oa Intelligent Denoising of Distributed Acoustic Sensing Seismic Data Using Deep Learning

The distributed acoustic sensing (DAS) technology has shown tremendous potential for high-resolution seismic surveys, owing to its high-density and cost-effective characteristic. However, DAS data often suffer from an exceptionally low signal-to-noise ratio (SNR) due to the presence of various potent noise sources, such as random noise, strong-amplitude erratic noise, and vertical and horizontal stripe noise. To address this challenge, researchers have explored several technologies, for example filtering and deep learning (DL), to enhance the SNR of DAS data. In this study, we present a DL-based denoising framework specifically tailored to attenuate complex DAS noise. Leveraging synthetic DAS data and real noise extracted from field DAS data, we construct a carefully curated dataset for training the designed neural network in a supervised manner, and subsequently, the trained network is served for denoising field DAS data. The experiments demonstrate that the trained network effectively suppresses noise in field DAS data, resulting in a remarkable improvement for the SNR, and the denoising process significantly enhances the detectability of DAS signals.