Statistical inverse modeling and geostatistical analysis of the Avra Valley aquifer

Persistent Link:
http://hdl.handle.net/10150/191730
Title:
Statistical inverse modeling and geostatistical analysis of the Avra Valley aquifer
Author:
Clifton, Peter Maxwell.
Issue Date:
1981
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:
Avra Valley is a deep, elongate alluvial basin in southern Arizona, which contains an extensive unconfined aquifer. This aquifer was modeled by using a statistical inverse model. The input data required by the inverse model are (1) spatially averaged logtransmissivity estimates in zones of the aquifer, (2) steady-state hydraulic head estimates at points in the aquifer, and (3) the covariance matrices of log-transmissivity and hydraulic-head estimation errors. The geostatistical interpolation technique of kriging was used to assign the spatially averaged log-transmissivities and to compute the covariance matrix of the log-transmissivity estimation errors. Estimates of the steady-state hydraulic heads were made by interpolating between known data points. Two independent determinations of the covariance matrix of the hydraulic-head estimation errors were made. The output from the inverse model is a modified set of spatially averaged logtransmissivities and the covariance matrix of their associated estimation errors. The magnitude of the estimation errors of this modified set of log-transmissivities is less than the magnitude of the estimation errors of the kriged log-transmissivities. A conditional simulation analysis was also performed to assess the magnitude of errors in the hydraulic heads predicted by the kriged log-transmissivity field and the logtransmissivity field computed by the inverse model.
Type:
Thesis-Reproduction (electronic); text
LCSH Subjects:
Hydrology.; Groundwater -- Arizona -- Avra Valley.; Groundwater -- Mathematical models.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Hydrology and Water Resources; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Neuman, Shlomo P.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleStatistical inverse modeling and geostatistical analysis of the Avra Valley aquiferen_US
dc.creatorClifton, Peter Maxwell.en_US
dc.contributor.authorClifton, Peter Maxwell.en_US
dc.date.issued1981en_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.abstractAvra Valley is a deep, elongate alluvial basin in southern Arizona, which contains an extensive unconfined aquifer. This aquifer was modeled by using a statistical inverse model. The input data required by the inverse model are (1) spatially averaged logtransmissivity estimates in zones of the aquifer, (2) steady-state hydraulic head estimates at points in the aquifer, and (3) the covariance matrices of log-transmissivity and hydraulic-head estimation errors. The geostatistical interpolation technique of kriging was used to assign the spatially averaged log-transmissivities and to compute the covariance matrix of the log-transmissivity estimation errors. Estimates of the steady-state hydraulic heads were made by interpolating between known data points. Two independent determinations of the covariance matrix of the hydraulic-head estimation errors were made. The output from the inverse model is a modified set of spatially averaged logtransmissivities and the covariance matrix of their associated estimation errors. The magnitude of the estimation errors of this modified set of log-transmissivities is less than the magnitude of the estimation errors of the kriged log-transmissivities. A conditional simulation analysis was also performed to assess the magnitude of errors in the hydraulic heads predicted by the kriged log-transmissivity field and the logtransmissivity field computed by the inverse model.en_US
dc.description.notehydrology collectionen_US
dc.typeThesis-Reproduction (electronic)en_US
dc.typetexten_US
dc.subject.lcshHydrology.en_US
dc.subject.lcshGroundwater -- Arizona -- Avra Valley.en_US
dc.subject.lcshGroundwater -- Mathematical models.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineHydrology and Water Resourcesen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairNeuman, Shlomo P.en_US
dc.contributor.committeememberDavis, Stanley N.en_US
dc.contributor.committeememberDavis, Donald R.en_US
dc.identifier.oclc212868912en_US
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