Support vector classification for geostatistical modeling of categorical variables

• Author / Creator

  Gallardo, Enrique

• 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.

• Subjects / Keywords

  • Geostatistics

• Graduation date

  Fall 2009

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R35716

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.