Multipliers waveform inversion

Abstract

The full waveform inversion (FWI) addresses the computation and characterization of subsurface model parameters by matching predicted data to observed seismograms in the frame of nonlinear optimization. We formulate FWI as a nonlinearly constrained optimization problem, for which a regularization term is minimized subject to the nonlinear data matching constraint. Unlike FWI which is based on the penalty function, the method of multipliers solves the resulting optimization problems by using the augmented Lagrangian function; and leads to a two step recursive algorithm. The primal step requires solving an unconstrained minimization problem like the traditional FWI with a difference that the data are replaced by the Lagrange multipliers. The dual step involves an update of the Lagrange multipliers. The overall performance of the algorithm is improved considering that this multiplier method does not require exact solve of these primal-dual subproblems. In fact, convergence is attained when only one step of a gradient-based method is taken on both subproblems. The proposed algorithm greatly improves the overall performance of FWI such as convergence from inaccurate starting models and robustness with respect to determination of the step length. Furthermore, it can be performed by the existing FWI engines with minimal change. We only have to replace the observed data at each iteration with the multipliers, thus all the nice properties of the traditional FWI algorithms are kept. Numerical experiments confirm that the multipliers waveform inversion can converge to a solution of the inverse problem in the absence of low-frequency data from an inaccurate initial model even with a constant step size.The full waveform inversion (FWI) has been widely accepted as an accurate method for the computation and characterization of subsurface model parameters by matching predicted data to observed seismograms (