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TitleData integration study for mineral potential mapping in northeastern Alberta
DownloadDownload (whole publication)
 
LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorChung, C J; Rencz, A N; Zhang, A
SourceExploring for minerals in Alberta: Geological Survey of Canada geoscience contributions, Canada-Alberta Agreement on Mineral Development (1992-1995); by Macqueen, R W (ed.); Geological Survey of Canada, Bulletin 500, 1997 p. 155-161, https://doi.org/10.4095/209211 Open Access logo Open Access
Year1997
PublisherNatural Resources Canada
Documentserial
Lang.English
Mediapaper; on-line; digital
RelatedThis publication is contained in Exploring for minerals in Alberta: Geological Survey of Canada geoscience contributions, Canada-Alberta Agreement on Mineral Development (1992-1995)
File formatpdf
ProvinceAlberta
NTS74M/09; 74M/10; 74M/15; 74M/16
Lat/Long WENS-111.0000 -110.0000 60.0000 59.5000
Subjectsmathematical and computational geology; geophysics; geochemical surveys; remote sensing; mineral occurrences; lithology; geophysical surveys; v l f surveys; magnetic surveys; gamma-ray surveys; exploration techniques; modelling; gold; mineralization; geographic information system applications; Precambrian
Illustrationssketch maps
ProgramCanada-Alberta Agreement on Mineral Development, 1992-1995
Released1997 10 01
AbstractA preliminary data integration study was conducted for a part of the Canadian Shield in northeastern Alberta to produce mineral potential maps. Digital data sets of geology, geophysics, geochemistry, mineral occurrences and remote sensing information were compiled and registered to a common coordinate system, thus forming a number of discrete layers of digital data. Several modelling procedures were developed to integrate the data to produce mineral potential maps. One of these procedures is the logistic discriminant function model. The application of this model involves using statistical correlations among data layers and gold occurrences at the locations of known gold occurrences in the study area, to predict the potential for unknown gold occurrences elsewhere in the study area. A training area was used to construct a statistical relationship among 27 of the 36 known gold occurrences in the area and the 14 layers of input data. The statistical relationship derived from the training area was then applied to the entire area. Application of the discriminant function model over the entire area successfully predicts the occurrence of more than 75% of the known gold occurrences in the area.
GEOSCAN ID209211

 
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