Incorporating posterior model discrepancy into a hierarchical framework to facilitate out-of-the-box MCMC sampling for geothermal inverse problems and uncertainty quantification

We consider geothermal inverse problems and uncertainty quantification from a Bayesian perspective. Our goal is to make standard, 'out-of-the-box' Markov chain Monte Carlo (MCMC) sampling more feasible for complex simulation models. To do this, we first show how to pose the inverse and prediction problems in a hierarchical Bayesian framework. We then show how to incorporate so-called posterior mod…