## 1 Introduction  
In seismic surveys, noise in the subsurface tends to interfere with effective signals. Its signal is weak but widely distributed and haphazard, and we often resort to various techniques to eliminate it. However, waves generated by vibrations from underground sources will carry information about underground tectonics as they propagate. If noise can be used, it can act as a substitute for an active source. In some cases where active source excitation is not possible, such as near cities, it is sufficient to set up geophones to receive passive sources of signals. At the same time, passive acquisition is cost-effective because there is no need to excite seismic sources. Furthermore, due to the rich frequency range of subsurface noise sources, there are a large number of low-frequency signals [1]. Therefore, passive sources are more advantageous than active sources in large-scale, deep seismic surveys. After acquiring the passive source seismic record, we can use seismic interferometry [2; 3] to reconstruct the passive source seismic record into a virtual shot record similar to the active source seismic shot record. However, using seismic interferometry to reconstruct passive seismic data inevitably results in coherent noise and artifacts. The noise has a greater impact on deteriorating the reconstruction of seismic data when the attenuation of the medium is high [4]. Removing the effects of this coherent noise and artifact disturbances is an ongoing effort by geophysicists. The F-K [5] filtering-based processing can suppress the coherent noise to some extent, but it may cause damage to the effective wave. Suppressing the coherent noise in the Radon domain has also been tried several times [6; 7; 8], but there is still a problem of low resolution. [PERSON] et al. [9] created a median filtering denoising method and later produced many derivative algorithms [10; 11]. They can operate on pixels on the image, but their small windows have no effect on coherent noise and artifacts, and large windows will blur effective signals. The above methods can remove coherent noise and artifacts to some extent. However, they can harm the effective signal or suppress it poorly. The selection of parameters will have a large impact on the processing effect, which is somewhat subjective. In addition, this method cannot effectively recover the effective waveform. Deep learning learns deep features of images and other information by training neural networks. [PERSON] [12] and others first invented a convolutional neural network and achieved good results in handwritten digit recognition. The AlexNet, invented by [PERSON] et al. [13], has achieved good results in image recognition and won the championship in the image recognition contest, setting off an upsurge of in-depth learning. Since then, various deep learning networks have sprung up in the public's view, such as VggNet [14], ResNet [15], FCN [16], DnCNN [17], and UNet [18]. These networks have achieved good results in classification and image segmentation. Based on ensuring the accuracy of manual recognition and conventional methods, they have higher processing efficiency, so they have been widely developed in various fields. In recent years, thanks to the upgrading of computer hardware resources, deep learning algorithms have been widely used in the geophysical field. [PERSON] et al. [19] realized low-frequency reconstruction in full-waveform inversion based on deep learning. [PERSON] et al. [20] synthetic data generation for deep learning-based inversion for velocity model building. [PERSON] et al. [21] used interactive salt segmentation from 3D seismic images using saltisnet3d. [PERSON] et al. [22] acoustic impedance inversion of seismic imaging profiles using self-attentive U-Net. [PERSON] et al. [23] use SafeNet to identify seismic disturbances. [PERSON] et al. [24] accomplished low-frequency extrapolation of multicomponent data in Elastic FWI using deep learning. [PERSON] et al.