Velocity estimation via registration-guided least-squares inversion

Least-squares acoustic waveform inversion often suffers from a very narrow basin of attraction near the global minimum. In order to mitigate this problem, this paper introduces an iterative inversion scheme where the notion of proximity of two traces is not the usual least-squares distance, but instead involves registration as in image processing. Observed data are matched to predicted waveforms via piecewise-polynomial warpings, obtained by solving a nonconvex optimization problem in a multiscale fashion from low to high frequencies. This multiscale process requires defining low-frequency augmented signals in order to seed the frequency sweep at zero frequency. Custom adjoint sources are then defined from the warped waveforms. The new method, referred to as Registration-guided least-squares, is successfully applied to a few scenarios of model velocity estimation in the transmission setting. We show that the new method can converge to the correct model in situations where conventional least-squares inversion suffers from cycle-skipping and converges to a