A time-domain multi-parameter elastic full waveform inversion with pseudo-Hessian preconditioning
Huang Jian-ping,*, Liu Zhang, Jin Ke-jie, Ba Kai-lun, Liu Yu-hang, Kong Ling-hang, Cui Chao, li Chuang
1. School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China.
2. Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China.
3. Institute of Exploration and Development, SINOPEC Shanghai Off shore Oil & Gas Company, Shanghai 200120, China.
Abstract：
Based on waveform fitting, full waveform inversion (FWI) is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However, the strong nonlinear equation used in FWI presents the following challenges, such as low convergence efficiency, high dependence on the initial model, and the energy imbalance in deep region of the inverted model. To solve these inherent problems, we develop a timedomain elastic FWI method based on gradient preconditioning with the following details: (1) the limited memory Broyden Fletcher Goldfarb Shanno method with faster convergence is adopted to im-prove the inversion stability; (2) a multi-scaled inversion strategy is used to alleviate the nonlinear inversion instead of falling into the local minimum; (3) in addition, the pseudo-Hessian preconditioned illumination operator is involved for preconditioning the parameter gradients to improve the illumination equilibrium degree of deep structures. Based on the programming implementation of the new method, a deep depression model with five diffractors is used for testing. Compared with the conventional elastic FWI method, the technique proposed by this study has better effectiveness and accuracy on the inversion effect and con-vergence, respectively.
基金资助:This research is supported by the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao) (Grant No. 2021QNLM020001), the National Key R&D Program of China (Grant No.2019YFC0605503C), the Major Scientifi c and Technological Projects of China National Petroleum Corporation (CNPC) (Grant No. ZD2019-183-003), the National Outstanding Youth Science Foundation (Grant No. 41922028), and the National Innovation Group Project (Grant No. 41821002).
作者简介: Huang Jian-ping, received his PHD’s degree from University of Science and Technology of China. Now, He is a professor in China University of Petroleum (East China). His research focuses on forward modeling and migration. E-mail: jphuang@upc.edu.cn
2025, Vol. 22 
