Unsupervised dual learning for seismic data denoising in the absence of labelled data

Abstract

ABSTRACTThe training and updating of the supervised deep learning model are both dependent on labelled data. Unfortunately, the data‐labelling process is usually expensive and time consuming, which makes obtaining labelled data a huge challenge, especially in the field of geophysics. Because of the above‐mentioned factors, some seismic data denoising methods based on supervised deep learning use synthetic seismic data for network training. Since synthetic seismic data are more or less different from real seismic data, the denoising results may have some amount of residual noise and some false signals when dealing with real seismic data. In response to the above problems, this paper introduces an unpaired domain‐to‐domain translation method based on the framework of two‐way generative adversarial networks, which does not need to use labelled data for training, and effectively solves the problem of lacking labelled data in the seismic data denoising tasks. We use an unpaired mixed training set containing synthetic seismic data and real seismic data to train the network, which effectively improves the denoising ability of the network for the real seismic data. The experimental results show that compared with the state‐of‐the‐art denoising methods such as denoising convolutional neural network, the proposed method can suppress the random noise and ground roll more thoroughly, and the denoising results basically would have no false signals.