Enhanced digital outcrop models attributed with hyperspectral reflectance data, or hyperclouds, provide a flexible, three-dimensional medium for data-driven mapping of geological exposures, mine faces or cliffs. This approach facilitates the collection of spatially contiguous information on exposed mineralogy, and so helps to quantify mineralising processes, interpret 1-D drillhole data, and optimise mineral extraction. Hyperclouds can be analysed using a variety of techniques to accurately map geological objects from a distance. Reference spectra from spectral libraries, ground or laboratory measurements can also be included to derive supervised classifications using machine learning techniques.
We demonstrate the potential of the hypercloud approach by integrating hyperspectral data from laboratory, tripod and unmanned aerial vehicle acquisitions to automatically map relevant lithologies and alterations associated with volcanic hosted massive sulphide mineralisation in the Corta Atalaya open-pit, Spain. These analyses allow quantitative and objective mineral mapping at the outcrop and open-pit scale, facilitating quantitative research and smart-mining approaches. We also show that random forests trained only on laboratory data from labelled hand-samples can be used to map appropriately corrected outcrop scale data.
More details can be found here: https://doi.org/10.1016/j.oregeorev.2021.104252