Toronto Geological Discussion Group






 
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  • Automated Mineralogy: A quantum leap in drift exploration

Automated Mineralogy: A quantum leap in drift exploration

  • 06 Nov 2018
  • 4:00 PM - 6:00 PM
  • 2nd Floor, 20 Toronto St, Toronto, ON M5C 2B8
  • 77

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Canada is covered by glacial sediments, and these shall be considered not as a hindrance to exploration, but as a tool. Mineralogy of glacial sediments has long been recognized as a powerful method, enabling not only to detect subtle dispersion trains, but also to characterize the mineralized source. Successes of gold grain counting and kimberlitic indicator minerals are countless.
However, the method is surprisingly artisanal and did not evolved since 40 years, so easy discoveries were made and improvements are desperately needed. Various research groups currently work at defining a vast array of new indicator minerals for various type of mineralization. These methods were, until now, impaired by the incapacity to visually sort these minerals. The issue is currently circumvented by the use of automated mineralogy, a technology that was borrowed from geometallurgy as used in ore dressing. Mineral concentrates are scanned, grains by grains, by automated scanning electron microscope to detect the presence of chemically distinct indicator minerals.
The technology is complex, but its efficiency is mind-blowing. And it evolves so quickly that even fashionable QEMSCAN are now heading to museum! A series of red-hot case studies, using rocket-science techniques, will be presented.


Speaker Bios:

Réjean Girard: P.Geo and president of IOS, he graduated from Laval University a long time ago as a mad scientist. Involved in more than 1400 exploration projects so far, his broad experience enabled him to bring out-of-the-box solution to the industry.
Alexandre Néron: P.Eng, M.Sc, is the lead R&D scientist at IOS. He graduated from Université du Québec à Chicoutimi, but only after an incursion in electrical engineering at Sherbrooke University. Skilled in computer programming and machine learning, he is the one who takes the ideas and makes them working.


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