Optimal Generalized Discrete Shannon Singular Kernel Staggered Grid Convolutional Differentiator for Seismic Wave Modeling
详细信息
- 责任者：Liu Shaolin (Key Laboratory of Earth Interior ; Institute of Geology and Geophysics ; Chinese Academy of Sciences ; Beijing 100029 ; China) ; Li Xiaofan
- 刊名：Geophysical and Geochemical Exploration
- 出版年：2013
- 卷期：37(3)
- 关键词：数值模拟 ; numerical simulation ; 弹性波 ; elastic waves ; 地质建模 ; geological modeling
- 页码：p.473-479
- 图表：illus., 2 tables, 36 refs.
- 分类号：1600
- issnNo：ISSN1000-8918
- isbnNo：CN11-1906/P
摘要
This paper deals with the generalized discrete Shannon convolutional differentiator(GDSCD) for solving seismic velocitystress equation.The first-order convolution differentiators are provided.An efficient method is proposed to optimize the coefficients of the convolution differentiator.A series of optimal coefficients are obtained for various operator lengths and sampling rates per shortest wave length.The operator accuracy is discussed through filter response.A comparison with various numerical methods and numerical experiments show that the new designed staggered grid convolution differentiator has high accuracy and efficiency for seismic wave modeling.
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