Accelerating Monte Carlo Markov chains with proxy and error models

Laureline Josset, Vasily Demyanov, Ahmed H Elsheikh, Ivan Lunati

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)
374 Downloads (Pure)


In groundwater modeling, Monte Carlo Markov Chain (MCMC) simulations are often used to calibrate aquifer parameters and propagate the uncertainty to the quantity of interest (e.g., pollutant concentration). However, this approach requires a large number of flow simulations and incurs high computational cost, which prevents a systematic evaluation of the uncertainty in presence of complex physical processes. To avoid this computational bottleneck, we propose to use an approximate model (proxy) to predict the response of the exact model. Here, we use a proxy that entails a very simplified description of the physics with respect to the detailed physics described by the “exact” model. The error model accounts for the simplification of the physical process; and it is trained on a learning set of realizations, for which both the proxy and exact responses are computed. First, the key features of the set of curves are extracted using functional principal component analysis; then, a regression model is built to characterize the relationship between the curves. The performance of the proposed approach is evaluated on the Imperial College Fault model. We show that the joint use of the proxy and the error model to infer the model parameters in a two-stage MCMC set-up allows longer chains at a comparable computational cost. Unnecessary evaluations of the exact responses are avoided through a preliminary evaluation of the proposal made on the basis of the corrected proxy response. The error model trained on the learning set is crucial to provide a sufficiently accurate prediction of the exact response and guide the chains to the low misfit regions. The proposed methodology can be extended to multiple-chain algorithms or other Bayesian inference methods. Moreover, FPCA is not limited to the specific presented application and offers a general framework to build error models.
Original languageEnglish
Pages (from-to)38-48
Number of pages11
JournalComputers and Geosciences
Issue numberPart B
Early online date10 Jul 2015
Publication statusPublished - Dec 2015


  • Functional data analysis
  • Bayesian inference
  • Two-stage MCMC
  • Imperial College Fault test case

ASJC Scopus subject areas

  • Information Systems
  • Computers in Earth Sciences


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