Sparse constrained encoding multi-source full waveform inversion method based on K-SVD dictionary learning  

基于K-SVD字典学习的稀疏约束编码多震源方向:全波形反演

作　　者：Guo Yun-dong Huang Jian-Ping Cui Chao LI Zhen-Chun LI Qing-Yang Wei Wei 国运东;黄建平;崔超;李振春;李庆洋;魏巍(中国石油大学(华东)地球科学与技术学院,山东青岛266580;中国石化中原油田分公司物探研究院,山东青岛266071;海洋国家实验室海洋矿产资源评价与探测技术功能实验室,濮阳457001;中国石油化工股份有限公司石油勘探开发研究院,北京100083)

机构地区：[1]School of Geosciences,China University of Petroleum(East China),Qingdao 266580,China [2]Laboratory for Marine Mineral Resources,Qingdao National Laboratory for Marine Science and Technology,Qingdao 266071,China [3]Geophysical Exploration Research Institute of Zhongyuan Oilfi eld Company,Puyang 457001,China [4]SINOPEC Petroleum Exploration and Production Research Institute,Beijing 100083,China

出　　处：《Applied Geophysics》2020年第1期111-123,169,共14页应用地球物理（英文版）

基　　金：jointly supported by the National Science and Technology Major Project(Nos.2016ZX05002-005-07HZ,2016ZX05014-001-008HZ,and 2016ZX05026-002-002HZ);National Natural Science Foundation of China(Nos.41720104006 and 41274124);Chinese Academy of Sciences Strategic Pilot Technology Special Project(A)(No.XDA14010303);Shandong Province Innovation Project(No.2017CXGC1602);Independent Innovation(No.17CX05011)。

摘　　要：Full waveform inversion(FWI)is an extremely important velocity-model-building method.However,it involves a large amount of calculation,which hindsers its practical application.The multi-source technology can reduce the number of forward modeling shots during the inversion process,thereby improving the efficiency.However,it introduces crossnoise problems.In this paper,we propose a sparse constrained encoding multi-source FWI method based on K-SVD dictionary learning.The phase encoding technology is introduced to reduce crosstalk noise,whereas the K-SVD dictionary learning method is used to obtain the basis of the transformation according to the characteristics of the inversion results.The multiscale inversion method is adopted to further enhance the stability of FWI.Finally,the synthetic subsag model and the Marmousi model are set to test the effectiveness of the newly proposed method.Analysis of the results suggest the following:(1)The new method can effectively reduce the computational complexity of FWI while ensuring inversion accuracy and stability;(2)The proposed method can be combined with the time-domain multi-scale FWI strategy flexibly to further avoid the local minimum and to improve the stability of inversion,which is of significant importance for the inversion of the complex model.全波形反演(FWI)是一种较为重要的速度建模方法,但计算量巨大是阻碍其实用化。业已证明通过多震源策略减少模拟单炮次数,可以大大提高全波形反演计算效率,但引入了交叉串扰噪音。为解决上述问题,本文提出一种基于K-SVD字典学习的稀疏约束编码多震源全波形反演方法。首先,增加不同单炮的差异性引入极性编码策略减少串扰噪音;其次基于FWI不同迭代次数反演结果特征引入K-SVD字典学习方法计算变换基函数,推导了基于稀疏约束的目标泛函;进一步我们引入基于维纳滤波的时间域多尺度反演方法,提高反演方法的稳定性。最后,通过洼陷模型和Marmousi模型测试验证表明:1)本文的基于K-SVD字典学习的多震源编码反演方法,在减少全波形反演计算量的同时,能有效克服反演串扰噪音,提高反演精度;2)新方法能灵活的与时间域多尺度反演方法结合,降低反演过程陷入局部极小值,增强反演稳定性,对复杂模型也具有较好的适应性。

关 键 词：K-SVD dictionary sparsity constraint polarity encoding MULTI-SOURCE full waveform inversion 

分 类 号：P631.4[天文地球—地质矿产勘探]