An introduction to variational inference in Geophysical inverse problems
Abstract
In a variety of scientific applications we wish to characterize a physical system using measurements or observations. This often requires us to solve an inverse problem, which usually has non-unique solutions so uncertainty must be quantified in order to define the family of all possible solutions. Bayesian inference provides a powerful theoretical framework which defines the set of solutions to inverse problems, and variational inference is a method to solve Bayesian inference problems using optimization while still producing fully probabilistic solutions. This chapter provides an introduction to variational inference, and reviews its applications to a range of geophysical problems, including petrophysical inversion, travel time tomography and full-waveform inversion. We demonstrate that variational inference is an efficient and scalable method which can be deployed in many practical scenarios.
Cite
@article{arxiv.2205.08861,
title = {An introduction to variational inference in Geophysical inverse problems},
author = {Xin Zhang and Muhammad Atif Nawaz and Xuebin Zhao and Andrew Curtis},
journal= {arXiv preprint arXiv:2205.08861},
year = {2022}
}