Two-grid stochastic full waveform inversion of 2D marine seismic data

We apply stochastic Full Waveform Inversion (FWI) to 2D marine seismic data to estimate the macromodel velocity field which can be a suitable input for subsequent local (gradient based) FWI. Genetic Algorithms are used as the global optimization method. Our two-grid representation of the subsurface, made of a coarse grid for the inversion and of a fine grid for the modeling, allows us to reduce the number of unknowns to an acceptable number for the given computer resources and to perform a stable and reliable finite difference modeling. Thus, notwithstanding the known high computational costs that characterize global inversion methods, we are able to reconstruct a smooth, low wavenumber, acoustic velocity model of the subsurface. The reliability of the estimated velocity macro-model is checked through the inspection of prestack depth migrated gathers and through the superposition of observed and modeled seismograms. The method we propose is less affected by the risk of being trapped in local minima of the misfit functional than gradient based FWI methods, and can be a viable alternative to estimate proper starting models for gradient based full waveform inversions.