TY - GEN
T1 - Maximum likelihood Bayesian averaging of air flow models in unsaturated fractured tuff
AU - Morales-Casique, Eric
AU - Neuman, Shlomo P.
AU - Vesselinov, Velimir V.
PY - 2008
Y1 - 2008
N2 - MLBMA is a maximum likelihood (ML) version of Bayesian model averaging (BMA) that renders it compatible with ML methods of model calibration and thus applicable to cases where prior information about the parameter may be unavailable. We explore the role of prior information in MLBMA by applying it to air flow during a cross-hole pneumatic injection test in unsaturated fractured tuff with and without reliance on packer-test data from six boreholes. We parameterize log air permeability and porosity geostatistically using pilot points and estimate them by calibrating a finite volume pressure simulator (FEHM) against cross-hole pressure data by means of a parallelized version of PEST considering several alternative variogram models. We assess the predictive capabilities of each model based on various model selection criteria and discuss future plans to generate corresponding predictions via MLBMA, cross-validate them against pressure data from the same cross-hole test, and validate them against data from another such test.
AB - MLBMA is a maximum likelihood (ML) version of Bayesian model averaging (BMA) that renders it compatible with ML methods of model calibration and thus applicable to cases where prior information about the parameter may be unavailable. We explore the role of prior information in MLBMA by applying it to air flow during a cross-hole pneumatic injection test in unsaturated fractured tuff with and without reliance on packer-test data from six boreholes. We parameterize log air permeability and porosity geostatistically using pilot points and estimate them by calibrating a finite volume pressure simulator (FEHM) against cross-hole pressure data by means of a parallelized version of PEST considering several alternative variogram models. We assess the predictive capabilities of each model based on various model selection criteria and discuss future plans to generate corresponding predictions via MLBMA, cross-validate them against pressure data from the same cross-hole test, and validate them against data from another such test.
KW - Air flow
KW - Bayesian model averaging
KW - Inverse modelling
KW - Maximum likelihood
UR - http://www.scopus.com/inward/record.url?scp=55249095298&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=55249095298&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:55249095298
SN - 9781901502497
T3 - IAHS-AISH Publication
SP - 70
EP - 75
BT - Proceedings of an International Conference on Calibration and Reliability in Groundwater Modelling
T2 - International Conference on Calibration and Reliability in Groundwater Modelling: Credibility of Modelling, ModelCARE2007
Y2 - 9 September 2007 through 13 September 2007
ER -