## 4 Discussion of the Impact of Different Wavefield Components  
During the signal reconstruction process, testing the effect of data training under different wavefield conditions is essential to improve the accuracy and reliability of seismic data processing. Therefore, by establishing labeled datasets under different wavefield conditions, the impact of different wavefield conditions on the signal reconstruction effect can be more accurately simulated, thereby optimizing the data processing algorithm. Here, we consider the wavefield in four cases. The absorbing boundary and free surface were used as the top boundary conditions. At the same time, the wavefield data with and without multiples were simulated under each surface boundary condition. Taking the four labeled data obtained by the layered velocity model shown in Figure 2c as an example, the single shot records with the source located at x = 500 m are shown in Figure 13a-d, respectively. The top boundary conditions of Figure 13a,b are absorbing boundaries, and Figure 13c,d are free surfaces. Figure 13a is the without free surface-related multiples wavefield but including direct wave, Figure 13b is the primary scattered field without direct wave, Figure 13c is the full wavefield, and Figure 13d is the full wavefield without direct wave. We compared the seismic records and single-trace waveforms of input data, expected data, BE-Transformer network, and CNN network prediction results under four wave field conditions. Figure 14 shows the comparison of the source's seismic records at x = 500 m. The prediction results of the BE-Transformer are highly consistent with the expected data events, and the fit of the waveform features is very high. Although the CNN network prediction results have a high degree of waveform matching, there are apparent artifacts. The prediction results in wavefields other than the no-free surface multiple wave field are very poor, the phase axis is discontinuous, the signal-noise ratio is low, and the information is obviously distorted. Figure 15 shows a comparison of single-trace waveforms randomly selected under four wavefield conditions. The offset of Figure 15a is 56 m, the offset of Figure 15b is 8 m, the offset of Figure 15c is 16 m, and the offset of Figure 15d is 16 m. The prediction results of the BE-Transformer network have high waveform consistency with the  
Figure 12: Anti-noise experiments. (**a**) Layered model; (**b**) Marmousi II velocity submodel. expected data in terms of amplitude and phase under arbitrary wave field conditions. However, the CNN network prediction results show a large number of waveform mismatches. The waveforms are discontinuous and there were many noise points and mutation points, and the fitting effect was not ideal. In summary, the BE-Transformer network performs well under different wave field conditions and can effectively deal with various problems in the reconstruction process of seismic data. In contrast, traditional CNN networks have apparent limitations in handling complex wavefield conditions. Figure 14: Comparison of BE-Transformer and CNN prediction results with input data and expected data seismic records under four wave field conditions. (**a**) without free surface-related multiples wavefield; (**b**) primary scattered field; (**c**) full wavefield; (**d**) the full wavefield without direct wave. Figure 13: Labeled datasets under different wavefield conditions. (**a**) without free surface-related multiples wavefield; (**b**) primary scattered field; (**c**) full wavefield; (**d**) the full wavefield without direct wave. To more intuitively demonstrate the signal compensation and reconstruction effect of the BE-Transformer network model, Figure 16 shows the spectrum analysis results of seismic records under four wavefield conditions. While the energy signal reconstructs the specific frequency band information in the original observation record, it also has a certain degree of richness in high and low-frequency bands.