## 3 Numerical Example
### Training Results  
The comparison of the prediction results obtained after 600 iterations with the input data and expected data seismic records is shown in Figure 5, where the seismic source is located at x = 500 m. The input data is the first type of band-slice seismic record with only a band-pass filter applied. The label data are the full band seismic record. The comparison shows that the prediction results of the BE-Transformer are consistent with the expected data, and the wave group characteristics are clear. Although the prediction results from CNN also contain the desired features, there are apparent artifacts in the records, and the signal-to-noise ratio is low. To display the prediction effect of the model more intuitively, Figure 6 shows the single-trace waveform comparison between the BE-Transformer and CNN network prediction results and the expected data. The offset of Figure 6a is 56 m, and the offset of Figure 6b is 0 m. The prediction results of the BE-Transformer maintain high consistency and accuracy with the expected data in terms of amplitude and phase. As  
Figure 4: Pearson correlation coefficient plot during training. Figure 3: The loss convergence of the network. for the CNN prediction results, although the main events match the expected data well, the continuous consistency of the phase information is not good, and there is an apparent undesired waveform oscillation phenomenon before the first arrival time, and the overall fitting effect is average. Therefore, from the comparison of macroscopic and local features, the BE-Transformer network has shown excellent reconstruction performance and good generalization ability from the tests on both simple and complex models. The effect is significantly better than that of the traditional CNN network. Reverse-time migration (RTM) was performed on the three data sets: original data (input data), expected data (label data), and BE-Transformer pre  
Figure 5: Comparison of BE–Transformer and CNN network prediction results with input and expected data seismic records. (**a**) Layered model; (**b**) Marmous II velocity submodel. Figure 6: Comparison of single-trace waveforms between BE-Transformer and CNN network prediction results and input and expected data. (**a**) Layered model (offset = 56 m); (**b**) Marmousi II velocity submodel (offset = 0 m). true velocity models to perform the RTM. The RTM imaging results and depth curves are shown in Figures 7 and 8, respectively. For the layered model, the comparison between the original and expected data shows that artifacts will appear in the imaging results, and the signal-to-noise ratio is low when the data lack low-frequency and high-frequency components. After signal reconstruction, the signal-to-noise ratio of the data is significantly improved, the velocity structure of the subsurface medium is correctly modeled, and the striatal continuity is good. The comparison between the input data and the prediction results shows that the artifacts in the input data have been significantly improved and eliminated, the details of the image are richer, and the imaging quality has been significantly improved. For the Marmousi II model, the main strong reflection interface of the expected data is clear, and the layer information will be accurate. However, due to insufficient frequency band information, the original data have side lobes and artifacts, and there is also certain layer distortion in thin layer areas and lower resolution. The data reconstructed by the BE-Transformer network have better resolution capabilities in the thin layer of shallow and the reservoir of deep, and the imaging accuracy is significantly improved. We use the second and third sample data sets to test the signal reconstruction effect and verify the BE-Transformer network's generalization ability. In addition to band-pass  
Figure 8: Comparison of BE-Transformer prediction results and original data and expected data reflectance curves. (**a**) Layered model; (**b**) Marmousi II velocity submodel.