Least-squares reverse time migration with a hybrid stochastic conjugate gradient
Li Chuang1,2, Huang Jianping1,2, Li Zhenchun1,2, Wang Rongrong3, Sun Miaomiao14
1. School of Geosciences, China University of Petroleum(East China), Qingdao, Shandong 266580, China; 2. Laboratory for Marine Mineral Resources, Pilot National Laboratory for Marine Science and Technology, Qingdao, Shandong 266580, China; 3. Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China; 4. Shengli Branch, Petroleum Engineering Geophysical Limited Corporation, SINOPEC, Dongying, Shandong 257088, China
Abstract：To improve the efficiency of the least-squares reverse time migration (LSRTM),a hybrid stochastic conjugate gradient method is derived with the stochastic optimization theory.The correlation-based shot sampling method is proposed to reduce the amount of migrated data set,which also reduces stochastic characteristics of the gradient.After sampling shot data,the stochastic gradient descent iteration and the conjugate gradient iteration are used alternatively to update the migration imaging.The conjugate gradient iteration improves the convergence efficiency and image quality of LSRTM.Numerical tests on the synthetic data of SEG/EAGE rugged topography model and 2D field data from the exploration area M show that the proposed method can improve the computational efficiency of standard LSRTM,and has higher image quality and faster convergence compared with the stochastic gradient descent method.
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