基于多尺度增强级联残差网络的DAS地震资料背景噪声衰减方法  

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

作　　者：钟铁 王玮钰 王伟 董士琦 卢绍平 董新桐 ZHONG Tie;WANG Weiyu;WANG Wei;DONG Shiqi;LU Shaoping;DONG Xintong(Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology,Ministry of Education,Jilin,Jilin 132012,China;College of Electrical Engineering,Northeast Electric Power University,Jilin,Jilin 132012,China;Ziyang Electric Power Supply Company,State Grid Sichuan Electric Power Company,Ziyang,Sichuan 641300,China;Research Institute of Petroleum Exploration&Development-Northwest,PetroChina,Lanzhou,Gansu 730020,China;School of Earth Sciences and Engineering,SUN YAT-SEN University,Guangzhou,Guangdong 510275 China;College of Instrumentation&Electrical Engineering,Jilin University,Changchun,Jilin 130026,China)

机构地区：[1]现代电力系统仿真控制与绿色电能新技术教育部重点实验室,吉林吉林132012 [2]东北电力大学电气工程学院,吉林吉林132012 [3]国网四川省电力公司资阳供电公司,四川资阳641300 [4]中国石油勘探开发研究院西北分院,甘肃兰州730020 [5]中山大学地球科学与工程学院,广东广州510275 [6]吉林大学仪器科学与电气工程学院,吉林长春130026

出　　处：《石油地球物理勘探》2023年第6期1332-1342,共11页Oil Geophysical Prospecting

基　　金：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);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。

摘　　要：由于复杂强背景噪声的影响,分布式光纤声学传感(Distributed Optical Fiber Acoustic Sensing,DAS)采集的地震记录普遍信噪比较低。如何有效抑制背景噪声,恢复弱上行反射信息,切实提升DAS记录信噪比,已成为资料处理领域的热点问题之一。针对复杂DAS背景噪声消减问题,提出了一种多尺度增强级联残差网络(Multiscale Enhanced Cascade Residual Network,MECRN)。MECRN具有双路径级联残差网络结构,通过双路径机制提取DAS记录浅层信息。在此基础上,引入空洞卷积和多尺度模块提取DAS记录的多尺度特征,并通过跳跃连接导入浅层特征,在避免有效特征损失的同时,提升网络的特征提取能力。最后,通过残差学习整合局部和全局特征,并对重建特征细化,进一步提升了MECRN的去噪能力。模拟和实际DAS资料处理结果均表明,MECRN可以有效地压制DAS记录中的复杂背景噪声,准确恢复弱反射信号,显著提升处理DAS资料的能力。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.

关 键 词：分布式光纤声学传感(DAS) 复杂背景噪声 多尺度增强级联残差网络 低信噪比 噪声衰减 

分 类 号：P631[天文地球—地质矿产勘探]