Mineralogy is a fundamental characteristic of a given rock mass throughout the mining value chain. Understanding bulk mineralogy is critical when making predictions on processing performance. However, current methods for estimating complex bulk mineralogy are typically slow and expensive. Whole-rock geochemical data can be utilized to estimate bulk mineralogy using a combination of ternary diagrams and bivariate plots to classify alteration assemblages (alteration mapping), a qualitative approach, or through calculated mineralogy, a predictive quantitative approach. Both these techniques were tested using a data set of multielement geochemistry and mineralogy measured by semiquantitative X-ray diffraction data from the Productora Cu-Au-Mo deposit, Chile. Using geochemistry, samples from Productora were classified into populations based on their dominant alteration assemblage, including quartz-rich, Fe oxide, sodic, potassic, muscovite (sericite)- and clay-alteration, and least altered populations. Samples were also classified by their dominant sulfide mineralogy. Results indicate that alteration mapping through a range of graphical plots provides a rapid and simple appraisal of dominant mineral assemblage, which closely matches the measured mineralogy. In this study, calculated mineralogy using linear programming was also used to generate robust quantitative estimates for major mineral phases, including quartz and total feldspars as well as pyrite, iron oxides, chalcopyrite, and molybdenite, which matched the measured mineralogy data extremely well (R2 values greater than 0.78, low to moderate root mean square error). The results demonstrate that calculated mineralogy can be applied in the mining environment to significantly increase bulk mineralogy data and quantitatively map mineralogical variability. This was useful even though several minerals were challenging to model due to compositional similarities and clays and carbonates could not be predicted accurately.

中文翻译：

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来自全岩分析数据的矿物学预测模型：来自智利 Productora Cu-Au-Mo 矿床的案例研究

矿物学是整个采矿价值链中给定岩体的基本特征。在预测加工性能时，了解散装矿物学至关重要。然而，目前用于估计复杂块体矿物学的方法通常缓慢且昂贵。全岩地球化学数据可用于估计大块矿物学，使用三元图和二元图的组合对蚀变组合进行分类（蚀变映射），一种定性方法，或通过计算矿物学，一种预测性定量方法。这两种技术均使用多元素地球化学和矿物学数据集进行测试，该数据集通过来自智利 Productora Cu-Au-Mo 矿床的半定量 X 射线衍射数据测量。利用地球化学，来自 Productora 的样品根据它们的主要蚀变组合被分类为种群，包括富石英、氧化铁、钠、钾、白云母（绢云母）和粘土蚀变，以及变化最少的种群。样品也按其主要的硫化物矿物分类。结果表明，通过一系列图形绘制蚀变图提供了对主要矿物组合的快速和简单的评估，这与测量的矿物学密切匹配。在这项研究中，使用线性规划计算的矿物学还用于生成主要矿物相的可靠定量估计，包括石英和总长石以及黄铁矿、氧化铁、黄铜矿和辉钼矿，它们与测量的矿物学数据非常吻合 (R2值大于 0.78，低到中等的均方根误差）。结果表明，计算矿物学可应用于采矿环境，以显着增加大量矿物学数据并定量绘制矿物学变异性。即使由于成分相似且无法准确预测粘土和碳酸盐，几种矿物的建模具有挑战性，这也是有用的。