Big data techniques for real-time hyperspectral core logging and mineralogical upscaling

Hyperspectral imaging is gaining widespread use in the resource sector, with applications in mineral exploration, geometallurgy, and mine mapping. However, the sheer size of many hyperspectral datasets (>1 Tb), and associated data correction and analysis challenges, limit the integration of this technique into time-critical exploration and mining workflows. Here we present an overview of several real-time processing capabilities we have developed to mitigate these challenges, and so provide hyperspectral data and derived products (e.g., mineral abundance estimates) in near real-time. This allows for efficient, timely, and automated delivery of hyperspectral data to enhance geological activities.