l1-Regularized full-waveform inversion with prior model information based on orthant-wise limited memory quasi-Newton method

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. (C) 2017 Elsevier B.V. All rights reserved.