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

Zhe Sha, Jonathan Rougier*, Maike Schumacher, Jonathan Bamber

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

5 Citations (Scopus)
184 Downloads (Pure)


Glacial isostatic adjustment (GIA) is a crucial component in evaluating sea level change. The GIA process has been simulated globally from various physical forward models and it can also be measured locally at some GPS stations. In this paper we combine the physical model simulations and GPS measurements in a Bayesian hierarchical modelling framework to update global GIA. In common with many large-scale spatial modelling applications, there are two major challenges. One is the scale of the update, which is too large for naive Gaussian conditioning. The other is the need to represent non-stationarity in the prior. We address the first challenge with the now well-established stochastic partial differential equations (SPDE) and integrated nested Laplace approximation (INLA) approach. For non-stationary global process, we propose two general models that accommodate commonly-seen geospatial patterns. We present and compare the GIA result for the two models, alongside the default option of assuming stationarity.
Original languageEnglish
Article numbere2530
Number of pages13
Issue number1
Early online date22 Oct 2018
Publication statusPublished - Feb 2019

Structured keywords

  • GlobalMass


  • Bayesian hierarchical model
  • geophysical processes
  • nonstationarity
  • spatial statistics
  • stochastic partial differential equations


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