Bayesian model-data synthesis with an application to global Glacio-Isostatic Adjustment

TL;DR

This paper presents a Bayesian hierarchical framework for updating large-scale geospatial models, specifically applied to global Glacio-Isostatic Adjustment, addressing computational challenges and non-stationarity.

Contribution

It introduces a novel model-data synthesis approach using SPDE and INLA for efficient Bayesian inference on large-scale non-stationary geospatial processes.

Findings

Effective updating of global GIA estimates using GPS data

Reduced computational cost with SPDE and GMRF approximation

Flexible models for non-stationary geospatial processes

Abstract

We introduce a framework for updating large scale geospatial processes using a model-data synthesis method based on Bayesian hierarchical modelling. Two major challenges come from updating large-scale Gaussian process and modelling non-stationarity. To address the first, we adopt the SPDE approach that uses a sparse Gaussian Markov random fields (GMRF) approximation to reduce the computational cost and implement the Bayesian inference by using the INLA method. For non-stationary global processes, we propose two general models that accommodate commonly-seen geospatial problems. Finally, we show an example of updating an estimate of global glacial isostatic adjustment (GIA) using GPS measurements.