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Труды Института математики и механики УрО РАН, 2012, том 18, номер 1, страницы 42–55
(Mi timm778)
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Эта публикация цитируется в 7 научных статьях (всего в 7 статьях)
Sparse optimization methods for seismic wavefields recovery
Y. F. Wang Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, P. R. China
Аннотация:
Due to the influence of variations in landform, geophysical data acquisition is usually sub-sampled. Reconstruction of the seismic wavefield from sub-sampled data is an ill-posed inverse problem. It usually requires some regularization techniques to tackle the ill-posedness and provide a stable approximation to the true solution. In this paper, we consider the wavefield reconstruction problem as a compressive sensing problem. We solve the problem by constructing different kinds of regularization models and study sparse optimization methods for solving the regularization model. The $l_p$-$l_q$ model with $p=2$ and $q=0,1$ is fully studied. The projected gradient descent method, linear programming method and an $l_1$-norm constrained trust region method are developed to solve the compressive sensing problem. Numerical results demonstrate that the developed approaches are robust in solving the ill-posed compressive sensing problem and can greatly improve the quality of wavefield recovery.
Ключевые слова:
seismic inversion, optimization, sparsity, regularization.
Поступила в редакцию: 10.05.2011
Образец цитирования:
Y. F. Wang, “Sparse optimization methods for seismic wavefields recovery”, Тр. ИММ УрО РАН, 18, no. 1, 2012, 42–55
Образцы ссылок на эту страницу:
https://www.mathnet.ru/rus/timm778 https://www.mathnet.ru/rus/timm/v18/i1/p42
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