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Permanent link (DOI): https://doi.org/10.7939/R35716

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Support vector classification for geostatistical modeling of categorical variables Open Access

Descriptions

Other title
Subject/Keyword
Geostatistics
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Gallardo, Enrique
Supervisor and department
Leuangthong, Oy (Civil and Environmental Engineering)
Examining committee member and department
Szymanski, Jozef (Civil and Environmental Engineering)
Ray, Nilanjan (Computing Science)
Department
Department of Civil and Environmental Engineering
Specialization

Date accepted
2009-08-26T18:40:38Z
Graduation date
2009-11
Degree
Master of Science
Degree level
Master's
Abstract
Subsurface geological characterization requires solving a classification problem to obtain a model of facies that is later populated with continuous properties. The classification problem, which consists of assigning a single category to any unsampled location based on observed data, is analyzed and solved in this thesis using geostatistical and machine learning tools. This research proposes an easy-to-implement heuristic technique that uses geostatistical criteria, such as correct classification of the observed data and good reproduction of the global proportions of categories, to obtain from the SVC algorithm a boundary classifier. This boundary is used to generate the facies model. The case studies show that the implementation of the proposed technique is highly automatic. The responses are comparable in terms of prediction accuracy to those obtained by the conventional geostatistical approach.
Language
English
DOI
doi:10.7939/R35716
Rights
License granted by Enrique Gallardo (egallard@ualberta.ca) on 2009-08-25T19:54:47Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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