Using many pilot points and singular value decomposition in groundwater model calibration

Steen Christensen, John Doherty

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

A significant practical problem with the pilot point method is to choose the location of the pilot points. We present a method that is intended to relieve the modeller from much of this responsibility. The basic idea is that a very large number of pilot points are distributed more or less uniformly over the model area. Singular value decomposition (SVD) of the normal matrix is used to reduce the large number of pilot point parameters to a smaller number of so-called super parameters that can be estimated by nonlinear regression from the available observations. A number of Eigenvectors corresponding to significant Eigen values (resulting from the decomposition) is used to transform the model from having many pilot point parameters to having a few super parameters. A synthetic case model is used to analyse and demonstrate the application of the presented method of model parameterization and calibration.

Original languageEnglish
Title of host publicationProceedings of an International Conference on Calibration and Reliability in Groundwater Modelling
Subtitle of host publicationCredibility of Modelling, ModelCARE2007
Pages235-239
Number of pages5
Edition320
Publication statusPublished - 2008
Externally publishedYes
EventInternational Conference on Calibration and Reliability in Groundwater Modelling: Credibility of Modelling, ModelCARE2007 - Copenhagen, Denmark
Duration: 9 Sep 200713 Sep 2007

Publication series

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

Conference

ConferenceInternational Conference on Calibration and Reliability in Groundwater Modelling: Credibility of Modelling, ModelCARE2007
Country/TerritoryDenmark
CityCopenhagen
Period9/09/0713/09/07

Keywords

  • Parameter estimation
  • Pilot point
  • Singular value decomposition

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