Abstract
Modelling and analysis of helicopter electromagnetic data result in resistivity and susceptibility models and derivatives of magnetic data that characterise shallow parts of the Stillwater Complex, critical for aiding exploration and expansion of globally scarce critical and battery mineral resources that include platinum group elements, nickel, copper and chromium. The magnetic susceptibly models derived from the electromagnetic data and the tilt derivative of the magnetic data image layering, mafic dikes, banded iron formation, and serpentinised peridotite. Known areas with contact-type mineralisation are generally characterised by low resistivities and susceptibilities where the volume of mineralised rock is large and/or the depth is shallow. We use iso-cluster and edge detection analysis of both resistivities and susceptibilities to identify potential mineralisation in poorly characterised regions as well as faults. Low resistivity layers beneath large landslides reflect water saturated porous slip surfaces which can interfere with drilling. This uncommon approach of tightly linking the resistivity and susceptibility models and magnetic anomaly data to rock property, surficial geologic, drill hole and soil geochemistry data to image the geology in the upper ∼100 m, aids identification of prospective mineralised regions as well landslides and faults that can impact mineral exploration and local hazards.
Acknowledgements
We thank Stillwater Critical Minerals for providing the soil geochemistry data. We thank Chris Jenkins, Jean Legault and an anonymous reviewer for helpful reviews. This work was funded by the U.S. Geological Survey Mineral Resource Program. Any use of trade, firm, or product names is for descriptive purposes and does not imply endorsement by the U.S. Government. The authors report there are no competing interests to declare.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
All data used in the modelling is available at Parks et al. (Citation2019): https://www.sciencebase.gov/catalog/item/5ed8591382ce7e579c670254.