Dr. Feltrin has recently joined HES at Laurentian University (January 2018) as Associate Professor of Earth Systems Modelling. His research considers the use of computational geoscience, AI and modern data analytics to increase the chance of discovery of ore deposits and improve the understanding of factors controlling mineral resources location in the Earth's crust. Applied research focuses on GIS-based mineral prospectivity mapping, data analytics, robotics and Earth systems 3D reconstructions and simulations. Starting in Winter 2018, he will be teaching the course GEOL 3056 titled Computer Applications in the Earth Sciences.
Research activity is focused on the use of computation and statistics to enhance and automate the mineral deposit discovery process. Most of Dr. Feltrin experience derives from multidisciplinary research and industry projects conducted in Canada and Australia, focusing dominantly on the study of base and precious metal endowed hydrothermal systems. Developing computational models and simulations of ore forming processes and interpreting their significance and applicability is challenging. The science we apply is therefore experimental in nature with the objective of providing innovative decision support strategies. Recent studies focused on the application of association rule learning, cluster analysis, and Bayesian probabilistic models. These numeric strategies efficiently integrate information and allow construction of predictive models, to identify previously unrecognized mineral deposits, or extensions of known ore bodies. Other work has rather focused on descriptive analytics of the footprints of known ore deposits. These studies are based on data mining of multivariate association statistics of geological information, with the objective of proposing new workflows for their systematic mapping in 3D as “digital footprints”. An overarching objective of the research effort is to generate workflows that facilitate the informatization process of mineral exploration data. These tools will find applicability in three connected areas of research that each inform wider questions concerning ore deposits: (1) geochemical and geophysical ore deposit mapping and simulation in 3D, to visualize geoscience information; (2) multivariate analysis to integrate geoscience information, and (3) development of Artificial Intelligence (AI) tools and their application to mineral exploration with emphasis on pattern recognition analysis to facilitate data interpretation.