Show simple item record

dc.contributor.authorHanna, Samuel Kamel.
dc.creatorHanna, Samuel Kamel.en_US
dc.date.accessioned2011-10-31T18:39:08Z
dc.date.available2011-10-31T18:39:08Z
dc.date.issued1995en_US
dc.identifier.urihttp://hdl.handle.net/10150/187402
dc.description.abstractAquifer heterogeneity plays an important role in the remediation of polluted aquifers. Kriging (or cokriging) is one of the popular methods used to interpolate and extrapolate measured transmissivity data. However, depicting aquifer heterogeneity at a high resolution is a difficult task. Traditional numerical inverse approaches often suffer from numerical stability problems. Also, the classical geostatistical inverse methods, e.g. cokriging, rely heavily on the linearized form of the flow equation without incorporating the principles of continuity and boundary effect. Classical cokriging is a linear predictor approach which uses approximate covariance and cross covariance functions. Therefore, it often produces inconsistent transmissivity and head fields which results in unacceptable velocity distributions, especially for highly heterogeneous aquifers. To circumvent these difficulties, an iterative stochastic approach to estimate transmissivity and head distributions in two-dimensional, steady-state, nonuniform flow in heterogeneous confined aquifers is developed. This approach is similar to the classical cokriging technique, using a linear estimator that depends on the approximate covariance functions. The linear estimator is improved successively by solving the governing flow equation and by updating the covariances and cross-covariance functions in an iterative manner. As a result, the nonlinear relationship between transmissivity and head is incorporated. At each iteration, the objective is to minimize the estimation mean square error "MSE" of the estimate of the natural logarithm of transmissivity, Ln (T). The iterations continue until the difference in the simulated variance of the Ln (T) field between two successive iterations is less than a specified tolerance level. The results show that the estimated transmissivity and hydraulic head fields have smaller MSE than those by the classical cokriging even in aquifers with high transmissivity variances. Also, our iterative method is superior to classical cokriging method for producing mass conservative velocity fields and less biased estimation error in both Ln (T) and head fields. Similarly, a comparison study between non-iterative and iterative co-conditional Monte Carlo simulation "MCS" is conducted to show the effect of iterations on the simulation. The Conditional mean fields based on our iterative MCS are better than those non-iterative MCS, which are close to the cokriging ones, in terms of less MSE and less bias.
dc.language.isoenen_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.titleAn iterative Monte Carlo technique for estimating conditional means and variances of transmissivity and hydraulic head fields.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.contributor.chairYeh, T-C. Jimen_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberMaddock, Thomas IIIen_US
dc.contributor.committeememberMacNish, Roberten_US
dc.contributor.committeememberContractor, Dinshaw N.en_US
dc.contributor.committeememberInce, Simonen_US
dc.identifier.proquest9622978en_US
thesis.degree.disciplineHydrology and Water Resourcesen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.namePh.D.en_US
refterms.dateFOA2018-09-03T12:30:58Z
html.description.abstractAquifer heterogeneity plays an important role in the remediation of polluted aquifers. Kriging (or cokriging) is one of the popular methods used to interpolate and extrapolate measured transmissivity data. However, depicting aquifer heterogeneity at a high resolution is a difficult task. Traditional numerical inverse approaches often suffer from numerical stability problems. Also, the classical geostatistical inverse methods, e.g. cokriging, rely heavily on the linearized form of the flow equation without incorporating the principles of continuity and boundary effect. Classical cokriging is a linear predictor approach which uses approximate covariance and cross covariance functions. Therefore, it often produces inconsistent transmissivity and head fields which results in unacceptable velocity distributions, especially for highly heterogeneous aquifers. To circumvent these difficulties, an iterative stochastic approach to estimate transmissivity and head distributions in two-dimensional, steady-state, nonuniform flow in heterogeneous confined aquifers is developed. This approach is similar to the classical cokriging technique, using a linear estimator that depends on the approximate covariance functions. The linear estimator is improved successively by solving the governing flow equation and by updating the covariances and cross-covariance functions in an iterative manner. As a result, the nonlinear relationship between transmissivity and head is incorporated. At each iteration, the objective is to minimize the estimation mean square error "MSE" of the estimate of the natural logarithm of transmissivity, Ln (T). The iterations continue until the difference in the simulated variance of the Ln (T) field between two successive iterations is less than a specified tolerance level. The results show that the estimated transmissivity and hydraulic head fields have smaller MSE than those by the classical cokriging even in aquifers with high transmissivity variances. Also, our iterative method is superior to classical cokriging method for producing mass conservative velocity fields and less biased estimation error in both Ln (T) and head fields. Similarly, a comparison study between non-iterative and iterative co-conditional Monte Carlo simulation "MCS" is conducted to show the effect of iterations on the simulation. The Conditional mean fields based on our iterative MCS are better than those non-iterative MCS, which are close to the cokriging ones, in terms of less MSE and less bias.


Files in this item

Thumbnail
Name:
azu_td_9622978_sip1_m.pdf
Size:
6.666Mb
Format:
PDF
Description:
azu_td_9622978_sip1_m.pdf

This item appears in the following Collection(s)

Show simple item record