An introduction to variational inference in geophysical inverse problems

In a variety of scientific applications we wish to characterize physical system using measurements or observations. This often requires us solve an inverse problem, which usually has non-unique solutions so uncertainty must be quantified in order define the family all possible solutions. Bayesian inference provides powerful theoretical framework defines set problems, and variational is method problems optimization while still producing fully probabilistic chapter introduction inference, reviews its range geophysical including petrophysical inversion, travel time tomography full-waveform inversion. We demonstrate that efficient scalable can deployed many practical scenarios.