Regularized least-squares migration of simultaneous-source seismic data with adaptive singular spectrum analysis

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• 作者：Chuang Li ; Jian-Ping Huang ; Zhen-Chun Li ; Rong-Rong Wang
• 关键词：Least ; squares migration ; Adaptive singular spectrum analysis ; Regularization ; Blended data
• 刊名：Petroleum Science
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：14
• 期：1
• 页码：61-74
• 全文大小：3103KB
• 刊物主题：Mineral Resources; Industrial Chemistry/Chemical Engineering; Industrial and Production Engineering; Energy Economics;
• 出版者：China University of Petroleum (Beijing)
• ISSN：1995-8226
• 卷排序：14

文摘

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.