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Approaches in highly parameterized inversion: bgaPEST, a Bayesian geostatistical approach implementation with PEST: documentation and instructions

Techniques and Methods 7-C9

Groundwater Resources Program and Global Change Research and Development. This report is Chapter 9 of Section C, Computer programs, in Book 7, Automated Data Processing and Computations.
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Abstract

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. The geostatistical component of the method refers to the way in which prior information about the parameters is used. A geostatistical autocorrelation function is used to enforce structure on the parameters to avoid overfitting and unrealistic results. Bayesian Geostatistical Approach is designed to provide the smoothest solution that is consistent with the data. Optionally, users can specify a level of fit or estimate a balance between fit and model complexity informed by the data. Groundwater and surface-water applications are used as examples in this text, but the possible uses of bgaPEST extend to any distributed parameter applications.

Additional Publication Details

Publication type:
Report
Publication Subtype:
USGS Numbered Series
Title:
Approaches in highly parameterized inversion: bgaPEST, a Bayesian geostatistical approach implementation with PEST: documentation and instructions
Series title:
Techniques and Methods
Series number:
7-C9
Year Published:
2013
Language:
English
Publisher:
U.S. Geological Survey
Publisher location:
Reston, VA
Contributing office(s):
Wisconsin Water Science Center
Description:
Report: vi, 86 p.; Software; Development GIT Repository
Larger Work Title:
Automated Data Processing and Computations (Book 7)
Number of Pages:
96
Online Only (Y/N):
Y
Additional Online Files(Y/N):
Y