A model-independent iterative ensemble smoother for efficient history-matching and uncertainty quantification in very high dimensions. (November 2018)

• Record Type: Journal Article
• Authors: White, Jeremy T.
• Is Part Of: Environmental modelling & software. Volume 109(2018)
• Page Start: 191
• Page End: 201
• Publication Date: 2018-11
• Subjects: Modeling -- Uncertainty -- Iterative ensemble smoother -- Gauss-Levenberg-Marquardt; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.06.009
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: An open-source, scalable and model-independent (non-intrusive) implementation of an iterative ensemble smoother has been developed to alleviate the computational burden associated with history-matching and uncertainty quantification of real-world-scale environmental models that have very high dimensional parameter spaces. The tool, named pestpp-ies, implements the ensemble-smoother form of the popular Gauss-Levenberg-Marquardt algorithm, uses the pest model-interface protocols and includes a built-in parallel run manager, multiple lambda testing and model run failure tolerance. As a demonstration of its capabilities, pestpp-ies is applied to a synthetic groundwater model with thousands of parameters and to a real-world groundwater flow and transport model with tens of thousands of parameters. pestpp-ies is shown to efficiently and effectively condition parameters in both cases and can provide means to estimate posterior forecast uncertainty when the forecasts depend on large numbers of parameters. Highlights: pestpp-ies implements a GLM form of the iterative ensemble smoother (iES). iES algorithms relax the parameter-based computational bound for uncertainty analysis. pestpp-ies implements parallel run management to reduce computational burden. pestpp-ies is non-intrusive and can be applied to many environmental modeling analyses.