Gradient based MCMC and Hybrid optimization framework for full waveform inversion

Seismic full-waveform inversion (FWI) is a powerful tool for reconstructing high-resolution subsurface models. A common practice is to implement FWI with local optimization methods. In order to achieve a geologically meaningful result, an accurate background model is often required by FWI based on local optimization methods. Building such accurate background model, however, can involve long and intensive pre-processing, and sometimes can't even be achievable due to the limitations of data and methodologies. Additionally, quantifying the uncertainty associated with the ill-posed inverse problem is still changeling. Here we present a gradient based MCMC sampling approach for FWI to estimate the posterior distribution for model parameters, the method is applied to acoustic FWI problems. We also combine advantages of local optimization and global optimization methods and propose a new class of optimization framework named hybrid optimization framework. The new framework can efficiently reduce the objective function and simultaneously explore possible model parameters. The methods enables FWI to start with uninformative models and prior bounds, which may allow to dispense with the pre-processing stage of building an accurate background starting model, making FWI much more automatic. We will introduce the methods and show several applications of the methods to active source seismic data acquired in various subduction zones and mid-ocean-ridges, we will also demonstrate the application to earthquake FWI problems.