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TitleComparison of the Data-Driven Random Forests Model and a Knowledge-Driven Method for Mineral Prospectivity Mapping: A Case Study for Gold Deposits Around the Huritz Group and Nueltin Suite, Nunavut, Canada
AuthorMcKay, G; Harris, J R
SourceNatural Resources Research vol. 25, issue 2, 2016 p. 125-143, https://doi.org/10.1007/s11053-015-9274-z
Year2016
Alt SeriesEarth Sciences Sector, Contribution Series 20160328
PublisherInternational Association for Mathematical Geosciences
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf
ProvinceNunavut
NTS65B; 65C; 65D
Lat/Long WENS-102.0000 -96.0000 61.0000 60.0000
Subjectsgold; prospecting; Hearne geologic province; random forest; mineral prospectivity; knowledge-drive tecniques
Illustrationslocation maps; geological sketch map; tables; flow charts; diagrams; graphs
ProgramSouth Rae Province Bedrock/Surficial geology, GEM2: Geo-mapping for Energy and Minerals
AbstractThis paper outlines the process taken to create two separate gold prospectivity maps. The first was created using a combination of several knowledge-driven (KD) techniques. The second was created using a relatively new classification method called random forests (RF). The purpose of this study was to examine the results of theRFtechnique and to compare the results to that of the KD model. The datasets used for the creation of evidence maps for the gold prospectivity mapping include a comprehensive lake sediment geochemical dataset, interpreted geological structures (form lines), mapped and interpreted faults, lithology, topographic features (lakes), and known Au occurrences. The RF method performed well in that the gold prospectivity map created was a better predictor of the knownAuoccurrences than theKDgold prospectivitymap. This was further validated by a fivefold repetition using a subset of the input training areas. Several advantages to the use of RF include (1) the ability to take both continuous and/or categorical data as variable inputs, (2) an internal, unbiased estimation of the mapping error (out-of-bag error) removing the need for a cross-validation of the final outputs to determine
accuracy, and (3) the estimation of importance of each input variable. Efficiency of prediction curves illustrates that the RF method performs better than the KD method. The success rate is significantly higher for the RF method than for the KD method.
Summary(Plain Language Summary, not published)
This paper compares two methods for producing a map that shows the potential for gold exploration in Northern Canada. We use geochemical, geophysical and geologic data to create the maps.
GEOSCAN ID299665