Bayesian Full-waveform Inversion with Realistic Priors

Seismic full-waveform inversion (FWI) uses full seismic records to estimate the subsurface velocity structure. This requires a highly nonlinear and nonunique inverse problem to be solved, so Bayesian methods have been used to quantify uncertainties in the solution. Variational Bayesian inference uses optimization to provide solutions efficiently. However, previously the method has only been applied to a transmission FWI problem, and with strong prior information imposed on the velocity such as is never available in practice. We show that the method works well in a seismic reflection setting, and with realistically weak prior information, representing the type of problem that occurs in reality. We conclude that the method can produce high-resolution images and reliable uncertainties using data from standard reflection seismic acquisition geometry, realistic nonlinearity, and practically achievable prior information.