Self-Supervised Denoising for Distributed Acoustic Sensing Vertical Seismic Profile Data via Improved Blind Spot Network

In recent years, distributed acoustic sensing (DAS) has been widely used for vertical seismic profile (VSP) data acquisition. Compared with conventional geophones, the data collected by DAS usually contain more noise. Therefore, denoising is an essential step in DAS VSP data processing. Benefiting from the development of neural networks, learning-based methods are widely used for seismic data denoising. However, supervised learning-based denoising methods are often limited in practical applications due to the scarcity of labeled datasets. Although recently proposed self-supervised learning-based methods, such as blind spot network (BSN), alleviate the reliance on ground-truth data, the harsh conditions that noise should satisfy zero mean and statistical independence are also difficult to meet. In this study, we propose an improved BSN for spatially correlated noise suppression in DAS VSP data. Specifically, we observe that the spatial correlation of DAS noise decreases as the distance between noise pixels increases and that the rate of decrease is direction-dependent. Based on this observation, we improve the pixel-shuffle downsampling (PD) strategy to sample and recombine the DAS VSP data to increase the distance between adjacent pixels, thereby weakening the spatial correlation of noise to meet the application conditions of BSN. In addition, we also propose a signal detail enhancement strategy to compensate for the effect of PD strategy on signal detail recovery. Qualitative and quantitative analysis on the denoising results of one synthetic and three field DAS VSP data show that the proposed method can effectively remove noise with certain spatial correlation, showing competitive performance.