Training
Recoverable Resource Estimation by nonlinear geostatistics – Module 1: Uniform Conditioning
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
- Recognize where a kriged model stops being usable for cut-off reporting, and why wide-spaced drilling makes the smoothing worse.
- Transform your grade distribution with a Gaussian anamorphosis, and apply the change of support from core to block scale.
- Apply Uniform Conditioning to estimate grade, tonnage, and metal quantities at any cut-off, and correct for the information effect.
- Localize UC at block or SMU level to produce a model mine planning can actually use, including on multi-domain and multivariate deposits.
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
- 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 anamorphosis: 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 variance – core 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.
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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