Stochastic conditional inverse modeling of subsurface mass transport: A brief review and the self-calibrating method

Conditioning transmissivity realizations to state variable data is complex due to the non-linear dependence of transmissivity (or any univariate transform of it) and piezometric heads, concentrations or velocities. A review of the literature shows these complexities. The self-calibrating algorithm c...

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Bibliographic Details
Published inStochastic environmental research and risk assessment Vol. 17; no. 5; pp. 319 - 328
Main Authors Gomez-Hernandez, J J, Franssen, H -J W M Hendricks, Sahuquillo, A
Format Journal Article
LanguageEnglish
Published Heidelberg Springer Nature B.V 01.11.2003
Subjects
Algorithms
Mass transport
Piezometric head
Q1
Research methodology
Reviews
Risk assessment
Statistics
Stochastic models
Transmissivity
Velocity
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Summary:Conditioning transmissivity realizations to state variable data is complex due to the non-linear dependence of transmissivity (or any univariate transform of it) and piezometric heads, concentrations or velocities. A review of the literature shows these complexities. The self-calibrating algorithm combines standard geostatistics and non-linear optimization in a way that allows the generation of multiple realizations of logtransmissivity, which are conditioned not only to logtransmissivity measurements but also to piezometric head and concentration data. The self-calibrating method is demonstrated in a two-dimensional synthetic exercise in which the trade-offs between transmissivity, piezometric head and concentration data are analyzed. [PUBLICATION ABSTRACT]
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ISSN:1436-3240
1436-3259
DOI:10.1007/S00477-003-0153-5