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Abstract As the state‐of‐the‐art method, full waveform inversion (FWI) is being broadly used to investigate the Earth's interior. However, the classical local‐optimization‐based FWI, relying on a good initial model, often encounters local minima issues. Benefiting from advancements in computational power, recent studies have integrated FWI with global optimization methods to mitigate the local minima issue by expanding search of the parameter space. Here, we introduce a hybrid optimization framework for FWI (HFWI) that can escape local minima by incorporating gradient and stochastic perturbations, with the modified stopping criterion to reduce the impact of hyper‐parameters. Synthetic tests are conducted to validate the performance of HFWI on the estimation of 3‐D model parameters. Although the poorly‐constrained initial models increase the likelihood of being trapped in local minima, the HFWI, because of the randomness in the searching direction, generally approaches the globally optimized model, with the residual data misfit less than a half period. The inverted model can be used as a good starting point for a local optimization based FWI to further refine its accuracies. Application of the elastic HFWI method to the earthquake recordings in Oklahoma, we validate the existence of sedimentary layers in the retrieved elastic model, and are able to constrain the spatial pattern of major geological units, such as the Anadarko Basin and the Cherokee Platform, with the illumination‐dependent uncertainties. Our results illustrate the ability of HFWI to estimate a 3‐D crustal model with a poor initial model, and the potential of HFWI on offering more detailed structural information.
Zhang et al. (Fri,) studied this question.