An introduction to variational inference in Geophysical inverse problems

TL;DR

This paper introduces variational inference as an efficient, scalable approach for solving Bayesian inverse problems in geophysics, enabling uncertainty quantification in complex physical systems.

Contribution

It provides an introductory overview of variational inference and reviews its application to various geophysical inverse problems, demonstrating its practicality.

Findings

Variational inference is effective for geophysical inverse problems.

It offers scalable solutions for complex Bayesian inference.

The method enables uncertainty quantification in physical system characterization.

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.