Seismic data reconstruction based on low dimensional manifold model

作     者：Nan-Ying Lan Fan-Chang Zhang Xing-Yao Yin Nan-Ying Lan;Fan-Chang Zhang;Xing-Yao Yin

作者机构：School of GeosciencesChina University of Petroleum(East China)QingdaoShandong 266580China 

出 版 物：《Petroleum Science》 (石油科学（英文版）)

年 卷 期：2022年第19卷第2期

页      面：518-533页

核心收录：

学科分类：0820[工学-石油与天然气工程] 081801[工学-矿产普查与勘探] 08[工学] 0818[工学-地质资源与地质工程] 

基　　金：supported by National Natural Science Foundation of China(Grant No.41874146 and No.42030103) Postgraduate Innovation Project of China University of Petroleum(East China)(No.YCX2021012) 

主　　题：Seismic data reconstruction Low dimensional manifold model Regularization Low-rank approximation 

摘      要：Seismic data reconstruction is an essential and yet fundamental step in seismic data processing workflow,which is of profound significance to improve migration imaging quality,multiple suppression effect,and seismic inversion accuracy.Regularization methods play a central role in solving the underdetermined inverse problem of seismic data reconstruction.In this paper,a novel regularization approach is proposed,the low dimensional manifold model(LDMM),for reconstructing the missing seismic data.Our work relies on the fact that seismic patches always occupy a low dimensional manifold.Specifically,we exploit the dimension of the seismic patches manifold as a regularization term in the reconstruction problem,and reconstruct the missing seismic data by enforcing low dimensionality on this manifold.The crucial procedure of the proposed method is to solve the dimension of the patches manifold.Toward this,we adopt an efficient dimensionality calculation method based on low-rank approximation,which provides a reliable safeguard to enforce the constraints in the reconstruction process.Numerical experiments performed on synthetic and field seismic data demonstrate that,compared with the curvelet-based sparsity-promoting L1-norm minimization method and the multichannel singular spectrum analysis method,the proposed method obtains state-of-the-art reconstruction results.