Statistical analysis of deep structural features in the Superior Province and their relationship to orogenic Au mineralisation
Abstract
Orogenic gold deposits are linked to geological structures at different crustal or even lithospheric levels that act as conduits for hydrothermal fluids. Sub-Crustal Lithospheric Mantle (SCLM) may act as fluid conduits from the upper mantle. The western Superior Province in Canada hosts world class Au deposits such as the Red Lake camp. In this study, deep regional structures at high angles to general mapped lithological and structural trends identified from enhanced aeromagnetic, pseudogravity and ground gravity data are integrated with geological data. Machine learning algorithms (notably the Random Forest algorithm) were trained using a compilation of the geophysical and geological datasets and engineered datasets from classical spatial analysis techniques (viz. buffer analysis, fry analysis, and fractal analysis) to produce a statistical model that defines the spatial relationship between the regional shear structures and orogenic gold mineralisation. The future goal of the project is to produce a prospectivity model for the western Superior based on results from the Red Lake area to both validate using known deposits and to identify other prospective areas for Au mineralisation elsewhere. This project was funded through NRCan's TGI mineral exploration program "Increasing Deep Exploration Effectiveness" and NSERC.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMIN41D0884A
- Keywords:
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- 0555 Neural networks;
- fuzzy logic;
- machine learning;
- COMPUTATIONAL GEOPHYSICS;
- 1849 Numerical approximations and analysis;
- HYDROLOGY;
- 1910 Data assimilation;
- integration and fusion;
- INFORMATICS;
- 1942 Machine learning;
- INFORMATICS