Training
Recoverable Resource Estimation by nonlinear geostatistics – Module 2: Multiple Indicator Kriging and Conditional Expectation
Where this module sits
The “Recoverable Resource Estimation” course is delivered in four modules that you can take separately or in sequence.
Module 1 — Uniform Conditioning. Unbiased grade-tonnage curves from sparse drilling, localized to the SMU level.
Module 2 — Multiple Indicator Kriging and Conditional Expectation. Two nonlinear estimators and how to choose between them.
Module 3 — Simulations. Multiple realizations with Turning Bands, SGS, and direct block simulation, post-processed into grade-tonnage curves.
Module 4 — Advanced multivariate modeling. Trend, decorrelation by PCA, MAF and PPMT, imputation and ratio transforms.
Objectives
- Run Multiple Indicator Kriging end-to-end in Isatis.neo, and make the variant and setting choices it demands.
- Apply Conditional Expectation through ordinary multi-Gaussian kriging, including block estimates and multivariate cases.
- Build grade-tonnage curves from either method, and account for the differences between them.
- Choose between MIK and CE for a given deposit, and state what each one gives up.
Target audience
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.
Content
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.
What the course includes
- Balanced learning approach. Theory paired with practical applications, so you understand and apply the concepts.
- Hands-on software training. Computer exercises in Isatis.neo, on real datasets.
- Personalized feedback. Individual guidance from experienced trainers throughout the online sessions.
- Comprehensive resources. A temporary software license, course documentation, journal files, and datasets to keep and reuse after the course.
- Certificate of completion issued at the end of the session.
Prerequisites
- Basic knowledge of linear geostatistics is recommended. The Mineral Resource Estimation course, which covers the fundamental concepts of geostatistics for resource estimation, provides an ideal foundation for this advanced course.
- A basic understanding of resource concepts such as grade, tonnage, and cut-off is beneficial.
Register, or ask us first
This form handles all three: registration, a quote for yourself or your team, and questions about the content or the prerequisites.
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