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Uncertainty in the Global Mean for Improved Geostatistical Modeling

  • Author / Creator
    Villalba Matamoros, Martha Emelly
  • Analysis of uncertainty in ore reserves impacts investment decisions, mine planning and sampling. Uncertainty is evaluated by geostatistical simulation and is affected by the amount of data and the modeling parameters. Incomplete uncertainty is given because the parameter uncertainty is ignored. Also, greater spatial continuity leads to more uncertainty. This increase is unreasonable in earth science. To address these problems, two approaches are proposed. The first approach is based on multiGaussian simulation where many realizations are performed at translated and/or rotated configurations and conditioned to the data. Variable configurations give different mean values that define uncertainty. The second approach is based on a stochastic trend; this approach randomizes the trend coefficients accounting for the fitted coefficients correlation. Variable set of coefficients provide different mean values. Furthermore, a methodology to account for parameter uncertainty is proposed. The uncertainty in the mean is transferred through simulation to deliver a more complete uncertainty.

  • Subjects / Keywords
  • Graduation date
    2011-11
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3RH0V
  • 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
  • Supervisor / co-supervisor and their department(s)
    • Dr. Clayton V. Deutsch (Civil and Environmental Engineering)
  • Examining committee members and their departments
    • Dr. Peng Zhang (Math and Statistical Sciences)
    • Dr. Jeffery B. Boisvert (Civil and Environmental Engineering)