Seismic modeling by combining the finite-difference scheme with the numerical dispersion suppression neural network

作     者：Hong-Yong Yan 

作者机构：Key Laboratory of Petroleum Resources ResearchInstitute of Geology and GeophysicsChinese Academy of SciencesBeijing 100029China Innovation Academy for Earth ScienceChinese Academy of SciencesBeijing 100029China 

出 版 物：《Petroleum Science》 (石油科学（英文版）)

年 卷 期：2024年第21卷第5期

页      面：3157-3165页

核心收录：

学科分类：070801[理学-固体地球物理学] 07[理学] 0708[理学-地球物理学] 

基　　金：supported by the National Natural Science Foundation of China (grant numbers: 41874160 and 92055213) 

主　　题：Finite difference Seismic modeling Numerical dispersion suppression Computational accuracy Computational efficiency 

摘      要：Seismic finite-difference(FD) modeling suffers from numerical dispersion including both the temporal and spatial dispersion, which can decrease the accuracy of the numerical modeling. To improve the accuracy and efficiency of the conventional numerical modeling, I develop a new seismic modeling method by combining the FD scheme with the numerical dispersion suppression neural network(NDSNN). This method involves the following steps. First, a training data set composed of a small number of wavefield snapshots is generated. The wavefield snapshots with the low-accuracy wavefield data and the high-accuracy wavefield data are paired, and the low-accuracy wavefield snapshots involve the obvious numerical dispersion including both the temporal and spatial dispersion. Second, the NDSNN is trained until the network converges to simultaneously suppress the temporal and spatial ***, the entire set of low-accuracy wavefield data is computed quickly using FD modeling with the large time step and the coarse grid. Fourth, the NDSNN is applied to the entire set of low-accuracy wavefield data to suppress the numerical dispersion including the temporal and spatial *** modeling examples verify the effectiveness of my proposed method in improving the computational accuracy and efficiency.