Improved Resource Estimates with Multiple Data Types

• Author / Creator

  Kim, Jinpyo

• Multiple data types should be used simultaneously to improve resource estimation models. The multivariate relationship between the data is required. One common approach involves using decorrelation transformation techniques to simplify complex relationships, but this method relies on having collocated data. With heterotopic data, these techniques cannot be applied.

  A Data Error Model (DEM) is developed as a solution to the challenge of using multiple data types that are not sampled at the same location. This model quantifies relationships between different types of data, even if they are not collocated. The workflow of DEM involves pairing analysis to understand the relationships between variables at different locations. The parameters of the DEM account for errors and biases in different data types. The DEM describes the relationships that emerge in pairing analysis with primary and secondary data types. Applying the DEM to simulated primary data produces collocated secondary data distributions. This allows us to obtain the relationships between two variables.

  The thesis proposes a method to improve the accuracy of resource estimation models by facilitating the use of multiple data types. The DEM is used to infer the relationships between different types of data, even if they are not collocated. The relationship inferred from DEM can be expressed as a Gaussian Mixture Model (GMM), which underlies the conditional distribution needed to impute collocated primary or error-free values. Imputation allows for the creation of estimation models conditioning to primary variable and secondary variable data. A case study using data from a Nevada gold mine demonstrates the improved estimates.

• Subjects / Keywords

  • Mining
  • Geostatistics
  • Multiple data types
  • Heterotopic data
  • Data Error Model
  • Imputation
  • Data relationships

• Graduation date

  Spring 2024

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-pbjz-2z62

• 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.