## 3 Numerical Example
### Training Results
Figure 7: Comparison of BE-Transformer prediction results with original data and expected data RTM imaging results. (**a**) Layered model; (**b**) Marmousi II velocity submodel. filtering 15\(-\)25 Hz, we also attenuate the amplitudes within different frequency bands of the filtered data set to simulate the condition of damage across various frequency ranges. Compared with the first data set, the second data set lacks some middle frequency. The third data set lacks high and low frequency and the frequency amplitude is very narrow. After 600 iterations, the BE-Transformer network prediction results of the layered model and the Marmousi II velocity model in the two cases are shown in Figures 9 and 10, along with the seismic record comparisons of input data and expected data. The predicted results have good waveform matching with the expected data, and the differences in phase and relative amplitude are very small, thus achieving amplitude-preserving prediction of seismic data. To show the reconstruction effect of seismic data in each frequency band more intuitively, spectral analysis was performed on the above dataset, and the results are shown in Figure 11. Input(a-c) refers to the three different types of band-slice seismic data sets, which are the input of the network. The Label is the network label full band seismic data set. Pred(a-c) are the prediction results of the three different types. Whether a simple or complex model, the network can learn and capture the intrinsic correlation between frequency band-slice data set and full frequency band data set. The frequency bands are balanced and linearly gained, which achieves the information extension and compensation of arbitrary lacking frequency bands of the seismic data from local to whole and from unknown to known. This further proves the generalization ability of the BE-Transformer model to cope with various complex seismic data processing scenarios. Figure 9: Comparison chart between BE-Transformer prediction results, input data, and expected data seismic records (Layered model). (**a**) is the simulated case lacking some middle frequency; (**b**) is the simulated case lacking high and low frequency and the frequency amplitude is very narrow. Considering that noise may impact the signal compensation reconstruction, random noise with a signal-to-noise ratio of 20 was added to the frequency band-slice data (only applied band-pass filter with 15\(-\)25 Hz) and entered into the model for anti-noise experiments. The comparison of seismic records before and after noise addition is shown in Figure 12. After testing, the noise has been effectively suppressed, and the effective signal has been restored and enhanced. The BE-Transformer network also has a good processing effect on data containing noise and has good anti-noise performance. Figure 11: Comparison of BE-Transformer prediction results, input data, and expected data amplitude spectrum. (**a**) Layered model; (**b**) Marmousi II velocity submodel. Figure 3:0 Comparison chart between BE-Transformer prediction results, input data, and expected data seismic records (Marmousi II velocity submodel). (**a**) is the simulated case lacking some middle frequency; (**b**) is the simulated case lacking high and low frequency and the frequency amplitude is very narrow.