Noise suppression of distributed fiber-optical acoustic sensing seismic data by attention-guided multiscale generative adversarial network

Abstract

Distributed fiber-optical acoustic sensing (DAS) is an emerging technology, which uses optical fiber as a sensor for signal acquisition and recently has been applied to seismic exploration. Seismic records collected with DAS equipment often are contaminated with multicomponent noise induced by complex causes which consequently affect the subsequent imaging, inversion, and even the interpretation work. Therefore, research on effective noise suppression algorithm in DAS seismic data has become a hot topic in geophysical prospecting. In this study, we develop an attention-guided multiscale generative adversarial network (AMGAN) based on the traditional GAN architecture and discuss its feasibility in multicomponent DAS noise suppression. In AMGAN, multiscale ideas are introduced to extract features of raw DAS data in different scales. In addition, attention mechanism is imported and trained at each hierarchy to help extract and fuse the features from different scales. Overall, AMGAN, attributed to the inversely fused multiscale features, can reveal more detailed reflected signal information in DAS data denoising tasks. The synthetic and field DAS data experiments indicate that AMGAN can effectively remove the multicomponent seismic noise in DAS data and recover the weak seismic events with advantages in clarity and continuity.