Low-frequency swell noise suppression based on U-Net

摘要

Low-frequency band-shaped swell noise with strong amplitude is common in marine seismic data.The conventional high-pass fi ltering algorithm widely used to suppress swell noise often results in serious damage of effective information.This paper introduces the residual learning strategy of denoising convolutional neural network(DnCNN)into a U-shaped convolutional neural network(U-Net)to develop a new U-Net with more generalization,which can eliminate low-frequency swell noise with high precision.The results of both model date tests and real data processing show that the new U-Net is capable of effi cient learning and high-precision noise removal,and can avoid the overfi tting problem which is very common in conventional neural network methods.This new U-Net can also be generalized to some extent and can eff ectively preserve low-frequency eff ective information.Compared with the conventional high-pass fi ltering method commonly used in the industry,the new U-Net can eliminate low-frequency swell noise with higher precision while eff ectively preserving low-frequency eff ective information,which is of great signifi cance for subsequent processing such as amplitude-preserving imaging and full waveform inversion.