Data- and knowledge-driven mineral prospectivity maps for Canada's North |
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Affiliation: | 1. State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China;2. Department of Earth and Oceans, James Cook University, Townsville, Queensland 4811, Australia;1. Faculty of Engineering, Malayer University, Malayer, Iran;2. Bedrock Geology and Resources, Geological Survey of Finland, Rovaniemi, Finland;1. X-plore Geoconsulting, 39 Morritt Close, Rockingham, WA 6168, Australia;2. Economic Geology Research Centre (EGRU), School of Earth & Environmental Science, James Cook University, Townsville, QLD 4811, Australia;3. ARC National Key Centre for Geochemical Evolution and Metallogeny of Continents (GEMOC), Department of Earth and Planetary Sciences, Macquarie University, North Ryde, NSW 2109, Australia;4. Kenex Ltd, PO Box 41136, Eastbourne, Wellington, New Zealand |
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Abstract: | Data- and knowledge-driven techniques are used to produce regional Au prospectivity maps of a portion of Melville Peninsula, Northern Canada using geophysical and geochemical data. These basic datasets typically exist for large portions of Canada's North and are suitable for a “greenfields” exploration programme. The data-driven method involves the use of the Random Forest (RF) supervised classifier, a relatively new technique that has recently been applied to mineral potential modelling while the knowledge-driven technique makes use of weighted-index overlay, commonly used in GIS spatial modelling studies. We use the location of known Au occurrences to train the RF classifier and calculate the signature of Au occurrences as a group from non-occurrences using the basic geoscience dataset. The RF classification outperformed the knowledge-based model with respect to prediction of the known Au occurrences. The geochemical data in general were more predictive of the known Au occurrences than the geophysical data. A data-driven approach such as RF for the production of regional Au prospectivity maps is recommended provided that a sufficient number of training areas (known Au occurrences) exist. |
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