孙苗苗^1,2,,
李振春¹,
李志娜^1,,,
李庆洋³,
李闯¹,
张怀榜²
1. 中国石油大学(华东)地球科学与技术学院, 山东青岛 266580
2. 中石化石油工程地球物理有限公司胜利分公司, 山东东营 257088
3. 中原油田分公司物探研究院, 河南濮阳 457001

基金项目: 国家重点研发计划（2016YFC060110501）；国家自然科学基金（41604103）；国家重大专项（2017ZX05005004-03）；国家科技重大专项（2016ZX05026-002-002，2016ZX05006-002-003）；中央高校基本科研业务费专项资金（18CX02009A，18CX02062A）资助


详细信息

作者简介: 孙苗苗, 女, 1981年生, 在读博士, 主要从事地震数据高分辨率处理等方面的研究.E-mail:upcmiaomiao@163.com

通讯作者: 李志娜, 女, 1986年生, 讲师, 主要从事地震波传播与成像研究.E-mail:lizhina00@163.com

中图分类号: P631

收稿日期:2018-02-03
修回日期:2018-11-16
上线日期:2019-03-05

Reconstruction of seismic data with weighted MCA based on compressed sensing

SUN MiaoMiao^1,2,,
LI ZhenChun¹,
LI ZhiNa^1,,,
LI QingYang³,
LI Chuang¹,
ZHANG HuaiBang²
1. School of Geosciences, China University of Petroleum, Shandong Qingdao 266580, China
2. SGC Shengli, Shandong Dongying 257088, China
3. Geophysical Exploration Research Institute of Zhongyuan Oilfield Company, Henan Puyang 457001, China


More Information

Corresponding author: LI ZhiNa,E-mail:lizhina00@163.com

MSC: P631

--> Received Date: 03 February 2018
Revised Date: 16 November 2018
Available Online: 05 March 2019


摘要
摘要:地震数据规则化重构是地震资料处理十分重要的基础性工作.压缩感知理论打破了香农采样定理的制约，利用信号在某个变换域的稀疏特性重构出完整的信号，在地震数据重构领域得到了很好的应用.深反射地震剖面大都布置在地质构造比较复杂的区段，复杂的地质构造使深反射地震剖面上的波阻特征复杂，采用单一稀疏变换不能最有效地表征数据的内部结构特征.MCA（形态成分分析）方法将信号分解为几种形态特征区别明显的分量来逼近数据的内部复杂结构，但是对各成分简单的叠加仍然无法有效地描述复杂构造数据的各种特征.结合两种方法的优点，本文提出了一种新的基于压缩感知的重构算法框架，在MCA方法的基础上对各稀疏字典进行加权，在迭代中不断更新各个稀疏字典的权值系数，对信号内部的各种特征进行最优描述，从而实现对信号的高质量重构.模型测试和实际资料处理结果表明：基于压缩感知的加权MCA方法不仅可以对地质构造复杂的地震数据进行高效的插值重建，而且可以很好的消除空间假频.
关键词: 地震数据重构/
加权MCA/
压缩感知/
稀疏表示

Abstract:The regularized reconstruction of seismic data is one of the most important and fundamental task in seismic data processing. The compressed sensing (CS) theory has been successfully applied in the reconstruction of seismic data, because it breaks the restriction of Shannon sampling theorem and can obtain a complete reconstructed signal by using the sparsity property in a certain transformation domain. Deep reflection seismic profiles commonly deployed in areas with complicated geological structure, resulted in complicated wave impedance characteristics on deep reflection seismic profiles. Consequently, the internal structure characteristics of data cannot be represented effectively by using a single sparse transformation. The morphological component analysis (MCA) method decomposes a signal into several components with distinguished morphological features to approximate the complex internal structure of data. However, the various characteristics of the complex data still cannot be described effectively through a simple summation of the signal components. To solve these problems, a new iterative algorithm frame was proposed based on the combination of the merits of the CS and MCA methods. The sparse dictionaries were weighted on the basis of the MCA method, and then the weight coefficients of each sparse dictionary was updated continuously in the process of iteration. Finally an optimal description of the various internal features of signals was obtained and a high-quality reconstruction was achieved for those complex signals. Model tests and real data processing results show that the weighted MCA method based on CS can not only reconstruct the seismic data from the complicated geological structure through interpolation efficiently, but also eliminate the space aliasing very well.
Key words:Seismic data reconstruction/
Weighted morphological component analysis (WMCA)/
Compressed sensing (CS)/
Sparse representation



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