SelfTexture: Self-Supervised Learning for Spatially Correlated Noise Removal of DAS VSP Data via Adaptive Texture Analysis

In recent years, significant progress has been made in self-supervised seismic denoising. However, most of these methods focus on random noise attenuation, which is of little practical use for distributed acoustic sensing (DAS) vertical seismic profile (VSP) data with a large amount of spatially correlated noise. In this article, we propose a new perspective to solve this problem, which is to redesign the masked spot training scheme by analyzing different texture features of effective signal and spatially correlated noise. Specifically, we fully analyze the correlation of different correlated noises in vertical and horizontal scales, and then use a novel masked spot masking strategy to carefully design the receptive field to expand the masked spot network (BSN) to neighborhood-mask network (NMN), which makes BSN still meet the assumption of noise pixel independence in the presence of a large amount of correlated noise. At the same time, we introduce a texture total variation (TTV) regularization to further eliminate correlated noise and preserve the local smooth structure of the effective signal. In order to maximize the texture difference between signal and noise, we propose asymmetric-dilated convolution (ADConv), which has a large receptive field in specific direction and acts as an information filter in the network. Our method, also called SelfTexture, only uses observed noisy DAS VSP data to remove correlated noise in a self-supervised manner and further demonstrates the great potential of BSN in dealing with correlated noise. Extensive experiments on synthetic and field DAS VSP data validate the superior performance of our SelfTexture.