Mining Geostatistics: From Data Anallysis to Reserve Estimation
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Mine and exploration geologists, mining engineers and managers involved in grade control, ore reserves estimation, feasibility studies or medium to long term planning. While the course does not require any prior knowledge of geostatistics theory, a basic understanding of statistics is recommended.

Objetivo

To give participants a sound understanding of the important concepts and methods of mining geostatistics. To cover all aspects of mining geostatistics from the basics (variograms) to the estimation of block grades, grade tonnage curves and resource classification issues.

Programa

  • Introduction: what is geostatistics and why use geostatistics ?
    The framework of geostatistics: regionalised variables, stationarity. Understanding the data through standard statistics (histograms, cross plots, declustering).
  • Calculating experimental variograms: dealing with outliers, locating the directions of mineralisation. Modelling the variogram: properties of the variogram model, nugget effect, anisotropies.
  • Dispersion and support: the distribution of block grades, additivity, regularization of the data.
  • The theory of kriging: the properties of simple and ordinary kriging, the kriging weights. Applying the theory: defining a kriging neighbourhood and cross-validation.
  • Multivariate geostatistics: calculating and modelling cross-variograms and properties of cokriging. Applying geostatistics in practice: the different steps of a study, from exploration to production.
  • Support effect for estimating the recoverable reserves after cut-off. Application to global estimation of tonnage and grade after cut-off. Local estimation using indicator kriging techniques or non-linear estimates like uniform conditioning.
  • Introduction to Simulations: reproducing the observed variability for grade control and mine planning. Overview of different techniques for simulating grades and assessing uncertainties.

 

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