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Resource Estimation with Multiple Data Types

  • Author / Creator
    Donovan, Patrick N
  • Improving the accuracy of estimates is of great importance in the mining industry. These estimates can be improved with the use of available secondary data. This includes any less trusted data source containing bias and error such as production sampling, legacy drill holes, or cheaper lower quality measurements. Cokriging methods allow the application of this secondary data without transmitting bias and error through to the final estimates. When secondary data is unequally sampled compared to the primary data source it is difficult to establish correlation between the data types. This is solved by modelling the cross covariance curve. A case study using exploration and production data shows that cokriging provides a more accurate result and a reasonable decrease in average estimation variance over ordinary kriging methods. This application of cokriging can be extended to improve resource classification in terms of data spacing. A decrease in average estimation variance using cokriging methods can be shown to relate to a decreased 'effective' data spacing for classification purposes. This spacing reflects the improvement in the estimates with the use of secondary data. A repeatable power model relationship with correlation is defined to relate average estimation variance improvement to data spacing. A case study shows an application with real exploration and production data. The result is a scaling of combined data spacing with reference to the information content of the secondary data type.

  • Subjects / Keywords
  • Graduation date
    2016-06
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R30863B13
  • 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)
    • Deutsch, Clayton (Civil and Environmental Engineering)
  • Examining committee members and their departments
    • Deutsch, Clayton (Civil and Environmental Engineering)
    • Liu, Victor ((Civil and Environmental Engineering)
    • Boisvert, Jeff (Civil and Environmental Engineering)