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

OBJECTIVES

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, such as grades. You’ll also learn how to post-process results to generate accurate grade-tonnage curves.

COURSE CONTENT
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.

OUTLINES

• Balanced learning approach: The course combines theory with practical applications, ensuring concepts are understood and applied effectively.
• Hands-on software training: Engage in computer-based exercises using Isatis.neo software, reinforcing learning through real-world data scenarios.
• Personalized feedback: Receive individualized guidance and feedback from experienced trainers during online sessions to support your learning journey.
• Comprehensive resources: Access detailed course materials, including documentation, journal files, and datasets, to reinforce learning and facilitate application post-training.

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.
 

PREREQUISITES

• Basic knowledge of linear geostatistics is recommended. The course Mineral Resource Estimation, which covers the fundamental concepts of geostatistics for resource estimation, offers an ideal basis for this advanced course.
• A basic understanding of resource concepts such as grade, tonnage, and cut-off is beneficial.
• You can enhance your skills by attending the two additional modules of this course: Module 2 focuses on Multiple Indicator Kriging, while Module 3 covers Simulations of continuous variables, to calculate metal and tonnage quantities in both modules.