Background noise attenuation method of DAS seismic data based on multiscale enhanced cascade residual network

Seismic records collected through distributed optical fiber acoustic sensing（DAS）typically exhibit a low signal-to-noise ratio（SNR）due to the pervasive influence of complex and intense background noise. How to effec- tively suppress background noise，restore weak upgoing reflection information，and substantially improve the SNR of DAS records havs become a prominent challenge in seismic data processing. To address the issue of complex DAS background noise attenuation，this paper proposes a multiscale enhanced cascade residual network（MECRN），which employs a dual-path cascade residual network structure to extract shallow information from DAS records. On this basis，dilated convolutional layers and multiscale modules are introduced to extract the multiscale features existing in DAS records. Additionally，skip connections are introduced to import shallow features，which enhances the feature extraction capability of MECRN and avoids effective feature loss. Finally，the local and global features are integrated by residual learning，and the reconstructed features are refined to improve the denoising capabilities of MECRN. The processing results from both simulated and field DAS data demonstrate that MECRN can effectively suppresses complex DAS background noise and accurately restores weak reflection signals，which enhances the processing ca- pacity of DAS data significantly. © 2023 Editorial office of Oil Geophysical Prospecting. All rights reserved.