Recoverable Resource Estimation by nonlinear geostatistics – Module 1: Uniform Conditioning

• This course provides a solid foundation in geostatistical methods for recoverable resource estimation. The skills you will develop will assist you in:
  – Estimating long-term resources,
  – Estimating grade-tonnage curves during exploration.It comprises three modules that can be taken separately:
• Module 1 dives into the importance of nonlinear techniques in generating unbiased grade-tonnage curves, especially in sparse sampling conditions. You will gain a deep understanding of Uniform Conditioning (UC) and confidently apply it to compute grade, tonnage, and metal quantities across various cut-offs.
• Module 2 explores Multiple Indicator Kriging and Conditional Expectation, helping you master when and how to apply each technique effectively.
• Module 3 introduces two powerful conditional simulation techniques for continuous variables like grades. You’ll also learn how to post-process results to generate accurate grade-tonnage curves.

• Introduction
  – Why kriging isn’t enough: Understand the limitations of kriging and how wide high drill hole spacing can lead to smoothing effects that underestimate variability.
  – Master the fundamentals of recoverable resource estimation and learn how to apply them in real-world mining projects.
• Transforming data
  – Model the Gaussian anamorphisms: Transform any distributions into Gaussian ones, a necessary step for nonlinear modeling.
  – Change of support made clear: Grasp the impact of support size on grade variancecore vs. block grades.
• Exploring Uniform Conditioning (UC)
  – Learn the fundamentals of UC to estimate recoverable resources for different cut-offs.
  – Understand the Information effect, how sampling density impacts your estimates, and how to correct them.
  – Localized Uniform Conditioning (LUC): Apply UC within panels at the block or SMU level to produce models compatible with mine planning.
  – Manage multi-domain and multivariate deposits.
  – Produce robust grade-tonnage curves and generate robust estimates of grade, tonnage, and metal quantities by cut-off grade from UC results to support your resource evaluations.

Who should attend 

Geologists, Mining engineers, and professionals involved in feasibility studies or medium to long-term planning who wish to deepen their theoretical and practical knowledge of mining geostatistics.