced deep-learning models and optimization techniques that can further enhance the performance of these models. Ultimately, our findings could help pave the way for a more accurate and efficient interpretation of subsurface geological structures, which could have important implications for a wide range of industries, including oil and gas exploration, mining, geothermal energy production, and CO\({}_{2}\) injection monitoring.  
Methodology, D.Y.; Software, J.Y.; Investigation, J.Y.; Writing--original draft, J.Y.; Supervision, D.Y. All authors have read and agreed to the published version of the manuscript. This work was supported by a Korea CCUS Association (K-CCUS) grant funded by the Korea Government (MOE, MOTIE) (KCCUS202020001, Human Resources Program for Reduction of greenhouse gases) and by Korea Institute of Marine Science & Technology Promotion (KIMST) grant funded by Ministry of Oceans and Fisheries (MOF) (2020254, Development of technology for seabed classification based on machine learning). Not applicable. Not applicable.  
The Sleipner CO\({}_{2}\) Reference datasets, 2020 can be obtained at [https://co2 datashare.org/dataset/sleipner-2019-benchmark-model](https://co2 datashare.org/dataset/sleipner-2019-benchmark-model) (accessed on 10 February 2022). The authors would like to thank Sleipner Group for providing the benchmark data (Sleipner CO\({}_{2}\) Reference datasets, 2020).  
The authors declare no conflict of interest.