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Predictive models of mineralogy from whole-rock assay data: Case study from the Productora Cu-Au-Mo deposit, Chile


Escolme, A and Berry, R and Hunt, J and Halley, S and Potma, W, Predictive models of mineralogy from whole-rock assay data: Case study from the Productora Cu-Au-Mo deposit, Chile, Economic Geology, 114, (8) pp. 1513-1542. ISSN 0361-0128 (2019) [Refereed Article]

Copyright Statement

2019 Society of Economic Geologists, Inc.

DOI: doi:10.5382/econgeo.2019.4650


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.

Item Details

Item Type:Refereed Article
Research Division:Earth Sciences
Research Group:Geology
Research Field:Resource geoscience
Objective Division:Mineral Resources (Excl. Energy Resources)
Objective Group:Primary mining and extraction of minerals
Objective Field:Mining and extraction of copper ores
UTAS Author:Escolme, A (Dr Angela Escolme)
UTAS Author:Berry, R (Associate Professor Ron Berry)
UTAS Author:Hunt, J (Dr Julie Hunt)
UTAS Author:Halley, S (Mr Scott Halley)
ID Code:137645
Year Published:2019
Web of Science® Times Cited:5
Deposited By:CODES ARC
Deposited On:2020-02-25
Last Modified:2020-07-30

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