Maximum likelihood Bayesian averaging of air flow models in unsaturated fractured tuff

Eric Morales-Casique, Shlomo P. Neuman, Velimir V. Vesselinov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of an International Conference on Calibration and Reliability in Groundwater Modelling
Subtitle of host publicationCredibility of Modelling, ModelCARE2007
Pages70-75
Number of pages6
Edition320
StatePublished - Nov 7 2008
EventInternational Conference on Calibration and Reliability in Groundwater Modelling: Credibility of Modelling, ModelCARE2007 - Copenhagen, Denmark
Duration: Sep 9 2007Sep 13 2007

Publication series

NameIAHS-AISH Publication
Number320
ISSN (Print)0144-7815

Other

OtherInternational Conference on Calibration and Reliability in Groundwater Modelling: Credibility of Modelling, ModelCARE2007
CountryDenmark
CityCopenhagen
Period9/9/079/13/07

Keywords

  • Air flow
  • Bayesian model averaging
  • Inverse modelling
  • Maximum likelihood

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

Fingerprint Dive into the research topics of 'Maximum likelihood Bayesian averaging of air flow models in unsaturated fractured tuff'. Together they form a unique fingerprint.

Cite this