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

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Date modified:: 2022-09-07

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Artificial intelligence for mining

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