Waveform inversion with a data driven estimate of the internal wave

TL;DR

This paper introduces a data-driven approach to improve wave speed estimation in seismic imaging by mitigating nonlinearity and cycle-skipping issues in waveform inversion.

Contribution

The authors propose using a data-driven estimate of the internal wave field to reduce nonlinearity and enhance inversion accuracy with a good initial kinematic guess.

Findings

Improved inversion performance with data-driven internal wave estimates.

Reduction of local minima in the objective function.

Enhanced robustness to initial guess errors.

Abstract

We study an inverse problem for the wave equation, concerned with estimating the wave speed, aka velocity, from data gathered by an array of sources and receivers that emit probing signals and measure the resulting waves. The typical mathematical formulation of velocity estimation is a nonlinear least squares minimization of the data misfit, over a search velocity space. There are two main impediments to this approach, which manifest as multiple local minima of the objective function: The nonlinearity of the mapping from the velocity to the data, which accounts for multiple scattering effects, and poor knowledge of the kinematics (smooth part of the wave speed) which causes cycle-skipping. We show that the nonlinearity can be mitigated using a data driven estimate of the internal wave field. This leads to improved performance of the inversion for a reasonable initial guess of the kinematics.