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TitleData- and knowledge-driven mineral prospectivity maps for Canada's North
AuthorHarris, J R; Grunsky, E; Behnia, P; Corrigan, D
SourceOre Geology Reviews vol. 71, 2015 p. 788-803, https://doi.org/10.1016/j.oregeorev.2015.01.004
Year2015
Alt SeriesEarth Sciences Sector, Contribution Series 20140290
PublisherElsevier
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf
ProvinceNunavut
NTS46N; 46O; 47A/03; 47A/04; 47A/05; 47A/06; 47A/11; 47A/12; 47A/13; 47A/14; 47B/01; 47B/02; 47B/07; 47B/08; 47B/09; 47B/10; 47B/15; 47B/16
AreaMelville Peninsula
Lat/Long WENS -85.0000 -83.0000 69.0000 67.0000
Subjectsprospecting; prospecting techniques; gold; mapping techniques; modelling; Random Forest (RF) supervised classifier
Illustrationsgraphs; flow charts; location maps; geological sketch maps; magnetic maps
ProgramMackenzie Corridor Project Management, GEM2: Geo-mapping for Energy and Minerals
AbstractData- 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.
Summary(Plain Language Summary, not published)
This paper presents a new method for producing a map showing areas of higher potential for mineral exploration. It uses a classification method in which known gold occurrences are used to predict areas of higher potential for gold exploration based on various geoscience data types. This can be used in the mineral potential assessment of areas that are being considered for Federal park expansion or the siting of new parks.
GEOSCAN ID295504