Petroleum Science >2017,??Issue 1:??61-74 DOI: https://doi.org/10.1007/s12182-016-0134-1
Regularized least-squares migration of simultaneous-source seismic data with adaptive singular spectrum analysis Open?Access
文章信息
作者：Chuang Li,Jian-Ping Huang,Zhen-Chun Li and Rong-Rong Wang
作者單位：
School of Geosciences, China University of Petroleum, Qingdao 266580, Shandong, China;School of Geosciences, China University of Petroleum, Qingdao 266580, Shandong, China;School of Geosciences, China University of Petroleum, Qingdao 266580, Shandong, China;Hisense (Shandong) Refrigerator Co. Ltd, Hisense, Qingdao 266580, Shandong, China
投稿時(shí)間：2016-04-12
引用方式：Li, C., Huang, JP., Li, ZC. et al. Pet. Sci. (2017) 14: 61. https://doi.org/10.1007/s12182-016-0134-1
文章摘要
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 RLSRTMcan 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 simultaneoussource data, incomplete data and noisy data.
關(guān)鍵詞
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Least-squares migration · Adaptive singular spectrum analysis · Regularization · Blended data