基于压缩感知理论的远震P波数据重建研究  

Teleseismic P-wave data reconstruction based on compressive sensing theory

作　　者：杨歧焱 吴庆举[3] 魏亚杰 曹静杰 蔡志成 杨志权 盛艳蕊[4] Yang Qiyan;Wu Qingju;Wei Yajie;Cao Jingjie;Cai Zhicheng;Yang Zhiquan;Sheng Yanrui(Key Laboratory of Intelligent Detection and Equipment for Underground Space of Beijing-Tianjin-Hebei Urban Agglomeration,Ministry of Natural Resources,Hebei GEO University,Shijiazhuang 050031,China;Hebei Key Laboratory of Strategic Critical Mineral Resources,Hebei GEO University,Shijiazhuang 050031,China;Institute of Geophysics,China Earthquake Administration,Beijing 100081,China;Hebei Earthquake Agency,Shijiazhuang 050021,China)

机构地区：[1]河北地质大学自然资源部京津冀城市群地下空间智能探测与装备重点实验室,石家庄050031 [2]河北地质大学河北省战略性关键矿产资源重点实验室,石家庄050031 [3]中国地震局地球物理研究所,北京100081 [4]河北省地震局,石家庄050021

出　　处：《地震学报》2024年第3期413-424,共12页Acta Seismologica Sinica

基　　金：河北省地震科技星火计划项目红山野外站科研专项(DZ2024011800001,DZ2021121700005);河北地质大学2023年国家预研项目(KY202311);2024年度河北省引进国外智力项目;河北地质大学科技创新团队项目;河北省高等学校百名优秀创新人才支持计划(Ⅲ)(SLRC2017024)共同资助。

摘　　要：本文将基于压缩感知理论的地震观测数据重建方法用于天然远震事件的P波到时处理之中,基于曲波(curvelet)变换,建立基于L_(1)范数的正则化反演模型,并采用迭代收缩阈值算法(ISTA)求解该模型。针对在内蒙古布设的流动地震台阵记录到的远震波形数据,对其进行稀疏采样,采用稀疏反演重建方法对欠采样数据进行重建,并拾取重建数据的P波到时,之后开展远震P波层析成像进行验证。研究结果表明,远震天然地震观测数据在曲波变换中表现出稀疏性,可利用压缩感知方法实现远震P波数据的完备化处理。基于内蒙古流动地震台阵数据三维P波成像也表明,基于压缩感知的数据重建技术可以提高地震层析成像的分辨率,且压缩感知采集技术在天然地震研究中具有潜在的应用价值。The non-uniformity and incompleteness of seismic data in space have long been one of the significant challenges affecting seismic imaging.The factors that cause the non-uniform spatial distribution of seismic data are primarily twofold:Firstly,the non-uniform distribution of earthquakes,which are the sources of seismic waves;Secondly,the non-uniform distribution of seismic stations used to record these waves.In areas where stations are difficult to built,such as mountainous regions and offshore locations,uniform array deployment becomes impractical.These factors lead to the acquisition of non-uniform and incomplete data,thereby reducing the resolution and accuracy of the imaging results.The core principle of compressed sensing theory is to exploit the sparse nature of the signal and then employ a non-linear reconstruction algorithm to recover the original signal.Since seismic wavefields exhibit continuity,then missing or irregularly sampled seismic data can potentially be recovered through compressed sensing techniques.In the time domain,seismic observation data usually contain rich frequency information.However,due to the filtering effects of subsurface layers,the bandwidth of the actual recorded seismic data is limited,and the data exhibit sparsity in the frequency domain.Based on this characteristic,under limited acquisition conditions,the reconstruction of missing seismic data utilizing compressed sensing theory can,to some extent,mitigate the problem of insufficient seismic data coverage.In oil and gas seismic exploration,data reconstruction methods based on compressed sensing theory have been widely applied to address the issue of insufficient sampling.However,relevant discussions are not yet common in the acquisition and processing of natural earthquake data.In fact,due to environmental and site constraints,the acquisition of natural earthquake data also suffers missing and irregular sampling issues.Moreover,owing to the irregular distribution of acquisition stations,the reconstruction of natural earthquak

关 键 词：压缩感知 曲波变换 远震P波 数据重建 L_(1)范数 

分 类 号：P315.61[天文地球—地震学]