Geostatistics and Resources Classification


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Managers, exploration geologists, mining engineers involved in feasibility studies or medium to long term planning. A good knowledge of geostatistics, including an understanding of non linear techniques (gaussian anamorphosis, simulations) is recommended.


The course presents what is at stake in resources classification and assessment of the estimates uncertainty. The kriging variance is a convenient tool for comparing different sampling strategies, but it is not an appropriate tool for the derivation of a true confidence interval, because the kriging variance is not conditioned to the data. A better solution consists in computing many conditional simulations, but this is time consuming. To obtain a confidence interval, simple models, like the discrete gaussian model may be sufficient and give an acceptable answer for the purpose of resources classification.


  • Reminders on resources and reserves notions.
  • Concepts of variances calculated in geostatistics (dispersion variance, estimation variance).
  • The kriging variance and its properties.
  • Data distribution and kriging.
  • Gaussian based methods: normal score transformation using the anamorphosis function.
  • Change of support within the discrete gaussian model.
  • Calculation of the confidence intervals using the discrete gaussian model.
  • Geostatistical simulations for achieving the same tasks and comparison with the previous results.
  • Available tools for analysing the results.
  • Discussion on the limitations of the method and solutions.


Half of the course is dedicated to practical computer exercises, using Isatis, that reinforce the previously presented theoretical notions.