Accelerating full waveform inversion using HSS solver and limited memory conjugate gradient method
Full waveform inversion (FWI) consists in finding an accurate optimal model of the subsurface from local measurements of the seismic wavefield. This aim is achieved by minimizing the difference between the observed and predicted data, starting from an initial estimation of the subsurface parameters....
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| Veröffentlicht in: | Journal of applied geophysics Jg. 159; S. 83 - 92 |
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| Sprache: | Englisch |
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Elsevier B.V
01.12.2018
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| ISSN: | 0926-9851, 1879-1859 |
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| Abstract | Full waveform inversion (FWI) consists in finding an accurate optimal model of the subsurface from local measurements of the seismic wavefield. This aim is achieved by minimizing the difference between the observed and predicted data, starting from an initial estimation of the subsurface parameters. One challenge for FWI is its intensive large-scale wavefield simulations, which seriously restricts its wide applications. Additionally, due to the ill-posedness of FWI problem, when the nonlinear conjugate gradient method is employed, the current gradient often lies in the space spanned by the previous directions, resulting in very slow convergence. In this paper, a limited memory version conjugate gradient method equipped with the scalable HSS-structured multifrontal solver is applied to efficiently solve the FWI problem. A hierarchically preconditioned scheme is considered to enhance the robustness of the inversion algorithm. Numerical experiments including 2D and 3D are illustrated to show the performances of this high efficient inversion algorithm.
•Multiforntal solver with HSS compression is employed to efficiently solve forward problems.•Nonlinear CG method guaranteed descent is accelerated with limited memory technique.•Numerical performances for this limited memory version CG method are conducted for 2D and 3D FWI problems. |
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| AbstractList | Full waveform inversion (FWI) consists in finding an accurate optimal model of the subsurface from local measurements of the seismic wavefield. This aim is achieved by minimizing the difference between the observed and predicted data, starting from an initial estimation of the subsurface parameters. One challenge for FWI is its intensive large-scale wavefield simulations, which seriously restricts its wide applications. Additionally, due to the ill-posedness of FWI problem, when the nonlinear conjugate gradient method is employed, the current gradient often lies in the space spanned by the previous directions, resulting in very slow convergence. In this paper, a limited memory version conjugate gradient method equipped with the scalable HSS-structured multifrontal solver is applied to efficiently solve the FWI problem. A hierarchically preconditioned scheme is considered to enhance the robustness of the inversion algorithm. Numerical experiments including 2D and 3D are illustrated to show the performances of this high efficient inversion algorithm.
•Multiforntal solver with HSS compression is employed to efficiently solve forward problems.•Nonlinear CG method guaranteed descent is accelerated with limited memory technique.•Numerical performances for this limited memory version CG method are conducted for 2D and 3D FWI problems. |
| Author | He, Qinglong Han, Bo |
| Author_xml | – sequence: 1 givenname: Qinglong surname: He fullname: He, Qinglong email: qlhe@gzu.edu.cn organization: School of Mathematics and Statistics, Guizhou University, Guiyang 550025, China – sequence: 2 givenname: Bo surname: Han fullname: Han, Bo organization: Department of Mathematics, Harbin Institute of Technology, Harbin 150001, China |
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| CitedBy_id | crossref_primary_10_1016_j_jappgeo_2020_103947 crossref_primary_10_1190_geo2019_0382_1 crossref_primary_10_1093_jge_gxz101 crossref_primary_10_1016_j_jappgeo_2022_104639 crossref_primary_10_1007_s11600_025_01588_4 crossref_primary_10_1016_j_apm_2021_10_016 crossref_primary_10_1016_j_physd_2024_134518 crossref_primary_10_1016_j_cageo_2025_105984 crossref_primary_10_1016_j_cam_2021_113896 |
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| Keywords | Conjugate gradient method Full waveform inversion Seismic imaging Limited memory Hierarchically semi-separable matrix |
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| Snippet | Full waveform inversion (FWI) consists in finding an accurate optimal model of the subsurface from local measurements of the seismic wavefield. This aim is... |
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| SubjectTerms | Conjugate gradient method Full waveform inversion Hierarchically semi-separable matrix Limited memory Seismic imaging |
| Title | Accelerating full waveform inversion using HSS solver and limited memory conjugate gradient method |
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