Multi-element soil geochemical data was analysed using neural network software for a client undertaking exploration for precious metals. Significant elemental associations known to typify certain mineralisation styles were identified and areas of relative prospectivity were delineated. The resulting data were post-processed using industry standard GIS software and imagery suitable for both internal and external stakeholder interest was generated.
The study successfully delineated the aerial extent and relative thickness of a sequence of sub-horizontally layered sediments and volcaniclastics targeted by an international mining company for its mineral endowment, across a substantial area using remotely sensed imagery. As the features were captured in 3D while being view stereoscopically using a digital photogrammetric workstation, 3D geological models were constructed from which approximate unit and overburden thickness metrics were able to be estimated. The study also provided a series of maps showing relative prospectivity and numerous targets were highlighted for follow up investigation.