Recoverable Resource Estimation by nonlinear geostatistics – Module 3: Simulations

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

COURSE CONTENT

Introduction

• Understand the fundamentals of recoverable resource estimation and its critical role in resource modeling and mine planning.

Simulations

• Master simulation general concepts. Learn the theory.
• Model the Gaussian anamorphosis: Transform any distributions into Gaussian ones, a necessary step for nonlinear modeling.
• Discover two widely used conditional simulation methods: Turning Bands Simulation (TBS) and Sequential Gaussian Simulation (SGS). Understand their theoretical foundations, practical applications, and where each method performs best.
• Unlock the power of Direct Block Simulations: Bypass the traditional point-scale modeling approach with this efficient technique that generates block-scale simulations directly, saving valuable time and disk space without compromising accuracy.

Post-processing of simulation results

• Produce robust grade-tonnage curves from simulations 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 participating in the two additional modules of this course: Module 1 focuses on Uniform Conditioning, while Module 2 focuses on Multiple Indicator Kriging, the two modules aiming at calculating metal and tonnage quantities.