Application of data integration for deformation potential mapping using remotely acquired data sets within the Lynn Lake Greenstone Belt, northwestern Manitoba, Canada

Abstract

The purpose of this paper is to demonstrate the ability of combining remotely acquired data using data integration techniques to define lineament trends that may represent high-strain zones within the Lynn Lake Greenstone Belt. A knowledge-driven data integration approach has been adopted for this study that makes use of theoretical principles and good working knowledge of the regional geology to translate components of a shear-hosted gold conceptual model into a spatial model. Lineaments extracted from airborne vertical gradient magnetic data (VG), very low frequency electromagnetic data (VLF-EM), and RADARSAT synthetic aperture radar (SAR) data are used as evidence for the occurrence of shear zones. Proximity maps created from the extracted lineaments are weighted based on deformational characteristics of shear zones. Combination of these proximity maps, using fuzzy logic and Dempster-Shafer theory, produces a potential for deformation map. Field mapping was undertaken to verify the accuracy of the deformation potential maps generated. Detailed strike-perpendicular structural mapping in areas of above-average outcrop exposure identified a series of subparallel D2 shear zones, which correlated with areas of high deformation potential. Other potential areas of high deformation were field checked and found to represent D2 shear zones not identified during structural mapping. Deformation potential mapping proved invaluable in the interpretation of the western strike extension of the Johnson Shear Zone (JSZ) and demonstrates the broadening of the JSZ west of the Gemmel Lake area. The results from this paper have major implications for future gold exploration in the belt and have demonstrated that VG, VLF, and SAR data can be useful tools to assist in the delineation of major structural trends and high deformation zones within greenstone belts.