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Application of Gaussian Mixture Model and Geostatistical Co-simulation for Resource Modeling of Geometallurgical Variables
Natural Resources Research ( IF 5.4 ) Pub Date : 2021-01-09 , DOI: 10.1007/s11053-020-09802-4
Yerkezhan Madenova , Nasser Madani

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.



中文翻译:

高斯混合模型和地统计学协同模拟在地质冶金变量资源建模中的应用

这项工作解决了地质冶金变量资源计算的实践。与矿产资源建模相似,应首先确定地质冶金变量的估计域。然后,需要在每个域中分别对被认为是同质的地球冶金变量进行建模。这样做的困难与地质冶金变量有关,很少能与矿床的地质解释相一致。为了克服这一困难,在铁矿石矿床中采用了一种机器学习方法,即高斯混合模型技术,以获得能够形成地球冶金领域的团簇。从地质冶金的角度证实所获得的域是合理的之后,提供了一种技术,用于按照概率级联方法共同模拟获得的地质冶金领域内的地质冶金变量。后者允许在建模过程中通常忽略的变量之间引入交叉依赖性。该算法表明,所提出的技术在统计上是有效的,并且可以应用于最佳的矿石加工厂和战略性矿山设计,而仅定义等级可能不足以决定进一步优化采矿项目。还表明,作为一种指导,所提出的算法如何能够为资源计算提供地球冶金变量的恢复函数。后者允许在建模过程中通常忽略的变量之间引入交叉依赖性。该算法表明,所提出的技术在统计上是有效的,并且可以应用于最佳的矿石加工厂和战略性矿山设计,而仅定义等级可能不足以决定进一步优化采矿项目。还表明,作为一种指导,所提出的算法如何能够为资源计算提供地球冶金变量的恢复函数。后者允许在建模过程中通常忽略的变量之间引入交叉依赖性。该算法表明,所提出的技术在统计上是有效的,并且可以应用于最佳的矿石加工厂和战略性矿山设计,而仅定义等级可能不足以决定进一步优化采矿项目。还表明,作为一种指导,所提出的算法如何能够为资源计算提供地球冶金变量的恢复函数。

更新日期:2021-01-10
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