Software framework for inverse modeling and uncertainty characterization. (April 2015)

• Record Type: Journal Article
• Authors: Osorio-Murillo, Carlos A.; Over, Matthew W.; Savoy, Heather; Ames, Daniel P.; Rubin, Yoram
• Is Part Of: Environmental modelling & software. Volume 66(2015:Apr.)
• Page Start: 98
• Page End: 109
• Publication Date: 2015-04
• Subjects: Modeling frameworks -- Method of Anchored Distributions -- Inverse modeling -- Spatial random fields -- Model integration; FM Forward Model -- FMD Forward Model Driver -- GIS Geographic Information System -- MAD Method of Anchored Distributions -- MAD# Method of Anchored Distributions C# Program -- pdf Probability Density Function -- RFG Random Field Generator -- RFGD Random Field Generator Driver -- SRF Spatial Random Field; 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.2015.01.002
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Estimation of spatial random fields (SRFs) is required for predicting groundwater flow, subsurface contaminant movement, and other areas of environmental and earth sciences modeling. This paper presents an inverse modeling framework called MAD# for characterizing SRFs, which is an implementation of the Bayesian inverse modeling technique Method of Anchored Distributions (MAD). MAD# allows modelers to "wrap" simulation models using an extensible driver architecture that exposes model parameters to the inversion engine. MAD# is implemented in an open source software package with the goal of lowering the barrier to using inverse modeling in education, research, and resource management. MAD# includes an intentionally simple user interface for simulation configuration, external software integration, spatial domain and model output visualization, and evaluation of model convergence. Four test cases are presented demonstrating the novel functionality of this framework to apply inversion in order to calibrate the model parameters characterizing a groundwater aquifer. Highlights: An inverse modeling framework for characterizing spatial random fields. A novel plugin method for integrating 3rd party model and tools. Four case studies demonstrate the system for groundwater assessment.