XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 1523
to reduce uncertainty and risk in geometallurgical model-
ing. There are a variety of collection modalities for hyper-
spectral imaging from laboratory, ground-based (tripod),
unmanned aerial vehicles (UAV), airborne, and satellite.
Hyperspectral imaging occurs across a variety of wavelength
ranges in the electromagnetic spectrum. There are a variety
of hyperspectral approaches and some of these approaches
can be automated. Results from hyperspectral imaging can
be stand-alone products or integrated with other geological,
geochemical, geophysical, mineral processing, and metallur-
gical data for a combined (fused) product. The only ques-
tion is when will imaging spectroscopy (aka hyperspectral
imaging) be fully incorporated into the mining life-cycle
beyond limited adoption for mineral exploration using sat-
ellite and airborne data and/or core logging.
REFERENCES
Asadzadeh, S. and C. R. de Souza Filho (2016). “A review on
spectral processing methods for geological remote sens-
ing.” International Journal of Applied Earth Observation
and Geoinformation 47: 69–90.
Barton, I. F., M. J. Gabriel, J. Lyons-Baral, M. D. Barton, L.
Duplessis and C. Roberts (2021). “Extending geomet-
allurgy to the mine scale with hyperspectral imaging: a
pilot study using drone- and ground-based scanning.”
Mining, Metallurgy &Exploration 38(2): 799–818.
Boardman, J. W. and F. A. Kruse (1994). Automated spectral
analysis: A geologic example using AVIRIS data, north
Grapevine Mountains, Nevada. In Proceedings, Tenth
Thematic Conference on Geologic Remote Sensing,
Environmental Research Institute of Michigan, Ann
Arbor, MI.
Butcher, A. R., Q. Dehaine, A. H. Menzies and S. P.
Michaux (2023). Characterization of Ore Properities
for Geometallurgy. Elements. 19: 352–358.
Coulter, D. W., X. Zhou, L. M. Wickert and P. D. Harris
(2017). Advances in Spectral Geology and Remote
Sensing: 2008–2017. In “Proceedings of Exploration
17: Sixth Decennial International Conference on
Mineral Exploration,” Toronto, Canada.
Dehaine, Q., S. P. Michaux, J. Pokki, M. Kivinen and A.
R. Butcher (2020). “Battery minerals from Finland:
Improving the supply chain for the EU battery industry
using a geometallurgical approach.” European Geologist
Journal 5–11.
Dominy, S. C., L. O’Connor, A. Parbhakar-Fox and S.
Purevgerel (2018). “Geometallurgy -a route to more
resilient mine operations.” Minerals 8:560.
Eismann, M. T. (2012). Hyperspectral Remote Sensing.
Bellingham, Washington, SPIE Press.
Goetz, A. F. H., G. Vane, J. E. Solomon and B. N. Rock
(1985). “Imaging Spectrometry for Earth Remote
Sensing.” Science 228(4704): 1147–1153.
Figure 8. Automated hyperspectral data processing considerations for geometallurgy
to reduce uncertainty and risk in geometallurgical model-
ing. There are a variety of collection modalities for hyper-
spectral imaging from laboratory, ground-based (tripod),
unmanned aerial vehicles (UAV), airborne, and satellite.
Hyperspectral imaging occurs across a variety of wavelength
ranges in the electromagnetic spectrum. There are a variety
of hyperspectral approaches and some of these approaches
can be automated. Results from hyperspectral imaging can
be stand-alone products or integrated with other geological,
geochemical, geophysical, mineral processing, and metallur-
gical data for a combined (fused) product. The only ques-
tion is when will imaging spectroscopy (aka hyperspectral
imaging) be fully incorporated into the mining life-cycle
beyond limited adoption for mineral exploration using sat-
ellite and airborne data and/or core logging.
REFERENCES
Asadzadeh, S. and C. R. de Souza Filho (2016). “A review on
spectral processing methods for geological remote sens-
ing.” International Journal of Applied Earth Observation
and Geoinformation 47: 69–90.
Barton, I. F., M. J. Gabriel, J. Lyons-Baral, M. D. Barton, L.
Duplessis and C. Roberts (2021). “Extending geomet-
allurgy to the mine scale with hyperspectral imaging: a
pilot study using drone- and ground-based scanning.”
Mining, Metallurgy &Exploration 38(2): 799–818.
Boardman, J. W. and F. A. Kruse (1994). Automated spectral
analysis: A geologic example using AVIRIS data, north
Grapevine Mountains, Nevada. In Proceedings, Tenth
Thematic Conference on Geologic Remote Sensing,
Environmental Research Institute of Michigan, Ann
Arbor, MI.
Butcher, A. R., Q. Dehaine, A. H. Menzies and S. P.
Michaux (2023). Characterization of Ore Properities
for Geometallurgy. Elements. 19: 352–358.
Coulter, D. W., X. Zhou, L. M. Wickert and P. D. Harris
(2017). Advances in Spectral Geology and Remote
Sensing: 2008–2017. In “Proceedings of Exploration
17: Sixth Decennial International Conference on
Mineral Exploration,” Toronto, Canada.
Dehaine, Q., S. P. Michaux, J. Pokki, M. Kivinen and A.
R. Butcher (2020). “Battery minerals from Finland:
Improving the supply chain for the EU battery industry
using a geometallurgical approach.” European Geologist
Journal 5–11.
Dominy, S. C., L. O’Connor, A. Parbhakar-Fox and S.
Purevgerel (2018). “Geometallurgy -a route to more
resilient mine operations.” Minerals 8:560.
Eismann, M. T. (2012). Hyperspectral Remote Sensing.
Bellingham, Washington, SPIE Press.
Goetz, A. F. H., G. Vane, J. E. Solomon and B. N. Rock
(1985). “Imaging Spectrometry for Earth Remote
Sensing.” Science 228(4704): 1147–1153.
Figure 8. Automated hyperspectral data processing considerations for geometallurgy