Geostatistics

Importance of Resource Modelling and Estimation in the Value Chain of Mining, Uni-variate and Multi-variate Explorative Data Analysis, Analysis of Spatial Continuity, the Spatial Random Function Model, Model Assumptions of Stationarity and Ergodicity, Inference of a Spatial Random Function using unbiased Estimators, Dealing with Preferential Sampling, Variography and Variogram Modelling, Simple Methods for Spatial Estimation including the Polygon Method, Triangulation, Inverse Distance Power and Polynomial Regression, Geostatistical Methods for Spatial Estimation including Simple Kriging, Ordinary Kriging and Universal Kriging, Integrating Secondary Information into Spatial Modelling using Techniques of Co-Kriging, other methods including Indicator Kriging and Block Kriging, Introduction in Modelling spatial Uncertainty using Conditional Simulation, the Method of Sequential, Gaussian Simulation, Geostatistical Considerations in Estimating Reserves in Terms of Volume-Variance Relationship for defining Smallest Minable Units and Grade Tonnage Curves, Applications in Mining Cases, Introduction to CRIRSCO-based International Reporting standards (example JORC Code)

The following course/module was developed at the TU Bergakademie Freiberg.

Learning outcomes / Competencies:

• Theoretical Understanding: Articulating the theoretical underpinnings of spatial data analysis, including key concepts and principles that govern the field.
• Geostatistical Model Building: Constructing geostatistical models and mastering techniques for estimation, providing a foundation for accurate resource estimation.
• Application of Geostatistical Methods: Applying geostatistical methods to estimate natural resources and reserves, utilizing these techniques to quantify and assess subsurface materials.
• Model Assumption Evaluation: Critically evaluating assumptions within different estimation and simulation methods, enabling informed choices of appropriate methodologies for specific applications.
• Method Selection: Discerning and selecting suitable estimation and simulation methods based on the context of resource estimation, ensuring accurate and reliable results.
• SMU-size Significance: Recognizing the critical role of the SMU (Sample Mining Unit) size in recovering reserves, understanding its impact on resource assessment accuracy.
• Case Study Proficiency: Conducting a resource/reserve estimation in a simplified case study scenario, putting theoretical knowledge into practice and demonstrating the ability to apply geostatistical techniques effectively.