Author:
Cook A.,Rondon O.,Graindorge J.,Booth G.
Abstract
AbstractMultivariate conditional simulations can be reduced to a set of independent univariate simulations through multivariate Gaussian transformation of the drill hole data to independent Gaussian factors. These simulations are then back transformed to obtain simulated results that exhibit the multivariate relationships observed in the input drill hole data. Several transformation techniques are cited in geostatistical literature for multivariate transformation. However, only two can effectively simulate high dimensional drill hole data with complex non-linear features: Flow Anamorphosis (FA) and Projection Pursuit Multivariate Transformation (PPMT). This paper presents an alternative iterative multivariate Gaussian transformation (IG) along with a multivariate simulation case study of a large Nickel deposit. Our findings show that IG is computationally faster than FA and PPMT which makes the technique more appealing for most practical and time-sensitive applications.
Publisher
Springer International Publishing
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