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TitleActivities under the mineral resource assessment component, GEM Minerals program
AuthorKerswill, J; Behnia, P; Harris, J; Chung, C; Franklin, J; Hillary, B; Bretzlaff, R
Source37th Annual Yellowknife Geoscience Forum, abstracts of talks and posters; by Jackson, V; Palmer, E; Northwest Territories Geoscience Office, Yellowknife Geoscience Forum Abstracts Volume vol. 2009, 2009 p. 77-78 Open Access logo Open Access
Alt SeriesEarth Sciences Sector, Contribution Series 20090445
Meeting37th Annual Yellowknife Geoscience Forum (2009); Yellowknife; CA; November 17-19, 2009
Subjectseconomic geology; mineral resource inventory
ProgramGEM: Geo-mapping for Energy and Minerals GEM Tri-Territorial Information management & Databases (Remote Predictive Mapping / Mineral Resource Assessment)
AbstractMineral resource assessment (MRA) is a component of the activities undertaken as part of the integrated Remote Predictive Mapping project of the GEM (Geo-mapping for Energy and Minerals) program of Natural Resources Canada. The principal long-term objectives of the MRA component are to develop viable quantitative methods for the identification of areas prospective for discovery of key deposit types across Canada's North and to provide reasonable estimates of the endowment of the commodities contained within the undiscovered deposits.
Recent work by Chung, Franklin and Hillary has resulted in the development and initial testing of a new knowledge-driven method for the identification of areas favourable for the discovery of VMS deposits. Exploration criteria or 'vectors to ore' for VMS were formulated from published regional scale bedrock maps and the new GIS-based approach was tested for the Hackett River belt and then for Slave Province as a whole. Exploration criteria are being identified for additional deposit types (gold, magmatic sulphide, diamonds, uranium, SEDEX, IOCG and porphyry systems). The new methodology will soon be tested for gold deposits.
Recent work by Kerswill, Behnia and Harris demonstrated that data-driven (weights of evidence or WofE) and knowledge-driven models identified similar areas as prospective for BIF-gold deposits in the North Rae Domain of Western Churchill Province, including several targets on Melville Peninsula. This work led to recognition of some data gaps which are being filled by the new geoscience investigations under GEM Minerals. These new data should improve the evidence layers used to generate the preliminary gold potential maps and thus facilitate better identification of prospective areas. Improved information is needed on the distribution of faults, iron formation, ultramafic rocks, felsic volcanic rocks and quartzite. Work is underway to determine if the best training set consists of all known occurrences (deposits, prospects and showings), or more restricted sets of deposits and prospects, or just deposits.
Recent work by Kerswill and Behnia indicates that both data-driven (WofE) and knowledge-driven models were successful in delineating areas prospective for discovery of IOCG deposits in the southern Great Bear Magmatic Zone (GBMZ). Models based solely on geological criteria defined relatively large targets compared to those generated using only geophysical data; as might be expected, the most useful prospectivity maps appear to be the result of including both geological and geophysical vectors to ore as evidence layers. Related work is being carried out under the IOCG-Great Bear Region project by Corriveau and coworkers.
Recent work by Bretzlaff has resulted in significant improvements to knowledge regarding the distribution and character of mineral occurrences in northern Melville Peninsula and in the GBMZ, two areas covered by projects under GEM Minerals. Such information is critical for the application of data-driven methods that depend upon a training set of known occurrences, and for the validation of knowledge-driven mineral potential maps that do not require a training set.

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