Reflection-based traveltime and waveform inversion with second-order optimization

作     者：Teng-Fei Wang Jiu-Bing Cheng Jian-Hua Geng Teng-Fei Wang;Jiu-Bing Cheng;Jian-Hua Geng

作者机构：State Key Laboratory of Marine GeologyTongji UniversityShanghai200092China School of Ocean and Earth ScienceTongji UniversityShanghai200092China 

出 版 物：《Petroleum Science》 (石油科学（英文版）)

年 卷 期：2022年第19卷第4期

页      面：1582-1591页

核心收录：

学科分类：081801[工学-矿产普查与勘探] 081802[工学-地球探测与信息技术] 08[工学] 0818[工学-地质资源与地质工程] 

基　　金：supported by National Natural Science Foundation of China (42074157) the National Key Research and Development Program of China (2018YFC0310104) the Strategic Priority Research Program of the Chinese Academy of Science(XDA14010203) 

主　　题：Reflection waveform inversion Reflection traveltime inversion Gauss-Newton Hessian 

摘      要：Reflection-based inversion that aims to reconstruct the low-to-intermediate wavenumbers of the subsurface model, can be a complementary to refraction-data-driven full-waveform inversion(FWI), especially for the deep target area where diving waves cannot be acquired at the surface. Nevertheless, as a typical nonlinear inverse problem, reflection waveform inversion may easily suffer from the cycleskipping issue and have a slow convergence rate, if gradient-based first-order optimization methods are used. To improve the accuracy and convergence rate, we introduce the Hessian operator into reflection traveltime inversion(RTI) and reflection waveform inversion(RWI) in the framework of second-order optimization. A practical two-stage workflow is proposed to build the velocity model, in which Gauss-Newton RTI is first applied to mitigate the cycle-skipping problem and then Gauss-Newton RWI is employed to enhance the model resolution. To make the Gauss-Newton iterations more efficiently and robustly for large-scale applications, we introduce proper preconditioning for the Hessian matrix and design appropriate strategies to reduce the computational costs. The example of a real dataset from East China Sea demonstrates that the cascaded Hessian-based RTI and RWI have good potential to improve velocity model building and seismic imaging, especially for the deep targets.