A Branch Construction-Based CNN Denoiser for Desert Seismic Data

Abstract

Seismic random noise reduction is an indispensable step in seismic data processing. Due to complex geological condition and acquisition environment, random noise in the desert seismic data has spatiotemporally variant noise levels and weak similarity to the signals, which severely obscures the seismic signals and increases the difficulty to extract the reflected seismic signals. This letter focuses on suppressing the desert random noise based on a convolutional neural network (CNN) and proposes a branch construction-based denoising network (BCDNet). The BCDNet contains a denoising main network and a branched network added to the downsampled layer of the main network. With the branched network, the global context feature of the seismic data is obtained early in the network to guide the denoising task of the subsequent main network, which allows a flexible denoising for the desert random noise. Moreover, the downsampled layer is able to enlarge the receptive field of the network without increasing the network depth, thus leading to the better retention of the structural features in the seismic records. The extensive experiments and the field desert data application confirm that our BCDNet not only has a significant denoising capacity to desert seismic data but also is competitive in training time and memory cost.