ℓ 1 -Regularized full-waveform inversion with prior model information based on orthant-wise limited memory quasi-Newton method
| Autor: | Jian Cao, Meng-Xue Dai, Jing-Bo Chen |
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| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
Optimization problem 010504 meteorology & atmospheric sciences Inversion (meteorology) 010502 geochemistry & geophysics 01 natural sciences Regularization (mathematics) Orthant Geophysics Norm (mathematics) Quasi-Newton method Algorithm Full waveform 0105 earth and related environmental sciences Mathematics |
| Zdroj: | Journal of Applied Geophysics. 142:49-57 |
| ISSN: | 0926-9851 |
| DOI: | 10.1016/j.jappgeo.2017.03.020 |
| Popis: | 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 l 1 -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 l 1 -regularized optimization problems and inherits the efficiency of L-BFGS. To take advantage of the l 1 -regularized method and the prior model information obtained from sonic logs and geological information, we implement OWL-QN algorithm in l 1 -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. |
| Databáze: | OpenAIRE |
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