Geostatistical inputs to resource classification | Training course

Learn about geostatistical techniques to assess mineral resource confidence and classify resources.


  • Find out about resource reporting and classification according to Mining codes (specific example of JORC).
  • Familiarize yourself with the various geostatistical techniques available to assess estimates’ confidence levels and their advantages and disadvantages.
  • Learn to classify resources using various criteria applied to kriging or simulation results or advanced techniques.


  • The first two days of the course are devoted to methodological presentations, and the last half-day to practical exercises on a real data set to deepen understanding of concepts.
  • Computer exercises with Isatis.neo Mining Edition.
  • Course material and temporary software license provided.

Who should attend

This course is designed for mining professionals who wish to familiarize themselves with the various geostatistical techniques that can be used to assess resource confidence levels and classify mineral resources accordingly. 

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.


As the course refers to advanced geostatistical concepts, it is strongly recommended that participants have a sound knowledge of variography, kriging, and simulations. Alternatively, participants may have completed the “Mineral Resource Estimation” or the “Conditional Simulation and Uncertainty Analysis in Isatis.neo Mining” training course.