Multivariate geostatistical simulation with PPMT: an application for uncertainty measurement

Abstract

ABSTRACT Grade models built by traditional estimation or simulation methods often fail to reproduce the complex relationships between the variables. This work investigates the use of the multivariate transformation called Projection Pursuit Multivariate Transform (PPMT), which fully decorrelates the multiple variables of interest, allowing the independent conditional simulation of each variable in the transformed space. Finally, the simulated variables are back-transformed, reproducing the initial correlations of the data. The PPMT workflow was applied to a nickel laterite deposit considering five variables: nickel, iron, silica, magnesium, and calcium grades. Conditional simulations of each variable were run and validated. The back-transformed realisations reproduced the multivariate relationships of the data. To calculate the uncertainties, mining panels equivalent to two and four weeks of production were generated using the k-means clustering technique. Uncertainties were summarised by the coefficient of variation (CV) and the results were used to define classes of mineral resources.