Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5787167 | Journal of Applied Geophysics | 2017 | 29 Pages |
Abstract
Full-waveform inversion (FWI) is an ill-posed optimization problem which is sensitive to noise and initial model. To alleviate the ill-posedness of the problem, regularization techniques are usually adopted. The â1-norm penalty is a robust regularization method that preserves contrasts and edges. The Orthant-Wise Limited-Memory Quasi-Newton (OWL-QN) method extends the widely-used limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method to the â1-regularized optimization problems and inherits the efficiency of L-BFGS. To take advantage of the â1-regularized method and the prior model information obtained from sonic logs and geological information, we implement OWL-QN algorithm in â1-regularized FWI with prior model information in this paper. Numerical experiments show that this method not only improve the inversion results but also has a strong anti-noise ability.
Keywords
Related Topics
Physical Sciences and Engineering
Earth and Planetary Sciences
Geophysics
Authors
Meng-Xue Dai, Jing-Bo Chen, Jian Cao,