Predictive Models of Mineralogy from Whole-Rock Assay Data: Case Study from the Productora Cu-Au-Mo Deposit, Chile

Author:

Escolme Angela12,Berry Ron F.12,Hunt Julie23,Halley Scott4,Potma Warren5

Affiliation:

1. Australian Research Council (ARC) Research Hub for Transforming the Mining Value Chain, University of Tasmania, Hobart, Tasmania 7001, Australia

2. Centre for Ore Deposits and Earth Sciences (CODES), University of Tasmania, Hobart, Tasmania 7001, Australia

3. Mineral Deposit Research Unit, Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada

4. Mineral Mapping Pty Ltd., 106 Joyce Street, Hawley Beach, Tasmania 7307, Australia

5. CSA Global Pty Ltd., Level 2, 3 Ord Street, West Perth, Western Australia 6005, Australia

Abstract

Abstract 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.

Publisher

Society of Economic Geologists

Subject

Economic Geology,Geochemistry and Petrology,Geology,Geophysics

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