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Fast Update of Conditional Simulation Ensembles

Clément Chevalier, Xavier Emery & David Ginsbourger

Abstract Gaussian random field (GRF) conditional simulation is a key ingredient in many spatial statistics problems for computing Monte-Carlo estimators and quantifying uncertainties on non-linear functionals of GRFs conditional on data. Conditional simulations are known to often be computer intensive, especially when appealing to matrix decomposition approaches with a large number of simulation points. This work studies settings where conditioning observations are assimilated batch sequentially, with one point or a batch of points at each stage. Assuming that conditional simulations have been performed at a previous stage, the goal is to take advantage of already available sample paths and by-products to produce updated conditional simulations at minimal cost. Explicit formulae are provided, which allow updating an ensemble of sample paths conditioned on n≥0 observations to an ensemble conditioned on n+q observations, for arbitrary q≥1. Compared to direct approaches, the proposed formulae prove to substantially reduce computational complexity. Moreover, these formulae explicitly exhibit how the q new observations are updating the old sample paths. Detailed complexity calculations highlighting the benefits of this approach with respect to state-of-the-art algorithms are provided and are complemented by numerical experiments.
   
Keywords Gaussian random fields Residual kriging algorithm Batch-sequential strategies Kriging update equations
   
Citation Chevalier, C., Emery, X., & Ginsbourger, D. (2015). Fast Update of Conditional Simulation Ensembles. Mathematical Geosciences, 47(7), 771-789.
   
Type Journal article (English)
Date of appearance 2015
Journal Mathematical Geosciences
Volume 47
Issue 7
Pages 771-789
URL http://link.springer.com/article/10.1007/s11004-014-9573-7