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Grandite-based resource characterization of the skarn-hosted Cu-Zn-Mo deposit of Antamina, Peru

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Abstract

Field and drill core hyperspectral sensing technologies are cost-effective tools for identifying footprints of hydrothermal mineral systems, vectoring towards potential ore bodies and differentiating domains in ore deposits. Hyperspectral visible-near, shortwave and thermal infrared data were collected from drill core and chip samples from the Cu-Zn-Mo deposit of Antamina, Peru, to map the relative abundance and chemical composition of major gangue minerals, such as garnet and quartz. This study focuses on grandite-series garnets (i.e. grossular to andradite), which show distinct variations with regard to their relative abundance and chemical composition across the Antamina deposit. Reflectance spectra were calibrated with quantitative mineralogy derived from scanning electron microscopy and X-ray diffraction-derived mineralogy. This allowed the modal abundances of garnet and quartz to be modelled using a partial least squares approach, achieving regression coefficients of up to 0.887 and 0.891, respectively. The accuracy of the modelled modal mineral abundance depended on the method of external calibration, and the mineral assemblage associated with the mineral of interest. Comparing the hyperspectral data with major sulfide mineralogy, whole-rock geochemistry, electron probe microanalysis and Fourier-transform infrared microscopy allowed the identification of five ore clusters at Antamina, including high Cu ± Zn-Mo; Zn + Cu ± Mo and Zn ± Cu in the exoskarn and Mo ± Cu-Zn and high Mo ± Cu-Zn in the endoskarn. The garnet species comprise mainly grossular ± andradite in the endoskarn and andradite in the exoskarn. Different garnet compositions are associated with different sulfide mineral assemblages, making Antamina an ideal candidate for evaluating hyperspectral sensing for rapid and reliable mineral characterisation of skarn deposits. Vector minerals typical of skarn deposits (e.g. garnet, vesuvianite) were determined using this approach, indicating its potential for effectively characterising skarn deposits worldwide.

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Acknowledgements

Lew Whitbourn, Zoe Willie, Warren Wright and Marc Jolivet are sincerely thanked for their great work with scanning the drill cores samples using the HyLogger-3 at CSIRO North Ryde. The authors of this research work acknowledge the AMTC-CSIRO Chile International Centre of Excellence, financed by CORFO Chile (INNOVA CSIRO-CHILE 10CEII-9007), and the Universidad de Chile, where laboratory work was conducted. The authors would like to thank Michael Verrall for help with QXRD interpretation. Peter Mason is thanked for fruitful discussions about the application of the PLS method to the HyLogger-3 data. Reviewers Rob Hewson and Heta Lampinen as well as editors Steffen Hagemann and Bern Lehmann are thanked for constructive reviews and feedback, which helped to improve the manuscript. Tom Cudahy and Ian Lau are thanked for reviews of earlier versions of this contribution.

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Authors and Affiliations

  1. CSIRO Mineral Resources, 26 Dick Perry Avenue, Kensington, WA, 6151, Australia

    Carsten Laukamp, Monica LeGras & Angus McFarlane

  2. Centre of Excellence for Mining and Mineral Processing, CSIRO Chile, Santiago, Chile

    Victor Montenegro & Angus McFarlane

  3. Compañia Minera Antamina S.A., Lima, Peru

    Steve Windle

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  1. Carsten Laukamp
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  2. Monica LeGras
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  3. Victor Montenegro
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  4. Steve Windle
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  5. Angus McFarlane
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Correspondence to Carsten Laukamp.

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Editorial handling: S. Hagemann

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Laukamp, C., LeGras, M., Montenegro, V. et al. Grandite-based resource characterization of the skarn-hosted Cu-Zn-Mo deposit of Antamina, Peru. Miner Deposita 57, 107–128 (2022). https://doi.org/10.1007/s00126-021-01047-2

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