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Journal of Inverse and Ill-posed Problems

Editor-in-Chief: Kabanikhin, Sergey I.


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1569-3945
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Volume 21, Issue 1

Issues

Data regularization using Gaussian beams decomposition and sparse norms

Yanfei Wang
  • Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, P. O. Box 9825, Beijing 100029, P. R. China
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/ Peng Liu
  • Lanzhou Center for Oil and Gas Resources, Institute of Geology and Geophysics, Chinese Academy of Sciences, Lanzhou, 730000, P. R. China; and University of Chinese Academy of Sciences, Beijing 100049, P. R. China
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/ Zhenhua Li
  • Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, P. O. Box 9825, Beijing 100029, P. R. China; and University of Chinese Academy of Sciences, Beijing 100049, P. R. China
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/ Tao Sun
  • Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, P. O. Box 9825, Beijing 100029, P. R. China; and University of Chinese Academy of Sciences, Beijing 100049, P. R. China
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/ Changchun Yang
  • Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, P. O. Box 9825, Beijing 100029, P. R. China
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Published Online: 2013-02-01 | DOI: https://doi.org/10.1515/jip-2012-0030

Abstract.

We consider seismic data regularization problems in this paper. Gaussian beam decomposition model is proposed for seismic data representation. To solve the representation problem, an l0 quasi-norm minimization model with different smooth approximations is proposed. To solve the l0 quasi-norm minimization problem, a projected gradient method with nonmonotone choice of iterative steps is developed. Numerical simulations on one-dimensional and two-dimensional seismic imaging problems are performed to verify the feasibility of our methods.

Keywords: Data regularization; Gaussian beams; l0 optimization; sparse inversion

About the article

Received: 2012-05-08

Published Online: 2013-02-01

Published in Print: 2013-02-01


Citation Information: Journal of Inverse and Ill-Posed Problems, Volume 21, Issue 1, Pages 1–23, ISSN (Online) 1569-3945, ISSN (Print) 0928-0219, DOI: https://doi.org/10.1515/jip-2012-0030.

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