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

Fienen, Michael N.; D'Oria, Marco; Doherty, John E.; Hunt, Randall J.

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.

Genre:

USGS Numbered Series

ProdID:

70042380

Citation Author:

Fienen, Michael N.; D'Oria, Marco; Doherty, John E.; Hunt, Randall J.

Citation Contributing Office:

Wisconsin Water Science Center

Citation Datum:

Citation Day:

Citation Edition:

Citation Editor:

Citation End Page:

Citation Issue:

Citation Keywords:

Citation Language:

English

Citation Larger Work Title:

Automated Data Processing and Computations (Book 7)

Citation LatN:

Citation LatS:

Citation LonE:

Citation LonW:

Citation Month:

Citation No Pagination:

Citation Number Of Pages:

Citation Online Only Flag:

Citation Phsyical Description:

Report: vi, 86 p.; Software; Development GIT Repository

Citation Projection:

Citation Public Comments:

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.

Citation Publisher:

U.S. Geological Survey

Citation Series:

Techniques and Methods

Citation Series Code:

Citation Series Number:

7-C9

Citation Search Results Text:

Approaches in highly parameterized inversion: bgaPEST, a Bayesian geostatistical approach implementation with PEST: documentation and instructions; 2013; TM; 7-C9; USGS Techniques and Methods; Automated Data Processing and Computations (Book 7); Fienen, Michael N.; D‘Oria, Marco; Doherty, John E.; Hunt, Randall J.