Geostatistics of Geometallurgical Parameters
Creating reliable potential analyzes is a prerequisite for the efficient mining of deposits. Often, however, these potential analyzes are based on a few borehole and rock investigations, sometimes of different origin. In the future, the data situation will not improve. On the contrary, deposits are found in increasingly lower locations or show a higher complexity. In addition, there are a variety of different investigation methods with different data types.
In order to deduce an entire 3D space from an individual data point, mathematical interpolation methods from geostatistics need to be applied. This way, the Modelling and Valuation Department predicts spatial distributions and quantifies the uncertainties associated with the estimation. In doing so, scientists focus primarily on geostatistical simulations and geostatistics of nonlinear scales of ore bodies necessary for the interpolation of geometallurgical parameters (e.g., modal mineralogy or particle size distribution).
Researching that topic is crucial, because modelling an uncertainty correctly, demonstrably has a decisive effect on the economic processing result. For this purpose, the Department works closely with the Departments of Exploration and Analytics, as they produce the basic data but also co-develop methods - for example, for the modelling of deposits using hyperspectral and geochemical data or the interpolation of geometallurgical properties in mining tailings.
- Van den Boogaart, K.G., Mueller, U., Tolosana-Delgado, R.
"An affine equivariant multivariate normal score transform for compositional data", Mathematical Geosciences, 2016
- Van den Boogaart, K.G., Tolosana-Delgado, R., Mueller, U., Matos-Camacho, S.
How details of the Geometallurgical Optimisation influence the overall value, in Proceedings of GeoMet2016, the Third AusIMM International Geometallurgy Conference, 2016
- Tolosana-Delgado, R., Mueller, U., van den Boogaart, K.G, Ward, C., Gutzmer J.
Improving processing by adaption to conditional geostatistical simulation of block compositions, The Journal of the South African Institute of Mining and Metallurgy, 2015