An iterative stochastic inverse approach for steady-state flow in heterogeneous, variably saturated porous media.

Persistent Link:
http://hdl.handle.net/10150/187317
Title:
An iterative stochastic inverse approach for steady-state flow in heterogeneous, variably saturated porous media.
Author:
Zhang, Jinqi.
Issue Date:
1996
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
An iterative stochastic inverse technique utilizing both primary and secondary information is developed to estimate conditional means of unsaturated hydraulic conductivity parameters (saturated hydraulic conductivity and pore-size distribution parameters) in the vadose zone. Measurements of unsaturated hydraulic conductivity parameters are considered as the primary information, while measurements of flow processes (soil-water pressure head and degree of saturation) are regarded as the secondary information. This inverse approach is similar to the classical geostatistical method, which utilizing a linear estimator that depends upon the (cross-)covariance functions of primary and secondary information. The linear estimator is, however, improved by solving the governing flow equation and by updating the residual (cross-)covariance functions, in an iterative manner. Using first-order Taylor series expansion of a discretized finite element equation, the (cross-)covariance functions of the primary and secondary information are derived. The sensitivity matrices in Taylor series expansion are evaluated by an adjoint sensitivity analysis. As a result, the nonlinear relations between unsaturated hydraulic conductivity parameters and flow processes are incorporated in the estimation. Through some numerical examples, the iterative inverse model demonstrates its ability to improve the estimates of unsaturated hydraulic conductivity parameters compared to the classical geostatistical inverse approach. In addition, the inconsistency problem existing in classical geostatistical inverse approach is alleviated. The estimated fields of unsaturated hydraulic conductivity parameters and flow fields not only retain their observed values at sample locations, but satisfy the governing flow equation as well.
Type:
text; Dissertation-Reproduction (electronic)
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Hydrology and Water Resources; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Yeh, T.-C. Jim

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleAn iterative stochastic inverse approach for steady-state flow in heterogeneous, variably saturated porous media.en_US
dc.creatorZhang, Jinqi.en_US
dc.contributor.authorZhang, Jinqi.en_US
dc.date.issued1996en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractAn iterative stochastic inverse technique utilizing both primary and secondary information is developed to estimate conditional means of unsaturated hydraulic conductivity parameters (saturated hydraulic conductivity and pore-size distribution parameters) in the vadose zone. Measurements of unsaturated hydraulic conductivity parameters are considered as the primary information, while measurements of flow processes (soil-water pressure head and degree of saturation) are regarded as the secondary information. This inverse approach is similar to the classical geostatistical method, which utilizing a linear estimator that depends upon the (cross-)covariance functions of primary and secondary information. The linear estimator is, however, improved by solving the governing flow equation and by updating the residual (cross-)covariance functions, in an iterative manner. Using first-order Taylor series expansion of a discretized finite element equation, the (cross-)covariance functions of the primary and secondary information are derived. The sensitivity matrices in Taylor series expansion are evaluated by an adjoint sensitivity analysis. As a result, the nonlinear relations between unsaturated hydraulic conductivity parameters and flow processes are incorporated in the estimation. Through some numerical examples, the iterative inverse model demonstrates its ability to improve the estimates of unsaturated hydraulic conductivity parameters compared to the classical geostatistical inverse approach. In addition, the inconsistency problem existing in classical geostatistical inverse approach is alleviated. The estimated fields of unsaturated hydraulic conductivity parameters and flow fields not only retain their observed values at sample locations, but satisfy the governing flow equation as well.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineHydrology and Water Resourcesen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairYeh, T.-C. Jimen_US
dc.contributor.committeememberWarrick, A. W.en_US
dc.contributor.committeememberZreda, Mareken_US
dc.contributor.committeememberContractor, Dinshaw N.en_US
dc.contributor.committeememberLansey, Kevinen_US
dc.identifier.proquest9620379en_US
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