TY - JOUR
T1 - Application of Gaussian Mixture Model and Geostatistical Co-simulation for Resource Modeling of Geometallurgical Variables
AU - Madenova, Yerkezhan
AU - Madani, Nasser
N1 - Funding Information:
The authors are grateful to Nazarbayev University for funding this work via “Faculty Development Competitive Research Grants for 2018–2020 under Contract No. 090118FD5336.” The authors also thank the Geovariances Company for providing the dataset. We are also grateful to two anonymous reviewers for their valuable comments, which substantially helped improving the final version of the manuscript.
Publisher Copyright:
© 2021, International Association for Mathematical Geosciences.
PY - 2021/4
Y1 - 2021/4
N2 - 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.
AB - 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.
KW - Co-simulation
KW - Gaussian mixture model
KW - Geometallurgical domain
KW - Geometallurgical variables
KW - Probabilistic weighting
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U2 - 10.1007/s11053-020-09802-4
DO - 10.1007/s11053-020-09802-4
M3 - Article
AN - SCOPUS:85099316773
SN - 1520-7439
VL - 30
SP - 1199
EP - 1228
JO - Natural Resources Research
JF - Natural Resources Research
IS - 2
ER -