## 3 Numerical Example
### Enhanced Reconstruction Results for a Small Number of Seismic Sources
In addition, where waveform discontinuities are present, our network complements them, restoring waveforms with good continuity. Figure 6: The processing results display virtual shot records with a small number of seismic sources under test model2. (**a**) A virtual shot of a small number of sources and input data. (**b**) A virtual shot of a large number of sources, labeled. (**c**) Processing result of attention mechanism UNet, prediction. (**d**) Processing result of improved network prediction. Although these networks can suppress coherent noise and artifacts effectively, they also extract effective signals. However, it can be clearly seen that the improved network can suppress noise more thoroughly. The continuity of the same waveform axis is better and clearer, which also proves that our improvement of the network is effective. In this section, we experiment with neural networks to reconstruct records of unevenly distributed seismic sources. Using the velocity model from the previous section, we can concentrate the subsurface seismic sources in a small region for numerical simulation. The resulting passive seismic source records are reconstructed into unevenly distributed virtual shot records. The distribution of seismic sources is shown in Figure 7.  
After 400 iterations, our network was trained, and we fed the test data into the network, which was processed as shown in the figure. To test the generalizability of our network under different models and the processing performance for virtual shots located at different positions, we selected records of virtual shots located in multiple locations for display (see Figures 8 and 9). Similar to the previous section, we can still use both of our neural networks to suppress coherent noise and artifacts. We can revert to a reasonable hyperbolic homogeneous axis in the virtual shot records on the side without the source distribution; we suppress the spurious homogeneous axis on the side with the source distribution. At the same time, the problem of insufficient continuation of the waveform is well resolved. We analyze that since there are many linear intersecting homography features in the training set corresponding to the hyperbolic homography axes in the training labels, it is possible to learn the corresponding features. The reason that coherent noise with artifacts can be suppressed is also similar to the previous section. In the tracts with the right side of the seismic distribution, our neural network recovers its waveform even though the training data are broken waveforms. Figure 8: \(Cont\). Figure 7: Schematic diagram of source distribution. The red dots represent where the sources are located. (**a**) Schematic diagram of inhomogeneous source distribution. (**b**) Schematic diagram of homogeneous source distribution. Figure 8: The processing results display virtual shot records with uneven seismic sources under test model1. (**a**) A virtual shot of uneven seismic sources and input data. (**b**) A virtual shot of uniformly distributed sources, labeled. (**c**) Processing result of attention mechanism UNet prediction. (**d**) Processing result of improved network prediction. Figure 9: The processing results display virtual shot records with uneven seismic sources under test model2. (**a**) A virtual shot of uneven seismic sources and input data. (**b**) A virtual shot of uniformly distributed sources. distributed sources, labeled. (**c**) Processing result of attention mechanism UNet prediction. (**d**) Processing result of improved network prediction. In the testing of uneven source distribution, we can see that both networks can effectively suppress coherent noise and artifacts. However, when carefully comparing the processed waveforms, we found that the virtual records processed by the improved network showed clearer and more continuous waveforms.