Non‐monotone spectral projected gradient method applied to full waveform inversion

Abstract

ABSTRACTThe seismic inversion problem is a highly non‐linear problem that can be reduced to the minimization of the least‐squares criterion between the observed and the modelled data. It has been solved using different classical optimization strategies that require a monotone descent of the objective function. We propose solving the full‐waveform inversion problem using the non‐monotone spectral projected gradient method: a low‐cost and low‐storage optimization technique that maintains the velocity values in a feasible convex region by frequently projecting them on this convex set. The new methodology uses the gradient direction with a particular spectral step length that allows the objective function to increase at some iterations, guarantees convergence to a stationary point starting from any initial iterate, and greatly speeds up the convergence of gradient methods. We combine the new optimization scheme as a solver of the full‐waveform inversion with a multiscale approach and apply it to a modified version of the Marmousi data set. The results of this application show that the proposed method performs better than the classical gradient method by reducing the number of function evaluations and the residual values.