Background noise attenuation method of DAS seismic data based on multiscale enhanced cascade residual network
ZHONG Tie1,2, WANG Weiyu3, WANG Wei4, DONG Shiqi1,2, LU Shaoping5, DONG Xintong6
1. Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education, Jilin, Jilin 132012, China; 2. College of Electrical Engineering, Northeast Electric Power University, Jilin, Jilin 132012, China; 3. Ziyang Electric Power Supply Company, State Grid Sichuan Electric Power Company, Ziyang, Sichuan 641300, China; 4. Research Institute of Petroleum Exploration & Development-Northwest, PetroChina, Lanzhou, Gansu 730020, China; 5. School of Earth Sciences and Engineering, SUN YAT-SEN University, Guangzhou, Guangdong 510275 China; 6. College of Instrumentation & Electrical Engineering, Jilin University, Changchun, Jilin 130026, China
Abstract：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 effectively 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 capacity of DAS data significantly.
基金资助:本项研究受国家自然科学青年基金项目“基于多尺度可迁移深度学习方法的多井DAS地震数据“智普”消噪技术研究”（42204114）、第6批博士后创新人才支持计划项目“基于对抗式深度学习策略的 DAS 地震资料智能消噪系统构建”（BX2021111）、吉林省科技厅面上基金项目“基于深度学习框架的复杂地震勘探资料智能消噪技术研究”（20220101190JC）、中国石油天然气集团公司前瞻性基础性项目“物探岩石物理与前沿储备技术研究”（2021DJ3505）及中国石油股份公司科技项目“川南页岩气开发区应力变化、构造活化与可能诱发地震机理研究”（2022DJ8004）联合资助。
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