Inference of geostatistical hyperparameters with the correlated pseudo-marginal method

We consider non-linear Bayesian inversion problems targeting the geostatistical hyperparameters of a random field describing hydrogeological or geophysical properties given hydrogeological or geophysical data. This problem is of particular importance in the non-ergodic setting as there are no analytical upscaling relationships linking the data to the hyperparameters, such as, mean, standard deviation, and integral scales. Full inversion of the hyperparameters and the local properties of the field (typically involving many thousands of unknowns) brings substantial computational challenges, such that simplifying model assumptions (e.g., homogeneity or ergodicity) are typically made. To prevent the errors resulting from such simplified assumptions while also circumventing the burden of high-dimensional full inversions, we use a pseudo-marginal Metropolis–Hastings algorithm that treats the random field as latent variables. In this random effects model, the intractable likelihood of observing the data given the hyperparameters is estimated by Monte Carlo averaging over realizations of the random field. To increase the efficiency of the method, low-variance approximations of the likelihood ratio are obtained by using importance sampling and by correlating the samples used in the proposed and current steps of the Markov chain. We assess the performance of this correlated pseudo-marginal method by considering two representative inversion problems involving diffusion-based and wave-based physics, respectively, in which we infer the hyperparameters of (1) hydraulic conductivity fields using apparent hydraulic conductivity data in a data-poor setting and (2) fracture aperture fields using borehole ground-penetrating radar (GPR) reflection data in a more data-rich setting. For the first test case, we find that the correlated pseudo-marginal method generates similar estimates of the geostatistical mean as classical rejection sampling, while an inversion assuming ergodicity provides biased estimates. For the second test case, we find that the correlated pseudo-marginal method estimates the hyperparameters well, while rejection sampling is computationally unfeasible and a simplified model assuming homogeneity leads to biased estimates. [Display omitted] •Hyperparameters enable general descriptions of target property fields.•No analytical relation linking hyperparameters and data in the non-ergodic setting.•Correlated pseudo-marginal method accounts for the influence of s