Seismic data modeling of characteristic Gaussian wave packet based on image domain decomposition

被引:1
|
作者
Shu Tao [1 ]
Yang Kai [1 ]
Wang HuaZhong [1 ]
机构
[1] Tongji Univ, Sch Ocean & Earth Sci, Shanghai 200092, Peoples R China
来源
CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION | 2022年 / 65卷 / 07期
关键词
Image domain decomposition; Gaussian packet; Seismic data modeling; Gabor function; Scattered Gaussian packet; SINGLE-SCATTERING WAVES; DEPTH MIGRATION; BEAM; PROPAGATION; COMPUTATION; INVERSION; FLOW;
D O I
10.6038/cjg2022P0400
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
The combination of wave-mode-based or spatiotemporally local-characteristic-wavefield-based tomography and prestack depth migration is one of the ways to apply full waveform inversion (FWI). How to efficiently and steadily obtain seismic data with local temporal and spatial characteristics is still a challenge. Based on the Gabor frame scattering wave simulation of Gaussian beam and Gaussian wave packet proposed by predecessors, a characteristic Gaussian wave packet seismic data modeling method based on image domain decomposition is proposed in this paper. The new method uses Gabor function with local spatial features to decompose the data on the imaging section. It can not only pick up the characteristic reflection interface by using the more intuitive geological significance of the imaging section, but also effectively overcome the low decomposition efficiency and accuracy of the traditional methods in the data domain, and ensure that the decomposed reflection data has more clear geological significance. In addition, different from the finite difference simulation of wave equation, the seismic data simulated by Gaussian wave packet based on spatiotemporal local characteristics contains more data attributes, such as arrival time of reflected wave, spatial position and propagation direction of wavefield, which has more advantages in data measurement and migration in tomography inversion. Numerical experiments show the effectiveness of the decomposition method proposed in this paper.
引用
收藏
页码:2636 / 2648
页数:13
相关论文
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