Geographic Information Systems (GIS) used in concert with statistical and geostatistical software provide the geologist with a powerful tool for processing, visualizing and analysing geoscience data for mineral exploration applications. This thesis focuses on different methods for analysing, visualizing and integrating geochemical data sampled from various media (rock, till, soil, humus), with other types of geoscience data. Different methods for defining geochemical anomalies and separating geochemical anomalies due to mineralization from other lithologic or surficial factors (i.e. true from false anomalies) are investigated. With respect to lithogeochemical data, this includes methods to distinguish between altered and un-altered samples, methods (normalization) for identifying lithologic from mineralization effects, and various statistical and visual methods for identifying anomalous geochemical concentrations from background. With respect to surficial geochemical data, methods for identifying bedrock signatures, and scavenging effects are presented. In addition, a new algorithm, the dispersal train identification algorithm (DTIA), is presented which broadly helps to identify and characterize anisotropies in till data due to glacial dispersion and more specifically identifies potential dispersal trains using a number of statistical parameters. The issue of interpolation of geochemical data is addressed and methods for determining whether geochemical data should or should not be interpolated are presented. New methods for visualizing geochemical data using red-green-blue (RGB) ternary displays are illustrated. Finally data techniques for integrating geochemical data with other geoscience data to produce mineral prospectivity maps are demonstrated. Both data and knowledge-driven GIS modeling methodologies are used (and compared) for producing prospectivity maps. New ways of preparing geochemical data for input to modeling are demonstrated with the aim of getting the most out of your data for mineral exploration purposes. Processing geochemical data by sub-populations, either by geographic unit (i.e., lithology) or by geochemical classification and alteration style was useful for better identification of geochemical anomalies, with respect to background, and for assessing varying alteration styles. Normal probability plots of geochemical concentrations based on spatial (lithologic) divisions and Principal Component Analysis (PCA) were found to be particularly useful for identifying geochemical anomalies and for identifying associations between major oxide elements that in turn reflect different alteration styles. (Abstract shortened by UMI.)
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/6421 |
| Date | January 2002 |
| Creators | Harris, Jeff R. |
| Contributors | Bonham-Carter, Grame, |
| Publisher | University of Ottawa (Canada) |
| Source Sets | Université d’Ottawa |
| Detected Language | English |
| Type | Thesis |
| Format | 355 p. |
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