Abstract
Numerical solutions of the acoustic wave equation, especially in anisotropic media, is crucial to seismic modeling, imaging and inversion as it provides efficient, practical, and stable approximate representation of the medium. However, a clean implementation (free of shear wave artifacts and dispersion) of wave propagation, especially in anisotropic media, requires an integral operator, the direct evaluation of which is extremely expensive. Recently, the low-rank method was proposed to provide a good approximation to the integral operator utilizing Fourier transforms. Thus, we propose to split the integral operator into two terms. The first term provides a differential operator that approximates that can be approximated with a standard finite-difference method. We, then, apply the lowrank approximation on the residual term of the finite-difference operator. We implement the two terms in two complementing steps, in which the spectral step corrects for any errors admitted by the finite difference step. Even though we utilize finite-difference approximations, the resulting algorithm admits spectral accuracy. Also, through the finite difference step, the method can deal approximately with the free surface and absorbing boundary conditions in a straight forward manner. Numerical examples show that the method is of high accuracy and efficiency.
Original language | English (US) |
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Title of host publication | SEG Technical Program Expanded Abstracts 2018 |
Publisher | Society of Exploration Geophysicists |
Pages | 3953-3958 |
Number of pages | 6 |
DOIs | |
State | Published - Aug 27 2018 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledgements: We thank KAUST for its support and the SWAG group for the collaborative environment. We also thank BP for providing the benchmark dataset. The numerical examples are reproducible using the open source package Madagascar(http://www. ahay.org/wiki/Main_Page). The research reported in this publication is supported by funding from King Abdullah University of Science and Technology (KAUST). We thank the computing support from the Supercomputing Laboratory at King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia.