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Approaches in Highly Parameterized Inversion: bgaPEST, a Bayesian Geostatistical Approach Implementation With PEST?Documentation and Instructions
Randall J. Hunt
(Author)
·
John E. Doherty
(Author)
·
Marco D'Oria
(Author)
·
Createspace Independent Publishing Platform
· Paperback
Approaches in Highly Parameterized Inversion: bgaPEST, a Bayesian Geostatistical Approach Implementation With PEST?Documentation and Instructions - D'Oria, Marco ; Doherty, John E. ; Hunt, Randall J.
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Synopsis "Approaches in Highly Parameterized Inversion: bgaPEST, a Bayesian Geostatistical Approach Implementation With PEST?Documentation and Instructions"
The application bgaPEST is a highly parameterized inversion software package implementing the Bayesian Geostatistical Approach in a framework compatible with the parameter estimation suite PEST. Highly parameterized inversion refers to cases in which parameters are distributed in space or time and are correlated with one another. The Bayesian aspect of bgaPEST is related to Bayesian probability theory in which prior information about parameters is formally revised on the basis of the calibration dataset used for the inversion. Conceptually, this approach formalizes the conditionality of estimated parameters on the specific data and model available.
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