Seismic random noise suppression by using deep residual U-Net

Abstract

Pre-stack seismic denoising is one of most important processing steps in seismic exploration, which can significantly enhance the signal-to-noise ratio (SNR) and resolution of real seismic data. Seismic random noise often presents some complicated characteristics (non-gaussian, non-stationary, non-linear) and may overlap with desired signals in frequency domain. Thus, we need to explore the corresponding approach to attenuate the unwanted random noise and simultaneously recover the signals. Recently, some methods based on convolutional neural network (CNN) have shown excellent performance in seismic data denoising. These CNN-based denoising methods can learn the potential features of labeled data, thereby establishing the mapping relationship between noisy seismic data and signals. However, these existing CNN-based methods only consider the single-scale potential features and neglect some useful multiscale features, leading to their performance degradation when processing some seismic data with low SNR. In view of above drawback, we propose a novel CNN by introducing the residual theory and reconstruction block into the conventional U-Net, called deep residual U-Net (DRUN). In DRUN, some residual blocks are utilized to obtain the multiscale features of noisy seismic data and the followed reconstruction block can incorporate features of different scales, thereby distinguishing signals and random noise. Both the synthetic and field seismic data are processed to verify the effectiveness of DRUN. Compared with the original U-Net, the experimental results demonstrate that the proposed DRUN has superiority in noise attenuation and signal preservation.