title: Regularized least-squares migration of simultaneous-source seismic data with adaptive singular spectrum analysis.
abstract: Simultaneous-source acquisition has been recognized as an economic and efficient acquisition method, but the direct imaging of the simultaneous-source data produces migration artifacts because of the interference of adjacent sources. To overcome this problem, we propose the regularized least-squares reverse time migration method (RLSRTM) using the singular spectrum analysis technique that imposes sparseness constraints on the inverted model. Additionally, the difference spectrum theory of singular values is presented so that RLSRTM can be implemented adaptively to eliminate the migration artifacts. With numerical tests on a flat layer model and a Marmousi model, we validate the superior imaging quality, efficiency and convergence of RLSRTM compared with LSRTM when dealing with simultaneous-source data, incomplete data and noisy data.
authors.affiliation: School of Geosciences, China University of Petroleum, Qingdao, 266580 Shandong China.
authors.forename: Chuang
authors.initials: C
authors.lastname: Li
authors.affiliation: School of Geosciences, China University of Petroleum, Qingdao, 266580 Shandong China.
authors.forename: Jian-Ping
authors.initials: JP
authors.lastname: Huang
authors.affiliation: School of Geosciences, China University of Petroleum, Qingdao, 266580 Shandong China.
authors.forename: Zhen-Chun
authors.initials: ZC
authors.lastname: Li
authors.affiliation: Hisense (Shandong) Refrigerator Co. Ltd, Hisense, Qingdao, 266580 Shandong China.
authors.forename: Rong-Rong
authors.initials: RR
authors.lastname: Wang
keywords: Adaptive singular spectrum analysis; Blended data; Least-squares migration; Regularization