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Artificial intelligence for mining

Applying leading science, technology and innovation is critical to the competitiveness of Canada’s minerals industry. The mineral development sequence involves complicated processes, costly and energy-intensive machinery and equipment, and exploring extensive areas of land. Harnessing innovation enhances efficiency, lowers costs, increases productivity, and improves environmental performance. NRCan’s digital solutions will help drive clean, sustainable growth for Canada’s mining sector competitiveness by reducing costs and accelerating productivity through automation.

Project title Timeline Project description Contacts Sector
Groundwater information network 2009-Ongoing
  • distributed groundwater data network
  • uses AI to improve access to and integrate groundwater data from multiple sources
Boyan Brodaric
  • Lands and Minerals
Mapping Canada's water and infrastructure 2017-Ongoing
  • accelerate the ability to create geospatial data layers for use in emergency management, flood mapping and change detection
  • enhance Canada’s foundational mapping layers
Karen Bronsard
  • Canada Centre for Mapping and Earth Observation (CCMEO)
Lalor geochemical drillhole classification 2014-2018
  • provide support with the classification of geochemical data
  • predict 3-dimensional distribution of underground geological units

Ernst Schetselaar

Patrick Mercier-Langevin

  • Lands and Minerals
Detecting deep mineral deposits with deep learning for resource extraction 2020-2022
  • develop machine learning/deep learning algorithms to help determine the probability of presence of valuable mineral deposits and rock type
  • can scale and replicate outcomes across Canada
Gilles Bellefleur Lands and Minerals

View all our artificial intelligence projects

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