Ben Eaton, M.Sc. Student, University of British Columbia


Ben is an M.Sc. Candidate with the Mineral Deposits Research Unit at the University of British Columbia with four years of experience working in applied mineralogy in precious/base metals, geometallurgy, and environmental characterization with SGS and Barrick Gold. His research interest is the intersection between applied mineralogy, geomatics and data integration, combined with boots-on-the-ground geology. He aims to contribute to the minerals industry by advancing new methods that ultimately make the mining sector more effective, to contribute to discoveries under the cover of glacial deposits, and to be increasingly sustainable through the renewable energy transition.

Project: Deep Learning & Drift Prospecting: Convolutional neural network classification of Cu-porphyry indicator minerals by optical microscopy-microXRF-SEM in the Kemess District, BC, & implications for copper exploration in British Columbia.

As the global economy transitions towards decarbonization, infrastructure upgrades are predicted to increase the demand for copper by up to 350% worldwide by 2050. Continued copper-porphyry exploration in geopolitically stable regions is critical to meeting this rapid rise in demand. This project will develop new copper-porphyry indicator mineral methods tested on glacial deposit samples from the Northwest Copper East Niv Cu-Au-porphyry property in BC’s North Central Region. The project will utilize machine learning and convolutional neural network methods to identify copper-porphyry indicator minerals by optical microscopy, micro-X-ray fluorescence (microXRF), and scanning electron microscopy (SEM). The research will contribute to the development of industry-applicable, cost-effective, and rapid analytical technologies for copper exploration under cover.