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Prediction of Local Uncertainty for Resource Evaluation

  • Author / Creator
    Daniels, Eric B.
  • Resource evaluation of a mineral deposit is an important and challenging task. Traditional estimation methods provide a best estimate at the block scale based on available data. The pitfalls of histogram smoothing and conditional bias associated with these methods are well known. Quantifying local uncertainty is an alternative to estimation that avoids these issues. Multiple methods have been established for quantifying local uncertainty and are presented here. This thesis explores the local dependence of change of support methods as well as improves upon available post processing techniques associated with quantifying uncertainty for resource evaluation. Change of support parameters are examined through a simulation test. The advertised benefits and issues surrounding localization of uncertainty are evaluated and a flexible methodology for localization is demonstrated. Artifact reduction techniques are provided for improved localization when a single model is required. Lastly, a case study demonstrates practical implementation of geostatistical methods and post-processing for modeling block scale uncertainty and resource evaluation.

  • Subjects / Keywords
  • Graduation date
    2015-06
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R35717W74
  • 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.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Civil and Environmental Engineering
  • Specialization
    • Mining Engineering
  • Supervisor / co-supervisor and their department(s)
    • Clayton Deutsch (Mining Engineering-Geostatistics)
  • Examining committee members and their departments
    • Dr. Jeff Boisvert (Mining Engineering - Geostatistics)
    • Dr. Hooman Askari-Nasab (Mining Engineering)