Total Variation Regularized Self-Supervised Bayesian Deep Learning for Seismic Random Noise Attenuation

Random noise attenuation is an essential procedure of seismic data processing, which is crucial to improve the signal-to-noise ratio (SNR) of seismic data. Recently, deep learning (DL) has emerged as a promising tool for seismic data denoising. Although the DL-based method has excellent learning and representation capabilities, it lacks the interpretability of the traditional handcrafted denoisers. Furthermore, supervised learning involved in most of the previous work is usually not feasible to construct a great amount of noisy/noise-free training data pairs for real applications. We develop a total variation (TV) regularized self-supervised Bayesian DL model, dubbed as TVRBNN, which combines the advantages of Bayesian neural network (BNN) and TV regularization techniques for seismic random noise suppression. The significant characteristic of the proposed model is that it does not rely on the ground-truth seismic data as training labels and solely utilizes the observed noisy data to train TVRBNN. Synthetic and field experiments are implemented to verify the effectiveness of the TVRBNN model in seismic data denoising. Compared with the classical non-learning denoising approaches and the state-of-the-art self-supervised DL model, TVRBNN can effectively enhance the lateral continuity of seismic events and preserve the geological structure information while effectively removing random noise.