Random Noise Attenuation of Seismic Data via Self-Supervised Bayesian Deep Learning

Random noise attenuation is a crucial task in seismic data processing, which can not only improve the signal-to-noise ratio (SNR) of seismic data but also facilitate accurate geological interpretation. Recently, deep learning (DL) has emerged as a powerful technique for seismic data noise suppression. However, most of the related works are based on supervised learning. To reduce the cost and complexity of constructing noise-free seismic data as training labels, this study focuses on a self-supervised DL method with the dropout strategy. By considering the Bayesian deep network, our proposed approach trains a denoising network with random weights, which predicts the noise-free seismic data from noisy seismic data. The experimental results of synthetic and field data illustrate the superiority and robustness of the self-supervised Bayesian neural network (BNN) model for seismic data denoising. Compared with the traditional denoising schemes and several state-of-the-art DL models, our method can effectively enhance the lateral continuity of seismic events and preserve the geological structure information while improving the SNR. We believe that this provides a solid foundation for the subsequent seismic data processing and interpretation.