Training

Geostatistical inputs to resource classification

Make resource classification more quantitative and defensible. Use kriging, simulations and uncertainty measures to apply auditable JORC criteria with greater confidence.
Next session Nov. 2-4, 2026
Duration 2,5 days
Price EUR 1,350

Objectives

  • Understand resource classification principles.
    Gain foundational knowledge of resource reporting and classification frameworks with a specific focus on the JORC Code.
  • Master geostatistical methods for confidence assessment.
    Explore geostatistical techniques such as kriging, conditional simulations, and uncertainty quantification to assess the reliability of resource estimates. Identify their strengths, limitations, and suitability for different classification contexts.
  • Apply classification criteria to resource models.
    Learn practical approaches to classifying resources using quantitative criteria derived from kriging or simulation results. Develop skills to apply advanced geostatistical tools for robust, auditable classification of resources into Inferred, Indicated, and Measured categories.

Target audience

This course is designed for mining professionals who want to learn the geostatistical techniques used to assess resource confidence levels and classify mineral resources accordingly.

Content

 

  • Review of JORC definitions regarding mineral resource classification: Competent Person, inferred-indicated-measured resources, resource reporting, resource classes.
  • Resource classification using the kriging neighborhood parameters.
  • How to enhance the accuracy of resource estimates through Kriging Neighborhood Analysis (KNA) and cross-validation to improve the confidence levels.
  • Resource classification using linear geostatistics: exploration of various classification criteria that can be applied to kriging outputs, such as standard deviation, variance, kriging efficiency, slope of regression, relative variance, variance of estimator, variance of interpolation, and risk index.
  • Resource classification using conditional simulations: exploration of various classification criteria that can be applied to simulation outputs, such as conditional variance, relative conditional variance, probability of deviation from the mean, and coefficient of variation.
  • Resource classification using advanced quantities such as global estimation variance, Spatial Sampling Density Variances (SSDV), and related specific volume, coefficient of variation, and risk index.

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

Because the course covers advanced geostatistical concepts, participants should have a solid understanding of variography, kriging, and simulation. Alternatively, participants may have completed the “Mineral Resource Estimation” training course.

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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