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arXiv · 1804.06285

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

Zhe Sha·Jonathan Rougier·Maike Schumacher·Jonathan Bamber
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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.

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Zhe Sha, Jonathan Rougier, Maike Schumacher, Jonathan Bamber. 2018-04-17. Bayesian model-data synthesis with an application to global Glacio-Isostatic Adjustment. https://arxiv.org/abs/1804.06285

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