Machine Learning for Prospectivity Mapping: Myths, Legends and Facts

Dr. Antoine Caté, Senior Consultant, SRK Consulting (Canada) Inc.

Speaker Biography:

Dr. Antoine Caté is a structural geology consultant at SRK Toronto. He has 8 years of both academia and industry field experience working on gold and base metal deposits. He is an expert in the applications of data science and machine learning in geosciences, including for prospectivity analysis. He was the leader of the team awarded the second place at the Integra Gold Rush challenge in 2016 and was involved in the founding of two companies applying machine learning in the mining industry. Antoine’s current work at SRK includes structural field investigation, 3D modelling, and the development of innovative tools applied to structural geology, and mineral exploration.

Talk Abstract:

Machine learning has been a hot topic in the mining industry in recent years. One of the most discussed applications of these advanced data-driven algorithms is in the integration of exploration data to generate prospectivity maps and exploration targets. This application has both proponents and opponents. Some see it as a revolution in the industry while others consider it as a useless shiny tool. As always, the reality is somewhere in between. This presentation aims at presenting what prospectivity mapping through machine learning is, compare it with other modern methods, and highlight the advantages and limits of this approach. In addition, a series of short examples will be used to demonstrate that machine learning is not limited to prospectivity modelling and that there are other applications that are similarly valuable to support target generation.

Live Online Broadcast:

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