Summary statistics from training images as prior information in probabilistic inversion

TL;DR

This paper introduces a Bayesian inversion method that incorporates prior geological information from training images to improve the reliability and accuracy of geophysical data inversion, demonstrated through synthetic case studies.

Contribution

It presents a novel approach combining multiple-point statistics and Bayesian inversion to effectively integrate prior geological knowledge into geophysical data analysis.

Findings

Prior information reduces inversion artifacts

Method improves parameter estimate accuracy

Enhances reliability of posterior models

Abstract

A strategy is presented to incorporate prior information from conceptual geological models in probabilistic inversion of geophysical data. The conceptual geological models are represented by multiple-point statistics training images (TIs) featuring the expected lithological units and structural patterns. Information from an ensemble of TI realizations is used in two different ways. First, dominant modes are identified by analysis of the frequency content in the realizations, which drastically reduces the model parameter space in the frequency-amplitude domain. Second, the distributions of global, summary metrics (e.g. model roughness) are used to formulate a prior probability density function. The inverse problem is formulated in a Bayesian framework and the posterior pdf is sampled using Markov chain Monte Carlo simulation. The usefulness and applicability of this method is demonstrated on two case studies in which synthetic crosshole ground-penetrating radar traveltime data are inverted to recover 2-D porosity fields. The use of prior information from TIs significantly enhances the reliability of the posterior models by removing inversion artefacts and improving individual parameter estimates. The proposed methodology reduces the ambiguity inherent in the inversion of high-dimensional parameter spaces, accommodates a wide range of summary statistics and geophysical forward problems.