Artificial intelligence: A tool for the future of mining

More than $379,000 in funding will support the development of a new deep learning approach for processing geophysical information.

QUÉBEC and MONTRÉAL, March 29, 2022 /CNW Telbec/ - Given the massive amounts of information that must be analyzed in the early stages of mining exploration, data interpretation is a considerable challenge for geologists. Professor Erwan Gloaguen of Institut national de la recherche scientifique (INRS) and his research partners decided to take developments in this field a step further by adapting existing deep learning architectures to interpret data from airborne imagery.

INRS Professor Erwan Gloaguen, specialist in geoscientific data assimilation. (credit : Christian Fleury) (CNW Group/Institut National de la recherche scientifique (INRS))

In studying vast, hard-to-access areas, geologist often have to interpret airborne geophysical data. Computer tools are invaluable for analyzing airborne images of these sites and generating a useful preliminary interpretation for geologists. The neural networks used in artificial intelligence have also proven highly effective for analyzing photographs or videos

The project, funded by Fonds de recherche du Québec – Nature et technologies (FRQNT), in partnership with the Ministère de l'Énergie et Ressources naturelles (MERN), under its second sustainable mining partnership research program, will analyze all airborne image data to predict the nature of the subsoil. The algorithms generated can be used as guides to target mineral exploration sites.

Parallel neural networks

This new approach to artificial intelligence aims to train parallel neural networks, each processing one type of variable, such as aeromagnetic or electromagnetic data. When processing is complete, a system will combine each network's predictions to generate a final prediction.

Professor Gloaguen's team is working with experts in artificial intelligence applied to mining for this network development project, the first phase of which is already underway. Team collaborators include Professors Bernard Giroux et Pierre-Simon Ross of INRS, Martin Blouin of Geolearn (an INRS startup), Jean-Philippe Paiement of Mira Geoscience, Guy Desharnais of Osisko Gold Royalties, and Antoine Caté of SRK. The team will use data from these partners and from SIGÉOM, Quebec's geomining information platform.

Postdoctoral researcher Mojtaba Bavand Savadkoohi, in the team of Professor Gloaguen, is currently working on a neural network that can apply the same resolution to all the data. "This will allow us to use satellite, aircraft, and helicopter images. Putting them in high resolution is essential, since the data isn't standardized," explains Professor Gloaguen.

In the second year of the project, the team will be able to build a deep network architecture and then teach it to recognize geophysical data, a step that will take several months. In the third year, the team will assess the potential for generalizing the algorithm. "We'll be testing the system to see if it can be generalized to all of Quebec or if it will need to undergo a learning phase for each geological region," says Professor Gloaguen.

About INRS

INRS is a university dedicated exclusively to graduate level research and training. Since its creation in 1969, INRS has played an active role in Québec's economic, social, and cultural development and is ranked first for research intensity in Québec. INRS is made up of four interdisciplinary research and training centres in Québec City, Montréal, Laval, and Varennes, with expertise in strategic sectors: Eau Terre Environnement, Énergie Matériaux Télécommunications, Urbanisation Culture Société, and Armand-Frappier Santé Biotechnologie. The INRS community includes more than 1,500 students, postdoctoral fellows, faculty members, and staff.

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SOURCE Institut National de la recherche scientifique (INRS)

Audrey-Maude Vézina, Service des communications et des affaires publiques de l'INRS, 418 254-2156, [email protected]