Predictive models of mineralogy from whole-rock assay data: case study from Productora Cu-Au-Mo deposit, Chile
Escolme, AJ and Berry, RF and Hunt, J and Halley, S and Potma, W, Predictive models of mineralogy from whole-rock assay data: case study from Productora Cu-Au-Mo deposit, Chile, SEG 2019 Conference, 7-10 October 2019, Santiago, Chile, pp. 1. (2019) [Conference Extract]
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-AuMo 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. 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 in volcanic rocks, which closely matches the measured mineralogy.
In this example, calculated mineralogy using linear programming generated 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, lowmoderate 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.
Fig. 1. Comparison of mineralogy estimates by semiquantitative X-ray diffraction data corrected to assays by
weighted least squares (WLSQ; measured mineralogy) against calculated mineralogy by linear programming.
Regression line and R2
also shown. A) Quartz. B) K-feldspar. C) Plagioclase (including sodic and calcic
plagioclase). D) Pyrite. E) Chalcopyrite. F) Molybdenite. G) Total chlorite and micas (including chlorite,
muscovite, phengite and biotites). H) Total clay (including kaolinite and smectite-montmorillonite). I) Fe-oxides
(including magnetite, hematite, goethite). Major minerals, including quartz, feldspars, pyrite and Fe-oxides show
excellent correlation between measured (WLSQ) and calculated (linear programming).
calculated mineralogy, geometallurgy, linear programming, Productora, whole rock geochemistry, alteration, mineralogy