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.

中文翻译：

---

地震全波形反演的混合优化框架

通过将导数信息纳入称为超快速模拟退火 (VFSA) 的全局优化方法的模型更新规则，提出了一种针对全波形反演 (FWI) 问题的混合优化框架。所提出的优化框架解决了非线性逆问题的局部最小值问题。此外，与传统的全局优化方法相比，它可以从不同的起点收敛到全局解的近邻域，收敛速度更快。应用于大规模 FWI 问题，该框架极大地缓解了 FWI 对起始模型的依赖问题。给定适当的调优和足够的迭代次数，基于混合优化的 FWI 可以呈现良好的背景模型，这是对基本事实的良好近似，即使有无信息的先验约束和糟糕的起始模型。基于混合优化的 FWI 的输出可用作后续基于局部优化的 FWI 运行的起始模型，以提高结果的空间分辨率。所提出的混合优化框架非常通用，可以应用于其他需要优化循环的线性和非线性问题。