## 4 Discussion  
In passive-source seismic exploration, the number and distribution of subsurface sources are very random. The traditional seismic interferometry needs to satisfy the uniform distribution of the sources and be at a certain depth to reconstruct a high-quality virtual shot record. And the number and distribution location of undesirable seismic sources have a great impact on the virtual shot record.  
Some conventional classical interferometric methods, such as inverse convolutional seismic interferometry [30], and algorithms developed on this basis, such as multidimensional deconvolutional seismic interferometry [35], can mitigate the effect of the inhomogeneous distribution of sources on the virtual seismic recordings to some extent. However, all of these methods require strict constraints on the uniform distribution of the geophones and the excision of the direct waves (which is very difficult to achieve for the noise sources). On this basis, the inverse is applied to the records received by all geophones. This operation is not only computationally huge, but the process is extremely unstable. These methods also do not give good results for specific source distributions (such as sources distributed in a narrow range on the survey line). In the method using deep learning, [PERSON] et al. [36] used neural networks to make the virtual seismic record under uneven source distribution and learn the signal of the active source. Not only were a lot of artifacts eliminated, but the waveforms were also clearer. However, there is no discussion of the extreme case of distributed sources or the case of a small number of sources. And the use of active source records for labeling may cause some loss of low-frequency signals.  
Compared with the improved seismic interferometry of the inverse convolution class, the cross-correlation-based seismic interferometry has fewer limitations, is computationally stable, and does not require strict uniform arrangement of geophones, cumbersome resection of direct waves, or computationally unstable inverse operations. And using the virtual seismic records of passive sources as labels for the training of the network can not only retain the low-frequency signals better but also train better due to the higher degree of similarity. Therefore, this method can improve the application effect of passive source seismic exploration to a certain extent on the basis of no additional requirements on the observation system and no loss of low-frequency signals. In this paper, we only show four of these cases of seismic source inactivity. After processing according to our network, the influence of the number and distribution of sources on the virtual seismic record can be reduced to some extent.  
Only four of these inactive cases are shown in this paper. After the processing based on our network, the influence of the distribution of the seismic sources on the virtual shot records can be reduced to a certain extent. Therefore, this method can improve the application of passive source seismic exploration to some extent.  
In the future development trend, with the future development of computing equipment and deep learning technology, there will be neural networks with better processing effects, which can solve the influence of the distribution of more complex seismic sources on the virtual shot record.