Recoverable Resource Estimation by nonlinear geostatistics – Module 2: Multiple Indicator Kriging and Conditional Expectation | Training course

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
  – Master the fundamentals of recoverable resource estimation and understand its critical role in resource modeling and mine planning.
• Multiple Indicator Kriging (MIK)
  – Dive into MIK theory, workflow, and key variants to effectively estimate resources.
  – Discover best practices for applying MIK using Isatis.neo, including key settings and strategic choices.
  – Weigh the pros and cons of MIK and understand where and when it delivers the best results.
  – Generate accurate grade-tonnage curves using MIK outputs for confident decision-making.
• Conditional Expectation (CE)
  – Learn CE basic principles and theory and how the technique fits into the nonlinear estimation toolkit.
  – Explore CE variants, including their link to multi-Gaussian kriging approaches.
  – Explore Ordinary Multi-Gaussian Kriging as a foundation for implementing CE in practice.
  – Compare the strengths and limitations of CE and discover its ideal application domains.
  – Produce robust grade-tonnage curves from CE results to support your resource evaluations.
  – Get hands-on with Isatis.neo: Learn the available CE options, including block estimates and multivariate modeling.

• 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.

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.

• Basic knowledge of linear geostatistics is recommended. The course Mineral Resource Estimation, covering 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 3 covers Simulations of continuous variables, with the aim of calculating metal and tonnage quantities in both modules.