Advances in mineral spectroscopy for rapid resource characterisation examples from South American base metal deposits
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1 Advances in mineral spectroscopy for rapid resource characterisation examples from South American base metal deposits Carsten Laukamp, Angus McFarlane, Victor Montenegro, Leandro Voisin June 2016 MINERAL RESOURCES
2 Challenges in mining operations Resource characterisation ore domains ore vs. waste Deleterious mineral phases Impact on grinding circuit wollastonite (skarn deposits) Impact on flotation and froth stability chlorite, mixed interlayer chl/ill and ill/sm (e.g. IOCG, porphyry Cu), Al-clays (e.g. CIDs) Mapping of geometallurgical parameters (e.g. hardness) 11. Identifying ore domains on the base of key mineral species and/or gangue mineral assemblages 22. Quantification of key mineral species and deleterious phases 33. Modelling fracture index Product 2 AESC 2016 Laukamp et al. Mineral spectroscopy for resource characterisation
3 Mineral Spectroscopy - gangue mineralogy epidote chlorite /dickite + illite montmorillonite halloysite kaolinite alunite proximal distal Quartz-Alunite Epithermal systems, modified after Tosdal et al. (2009 ) USGS Spectral Library 3 AESC 2016 Laukamp et al. Mineral spectroscopy for resource characterisation
4 1 - Resource characterisation - oxide zone Cu/Cl ratio! Radomiro Tomic Porphyry Cu Deposit, Chile Cu 2+ 2(OH) 3 Cl Patricio Cuadra & Gonzalo Rojas (2001) Laukamp et al. (2015) 4 AESC 2016 Laukamp et al. Mineral spectroscopy for resource characterisation
5 1 - Resource characterisation - Antamina Cu-Zn-Mo, Peru Quartz monzonite porphyry intruded Upper Cretaceous sedimentary rocks of the Jumasha and Celendin Formations, Skarn alteration along contact and crosscut by later stage hydrothermal breccias. Mineralisation: mainly, Mo ± Ag and Bi, majority of Cu-Zn ore present in exoskarn and, majority of Cu-Mo ore in endoskarn Love et al. (2004) 5 AESC 2016 Laukamp et al. Mineral spectroscopy for resource characterisation
6 1 - Antamina Methods: HyLogger data acquired from drill core & blast hole samples XRD EPMA Infrared microscopy Company provided: QEMSCAN Whole rock geochem 6 AESC 2016 Laukamp et al. Mineral spectroscopy for resource characterisation
7 1 - Antamina AlFe Fe 2+ Cr -1 grossular andradite uvarovite? Can ore variations of base metal ratios across endo- and exoskarns mapped on the base of compositional changes of gangue mineralogy? a) Ca 3 Al 2 (SiO 4 ) 3 Ca 3 (Fe 3+ ) 2 (SiO 4 ) 3 Ca 3 Cr 2 (SiO 4 ) 3 Cu [ppm] b) Zn [ppm] Variations of base metal ratios across endo- and exoskarns associated with changes in gangue mineralogy (that is mineral assemblages and compositional changes, for example, in garnets) c) Zn + Cu ± Mo high Cu ± Zn, Mo Mo [ppm] d) Zn + Cu ± Mo high Cu ± Zn, Mo MT-code Mo ± Zn ± Cu Mo ± Zn ± Cu high Mo ± high Mo ± 7 AESC 2016 Laukamp et al. Mineral spectroscopy for resource characterisation
8 1 - Antamina AlFe Fe 2+ Cr -1 grossular andradite uvarovite? Can ore variations of base metal ratios across endo- and exoskarns mapped on the base of compositional changes of gangue mineralogy? a) Ca 3 Al 2 (SiO 4 ) 3 Ca 3 (Fe 3+ ) 2 (SiO 4 ) 3 Ca 3 Cr 2 (SiO 4 ) 3 Cu [ppm] b) Zn [ppm] Variations of base metal ratios across endo- and exoskarns associated with changes in gangue mineralogy (that is mineral assemblages and compositional changes, for example, in garnets) c) Zn + Cu ± Mo high Cu ± Zn, Mo Mo [ppm] d) Zn + Cu ± Mo high Cu ± Zn, Mo MT-code Quality of results dependant on sample material (i.e. core vs. chips)? Mo ± Zn ± Cu Mo ± Zn ± Cu Dependant on target mineral, though only negligible impact on key minerals (e.g. garnet) high Mo ± high Mo ± 8 AESC 2016 Laukamp et al. Mineral spectroscopy for resource characterisation
9 1 - Antamina a) b) 305: And 91 Grs 9 306: And 98 Grs 2 c) grossular AlFe andradite Ca 3 Al 2 (SiO 4 ) 3 Ca 3 (Fe 3+ ) 2 (SiO 4 ) 3 307: And 98 Grs 2 308: And 89 Grs : And 83 Grs 17 B C D 310: And 88 Grs : And 73 Grs : And 73 Grs : And 73 Grs 27 T 9 AESC 2016 Laukamp et al. Mineral spectroscopy for resource characterisation
10 2 - Mineral quantification - Antamina PLS modelling using XRD and QEMSCAN for calibration TIR for modelling modal abundances of anhydrous silicates, carbonates and phosphates SWIR for modelling modal abundances of hydroxylated silicates and sulphates 10 AESC 2016 Laukamp et al. Mineral spectroscopy for resource characterisation
11 3 - PLS modelling of fracture index Final regression coefficient of each input band. Arrows point to high positive FRCs that can be related to spectral features of respective minerals; e) f) Predicted vs. observed fracture index TIR spectral signatures of highest data density volume of samples with low fracture index. Calcite, vesuvianite and garnet represent typical minerals of samples with low fracture index; TIR spectral signatures of highest data density volume of samples with high fracture index (3). Quartz and garnet represent major mineral phases in samples with high fracture index. 11 AESC 2016 Laukamp et al. Mineral spectroscopy for resource characterisation
12 Mineral Spectroscopy can be used to Identifying ore domains on the base of key mineral species and/or gangue mineral assemblages 22. Quantification of key mineral species and deleterious phases 33. Modelling fracture index easily map gangue mineralogy and, in cases, ore! Hyperspectral data provide additional information about gangue mineralogy (e.g. mineral composition) that normally requires detailed & time consuming work allows comprehensive comparison of gangue mineralogy across various deposits, improving our understanding of deposit type variability quantify key mineral & deleterious minerals model geometallurgical parameters (e.g. fracture index, hardness) & identify controlling mineral phases 12 AESC 2016 Laukamp et al. Mineral spectroscopy for resource characterisation
13 Thank you CSIRO Mineral Resources Carsten Laukamp Senior Research Geoscientist t e carsten.laukamp@csiro.au MINERAL RESOURCES
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