Mine planning is a crucial part of all mining industry projects, and nowadays, geometallurgical mapping through advanced computer technologies provides an opportunity to involve metallurgical responses of a deposit into 3D block model in order to optimise mine planning activities, especially for production forecasting. Including these metallurgical parameters apart from traditional geology and grade-based attributes into resource modelling leads to an extensive approximation for the economic maximisation of mine production due to better forecasting, planning and increasing certainty and the reliability of the resource model (Macfarlane and Williams, 2014). Commonly, geostatistical techniques and algorithms are used to produce models with the high resolution (Brissette et al. 2014; Deutsch et al. 2014; Tolosana-Delgado et al. 2015). The accuracy of the modelling procedure deeply depends on a reliable block model, produced by spatial analysis of corresponding variables that define the geological, metallurgical, mineralogical and chemical behaviour of the deposit. However, there are special cases, where the application of enhanced geostatistical methods is required, to solve the occurred difficulties between variables with multivariate complexities. For example, in oxide copper deposits, the soluble copper grade is a percentage of total copper grade and recoverable by heap leaching processes (Emery, 2012; Hosseini and Asghari, 2015). Hence, the joint spatial modelling of these variables has two difficulties.
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