## 3 Experiment  
The present study uses the publicly available Sleipner CO\({}_{2}\) 4D seismic survey dataset for the interpolation of 3D seismic survey data, which targets a reservoir layer located near the North Sea in Norway [24] (Figure 5). The data were obtained from repeated surveys of a 4 km \(\times\) 7 km area that includes the reservoir layer, with the reference data collected in 1994 and the monitoring data collected in 2001, 2004, 2006, 2008, and 2010. The seismic data up to and including the 2001 survey were processed by WesternGeco, and the seismic data from 1994, 2001, 2004, and 2006 were fully reprocessed by PGS. Time-lapse processing in 2008 and 2010 was carried out in a similar procedure. The 2010 data set includes time-lapse and seismic imaging data with four offset stacks featuring full, near, mid and far folds. This study used the seismic image processed 2010 data with full fold, which consists of 1121 crosslines, 216 inlines, and 1001 samples, with a total of 242,136 traces [25,26]. Table 5 provides further detailed information on the data.  
Figure 4: Interpolation strategy applied to 3D cWGAN (the part indicated by the orange line is the training data, and the part indicated by the green line is the test data). (**a**) Training data and data extraction direction of 3D cWGAN; (**b**) test data and data extraction direction of 3D cWGAN.  
To train each model, the two strategies described were applied to generate train data. For the 2D cWGAN, regularly 50% of the traces were missed in the inline direction, and 80 \(\times\) 80 sized patches were extracted and applied to train the model. For the 3D cWGAN, data were generated by regularly missing 50% of the traces in the crossline direction, and 80 \(\times\) 80 \(\times\) 80 sized cubes were extracted; 70% of these were used for model training. For the 3D U-Net that only used the generator of 3D cWGAN, the data were generated in the same way as those generated with the 3D cWGAN, with 80 \(\times\) 80 \(\times\) 80 sized cubes being extracted; 70% of these were used for the training data, 20% for the validation data, and 10% for the testing data. We used the RMSprop optimizer with a learning rate of 5 \(\times\) 10\({}^{-5}\) and 1 \(\times\) 10\({}^{-5}\) for 2D and 3D cWGAN, respectively, and the Adam optimizer with a learning rate of 1 \(\times\) 10\({}^{-4}\) for 3D U-Net. The batch sizes are 4, 4, and 32 for 3D U-Net, 3D cWGAN and 2D cWGAN, respectively. All network training was carried out on TensorFlow 2 framework, and two GPUs (GeForce RTX 2090 Ti, NVIDIA, Santa Clara, CA, USA) were used to accelerate computation.  
Six different test datasets were generated for testing the trained model, and these are shown in Figure 6. For testing, data with misses of 50%, 67%, and 75% in a regular/irregular pattern were generated from the same dataset in the crossline direction and were used. The same testing data were applied to the 2D cWGAN, 3D cWGAN, and 3D U-Net models.  
Figure 5: Exploration location of the vertical section of data used. (**a**) Exploration area (about the box area); (**b**) vertical section of the exploration area.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Data of 2010** \\ \hline Shot interval (m) & 12.5 \\ Shoot direction & N-S \\ Group interval (m) & 12.5 \\ Sample interval (ms) & 2 \\ Record length (ms) & 4608 \\ Amount of injected CO\({}_{2}\) (kt) & 12,080 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Acquisition parameter of Sleipner 4D seismic data 2010.  
The test data use data that were missed at rates of 50%, 67%, and 75% in a regular/irregular manner in the crossline direction, and the same test data were applied to the 2D cWGAN, 3D cWGAN, and 3D U-Net models.