Application of Gaussian Mixture Model and Geostatistical Co-simulation for Resource Modeling of Geometallurgical Variables

Abstract

This work addresses the practice of resource calculation for geometallurgical variables. Similar to mineral resource modeling, estimation domains for geometallurgical variables should be identified first. Then, the geometallurgical variables that are deemed homogeneous need to be modeled separately in each domain. A difficulty for this is related to the geometallurgical variables that can rarely be in agreement with the geological interpretation of a deposit. To circumvent this difficulty, a machine learning approach, namely Gaussian mixture model technique, is employed in an Fe ore deposit to obtain clusters that can turn out the geometallurgical domains. After corroborating that the obtained domains are reasonable from a geometallurgical perspective, a technique is provided to co-simulate the geometallurgical variables within the attained geometallurgical domains following a probabilistic cascade approach. The latter allows incorporation of cross-dependency among the variables that usually are neglected in the modeling process. The algorithm showed that the proposed technique is statistically valid and can be applied for optimum ore processing plant and strategic mine design, where defining the grade alone may not be enough for deciding on further optimization of a mining project. It is also showed that as an instruction, how the proposed algorithm can provide the recovery functions of the geometallurgical variables for resource calculation.