石油物探2018，Vol.57Issue(1)：33-38,6.DOI:10.3969/j.issn.1000-1441.2018.01.004
基于稀疏反演的同步震源地震数据分离方法
Separation of simultaneous source data based on sparse inversion
摘要
Abstract
Separation of blended seismic data acquired in simultaneous source acquisition is substantially necessary.A sparse inver-sion-based method to separate blended data in case of a random time-dithering scheme is presented in this paper.The first step is the extraction of block training data from clean shot gathers without blending.The extracted data is used to train a learned dictiona-ry through the K-SVD algorithm,based on sparse representation and patch-wise dictionary learning.An inverse problem expression for the separation of blended data was then developed based on sparse inversion.We used the sparse representation of blended data as regularization constraint and performed an alternate iterative scheme to update the separated recovery data and sparse coeffi-cients respectively.Testing on the synthetic and field data demonstrated that the recovery data obtained from dictionary learning had better separation accuracy compared to that based on two dimensional fixed local discrete cosine transform.关键词
同步震源采集/字典学习/稀疏反演/地震记录分离/稀疏表示Key words
simultaneous source acquisition/dictionary learning/sparse inversion/separation of blended seismic data/sparse repre-sentation分类
天文与地球科学引用本文复制引用
周艳辉,陈文超..基于稀疏反演的同步震源地震数据分离方法[J].石油物探,2018,57(1):33-38,6.基金项目
国家自然科学基金项目(41504093,41774135)和陕西省工业攻关项目(2015GY058)共同资助. This research is financially supported by National Natural Science Foundation of China (Grant Nos.41504093,41774135 )and the Industrial re-search project of Science and Technology Department of Shaanxi Province (Grant No.2015GY058). （41504093,41774135）
