A Hybrid Optimization Framework for Seismic Full Waveform Inversion

Abstract A hybrid optimization framework is proposed for full waveform inversion (FWI) problems by incorporating derivative information into the model update rule of a global optimization method called Very Fast Simulated Annealing (VFSA). The proposed optimization framework tackles the local minima issue of non‐linear inverse problems. Additionally, it can converge to the close neighborhood of the global solution from different starting points with an improved convergence speed compared to traditional global optimization methods. Applied to large‐scale FWI problems, the proposed framework greatly relaxes the issue of the dependence of FWI on starting models. Given a proper tuning and sufficient number of iterations, hybrid optimization based FWI can render a good background model, which is a good approximation to the ground truth, even with uninformative prior constraints and poor starting models. The output of hybrid optimization based FWI can be used as the starting model for a subsequent local optimization based FWI run to improve the spatial resolution of the result. The proposed hybrid optimization framework is very general, and can be applied to other linear and non‐linear problems that need optimization loops.