Machine Learning for Exploration Targeting

Luis Montiel Petro - Vale

Talk Abstract:

With the development and availability of algorithms and tools for machine learning, there is an opportunity to integrate these technologies in current exploration efforts. Exploration geologists analyze large and diverse amount of geoscientific data towards identifying mineral prospective areas. There is an opportunity to combine current knowledge and understanding of the areas with machine learning techniques to enhance the targeting process. Besides capturing nonlinear patterns within the data, machine learning models allow integrating large amounts of data that can be difficult for professionals to process. Model results outline the capability of machine learning models to identify known deposits and prospective areas, confirming that those techniques are applicable as an integration method, and it is recommended their incorporation in routine exploration targeting workflows.

Speaker Bio:

Luis is a mining engineer from the Global Exploration team at Vale based in Toronto. Luis holds a bachelor’s degree in mining engineering from the Universidad Nacional de Colombia with a graduate formation in Open Pit Mining from the Paris School of Mines in France, a PhD in Mining Engineering from McGill University in Canada and a graduate formation in Artificial Intelligence and Machine Learning from the Emeritus Institute of Singapore. Luis has more than 10 years of experience in different areas of mining including resource modelling, mine planning and mine production in both open pit and underground projects and operations. Luis has led several initiatives implementing machine learning in different areas including mineral exploration.

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