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

2018/04/17 by Sha, Zhe, Rougier, Jonathan, Schumacher, Maike +1
#Applications (stat.AP) #FOS: Computer and information sciences #Other Statistics (stat.OT)

paper · doi:10.48550/arxiv.1804.06285

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.

Related

• A Review of Bayesian Modelling in Glaciology
• A Hierarchical Spatio-Temporal Statistical Model Motivated by Glaciology
• Computation and Visualisation for large-scale Gaussian updates
• Beyond Intuition, a Framework for Applying GPs to Real-World Data
• Nonstationary Bayesian modeling for a large data set of derived surface\n temperature return values