SP44: Chapter 1: Mineral Potential Mapping: Examples from the Red Lake Greenstone Belt, Northwest Ontario
by J.R. Harris and M. Sanborn-Barrie.
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This chapter is from GAC's Special Paper 44: "GIS For the Earth Sciences" editted by Dr. Jeff Harris. To purchase the entire book in hardcopy or disc formats, please see the Special Papers section of our bookstore.
ABSTRACT: Geographic Information Systems (GIS) are now commonly used in concert with various spatial modelling and statistical software packages by exploration companies to generate maps to assist in targeting various mineral commodities. A number of different modelling methods may contribute to the generation of such mineral potential maps. This paper provides a general review of the mineral potential mapping process using a GIS-based system and highlights a number of key issues that are pertinent to the production of prospectivity maps. These include the effect of using sparse vs. more extensive datasets, the choice of binary vs. continuous surface format for input data, the impact of utilizing different modelling algorithms including weights of evidence, weighted logistic regression, fuzzy logic and neural networks, and the consequences of using different populations of gold occurrences (e.g., mines vs. prospects vs. showings) as control populations ("training sets") for modelling. The impact of these issues on the GIS mineral potential mapping process is explored using data from the Red Lake greenstone belt of Ontario, Canada, one of Canada's most prolific gold districts.
The most reliable gold potential maps generated for the Red Lake greenstone belt were produced from datasets that included many evidence maps rather than just a few evidence layers, demonstrating that the use of sparse datasets is likely to adversely affect the degree to which the derived mineral potential maps identify prospective areas and predict known mineral deposits. The use of evidence maps in binary format resulted in gold potential maps that were slightly better predictors than maps produced using continuous surface evidence maps. However, the results vary depending on how thresholds (i.e., geochemical concentration, proximity to a contact, etc.) are determined when creating the binary map. The different data-driven modelling methods investigated in this study resulted in gold potential maps that are similar with respect to the areas identified as high potential. This indicates that the input data (evidence maps) used for a particular exploration model are probably more critical than the modelling method chosen to combine the data.
The potential maps derived for the Red Lake greenstone belt identified several new areas with geological and/or geochemical characteristics that are similar to the producing mines; these new areas represent prospective targets for further exploration.
Geological Association of Canada
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