Advanced Mining Geostatistics: from theory to practice| Training course

Learn from experts in geostatistics both the theory and practice of Conditional Simulations, Uniform Conditioning, and other advanced geostatistics techniques for resource estimation and classification, and comprehensive uncertainty analysis


  • Get a refresher on geostatistics fundamentals for mineral resource estimation.
  • Understand the key concepts of non-linear geostatistics applied to resource classification, recoverable resource estimation, and uncertainty analysis.
  • Weight the pros and cons of the various methodologies introduced in the course.
  • Learn how to implement and use them in Isatis.neo Mining Edition and Isatis.


  • Course jointly conducted by Didier Renard, research professor with the Geostatistics Group from MINES ParisTech (Ecole des Mines de Paris) and Marie-Cécile Febvey, Senior Mineral Resource Consultant with Geovariances
  • Half of the course is devoted to theoretical and methodological presentations, the second half to practical exercises on real-life cases to deepen the understanding of concepts. The focus is on illustrations and practical contributions of the covered concepts.
  • Computer exercises with Isatis.neo Mining Edition and Isatis.
  • Course material provided.

Who should attend

Resource geologists, engineers and anyone seeking a sound theoretical and practical knowledge of non-linear geostatistics (including Conditional Simulations, Uniform Conditioning) for resource estimation and classification, and uncertainty analysis.

Course content


  • Reminders:
    – Geostatistic history, random function, and random variable definition
    – Geostatistics hypotheses: stationarity, ergodicity
    – Spatial data analysis: a review of various statistics charts, experimental variograms
    – Variogram calculation and modeling in univariate and multivariate environments
  • Getting started with Isatis.neo


  • Reminders:
    – The theory and properties of kriging
    – Kriging in practice including Kriging Neighborhood Analysis (KNA) and cross-validation
    – In-situ resource estimation
    – Resource classification through the spatial sampling density variance


  • Introduction to recoverable resource estimation: what is the issue?
  • Change of support and information effect correction
  • Recoverable resource estimation: introduction to Uniform Conditioning and
  • Disjunctive Kriging. Pros and cons of each technique


  • Simulation of continuous variables (grades, accumulations, …): Turning Bands and Direct Block Simulations
  • Uncertainty on local and recoverable resource estimates: simulation post-processing


  • Introduction to categorical variable simulation: Plurigaussian simulations or how to simulate a lithology
  • Resource classification through conditional simulations


As the course refers to advanced geostatistical concepts, it is highly recommended that attendees have a reasonable knowledge of variography and kriging.