Geostatistical inputs to resource classification

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 a range of geostatistical techniques, such as kriging, conditional simulations, and uncertainty quantification, that help 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.

Course 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 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, 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 the global estimation variance, the Spatial Sampling Density Variances (SSDV) and the related specific volume, coefficient of variation, and risk index.

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 

This course is designed for mining professionals who wish to familiarize themselves with geostatistical techniques for assessing resource confidence levels and classifying mineral resources accordingly.

As the course covers advanced geostatistical concepts, it is strongly recommended that participants have a solid understanding of variography, kriging, and simulation. Alternatively, participants may have completed the “Mineral Resource Estimation” training course.

This course can also be followed by an “à la carte” workshop based on your data.