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TitleProspectivity mapping for gold deposits hosted by iron formation, in a portion of western Churchill Province that includes Melville Peninsula, Nunavut, Canada
AuthorBehnia, P; Kerswill, J; Bonham-Carter, G; Harris, J
SourceProceedings of the 17th International Conference on Geoinformatics; by IEEE; 2009 p. 1-6, https://doi.org/10.1109/GEOINFORMATICS.2009.5293437
Year2009
Alt SeriesEarth Sciences Sector, Contribution Series 20090467
PublisherIEEE
MeetingThe 17th International Conference on Geoinformatics; Fairfax, Virginia; US; August 12-14, 2009
Documentbook
Lang.English
Mediapaper; on-line; digital
File formatpdf
ProvinceNunavut
NTS46J; 46K; 46L; 46M; 46N; 46O; 46P; 47A; 47B; 47C; 47D
AreaMelville Peninsula
Lat/Long WENS-88.0000 -80.0000 70.0000 66.0000
Subjectsmiscellaneous; mathematical and computational geology; statistical methods; analytical methods; gold; mineralization; iron formations; iron; modelling
ProgramGEM Tri-Territorial Information management & Databases (Remote Predictive Mapping / Mineral Resource Assessment), GEM: Geo-mapping for Energy and Minerals
AbstractData-driven models employing weights of evidence (WofE) and logistic regression (LR) have been used in a series of experiments to characterize the spatial association of iron formation-hosted gold with geological and geophysical features in the Meadowbank to Melville Peninsula corridor of the northern Rae Domain. A number of geological features were extracted from a 1:550,000 bedrock compilation map and used as evidence. A total of 52 BIF-hosted gold occurrences were available for use in the training sets. For each experiment, weights were determined for individual evidence layers based upon the selected training sites. The evidence maps were reclassified into binary or ternary (2- or 3-class) maps guided by contrast values and associated studentized contrast values calculated on the ordered data. The evidence layers were combined using WofE and LR models to create posterior probability target maps for BIF-hosted gold deposits. To evaluate the validity of the mineral potential maps, training and testing sets were used to assess efficiency of classification and prediction of potential maps respectively.
GEOSCAN ID262719