Multiscale recurrent-guided denoising network for distributed acoustic sensing-vertical seismic profile background noise attenuation

Abstract

In recent years, distributed optical fiber acoustic sensing (DAS) has emerged as a novel seismic acquisition technique. Compared with conventional hydrophones and geophones of microelectromechanical systems, DAS has an advantage in terms of acquisition geometry, such as low-cost and high-density observations. However, the collected DAS records always suffer from various types of noise, which poses challenges for subsequent processing. Thus, deep learning-based solutions have addressed the attenuation of seismic background noise. Denoising networks can provide excellent denoising results. Nonetheless, the architectures of these networks are relatively simple, which may result in degeneration when confronted with the complex DAS background noise. To effectively attenuate complex noise, a novel multiscale network called recurrent-guided self-enhanced attention network (RGSA-Net) is developed for complex seismic data processing. Specifically, the backbone of RGSA-Net uses a conventional feedforward neural network to preliminarily extract the potential features. Meanwhile, multiscale modules, inspired by the recurrent-guided scheme, are used to enhance the contour information. On this basis, a self-enhanced attention module is applied to fuse the multiscale features and further reinforce the effective information, thereby improving the noise attenuation capability. Synthetic and field experiments demonstrate that RGSA-Net indicates promise in complex noise attenuation and weak upgoing signal recovery.