Distributed Acoustic Sensing Vertical Seismic Profile Data Denoising Based on Multistage Denoising Network
Yue Li, Man Zhang, Yuxing Zhao, Ning Wu
Abstract
Distributed acoustic sensing (DAS) is a new exploration technology widely used to acquire vertical seismic profiles (VSPs). DAS can achieve low-cost and high-density observations, but the signal-to-noise ratio (SNR) of the VSP data collected by DAS is low compared with traditional electrical geophones. Moreover, DAS VSP data cover many types of noise, including random noise, fading noise, checkerboard noise, and long-period noise. These noises bring many difficulties to the imaging and interpretation of DAS VSP data. To solve this problem, we proposed a multi-stage denoising network (MSDN) to denoise DAS VSP data. MSDN is a progressive denoising network consisting of four stages. MSDN can recover the signal details better than a single-stage denoising network, which is beneficial when processing deep reflection signals. In addition, MSDN combines residual structure and an attention mechanism. The residual structure can prevent the degradation of the deep neural network, while the attention mechanism can make the network focus on effective signals, making network learning more accurate and efficient. Both synthetic data and field data denoising results showed that MSDN could effectively remove various complex noises and restore signals covered by noise. Compared with other denoising methods, our method has improved signal amplitude preservation ability and noise suppression ability.