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 publicationIAHS-AISH Publication
Pages70-75
Number of pages6
Edition320
StatePublished - 2008
EventInternational Conference on Calibration and Reliability in Groundwater Modelling: Credibility of Modelling, ModelCARE2007 - Copenhagen, Denmark
Duration: Sep 9 2007Sep 13 2007

Other

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

Fingerprint

tuff
airflow
air permeability
variogram
simulator
borehole
porosity
calibration
test
prediction

Keywords

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

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

Cite this

Morales-Casique, E., Neuman, S. P., & Vesselinov, V. V. (2008). Maximum likelihood Bayesian averaging of air flow models in unsaturated fractured tuff. In IAHS-AISH Publication (320 ed., pp. 70-75)

Maximum likelihood Bayesian averaging of air flow models in unsaturated fractured tuff. / Morales-Casique, Eric; Neuman, Shlomo P; Vesselinov, Velimir V.

IAHS-AISH Publication. 320. ed. 2008. p. 70-75.

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

Morales-Casique, E, Neuman, SP & Vesselinov, VV 2008, Maximum likelihood Bayesian averaging of air flow models in unsaturated fractured tuff. in IAHS-AISH Publication. 320 edn, pp. 70-75, International Conference on Calibration and Reliability in Groundwater Modelling: Credibility of Modelling, ModelCARE2007, Copenhagen, Denmark, 9/9/07.
Morales-Casique E, Neuman SP, Vesselinov VV. Maximum likelihood Bayesian averaging of air flow models in unsaturated fractured tuff. In IAHS-AISH Publication. 320 ed. 2008. p. 70-75
Morales-Casique, Eric ; Neuman, Shlomo P ; Vesselinov, Velimir V. / Maximum likelihood Bayesian averaging of air flow models in unsaturated fractured tuff. IAHS-AISH Publication. 320. ed. 2008. pp. 70-75
@inproceedings{da8b3b625aa048368bee1c2c2198199e,
title = "Maximum likelihood Bayesian averaging of air flow models in unsaturated fractured tuff",
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.",
keywords = "Air flow, Bayesian model averaging, Inverse modelling, Maximum likelihood",
author = "Eric Morales-Casique and Neuman, {Shlomo P} and Vesselinov, {Velimir V.}",
year = "2008",
language = "English (US)",
isbn = "9781901502497",
pages = "70--75",
booktitle = "IAHS-AISH Publication",
edition = "320",

}

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

SP - 70

EP - 75

BT - IAHS-AISH Publication

ER -