Waveform Inversion with a Data Driven Estimate of the Internal Wave

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