Bayesian full-waveform inversion with realistic priors
Xin Zhang, Andrew Curtis
Abstract
ABSTRACT 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; therefore, Bayesian methods have been used to quantify uncertainties in the solution. Variational Bayesian inference uses optimization to efficiently provide solutions. 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 have found 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.