MULTI-LABEL CLASSIFICATION FOR DRILL-CORE HYPERSPECTRAL MINERAL MAPPING

We developed a deep learning model that uses hyperspectral imaging data to predict key physical rock properties, specifically density, slowness, and gamma-ray values. Our model successfully learned to translate hyperspectral information into predicted physical properties. Tests on independent data gave accurate results, demonstrating the potential of hyperspectral data for mapping physical rock properties.

We apply multi-range hyperspectral imaging on drill core material from a Kupferschiefer-type Cu–Ag deposit in Germany, mapping minerals such as iron oxides, kaolinite, sulfate, and carbonates at millimetre resolution and in a rapid, cost-efficient, and continuous manner to track hydrothermal fluid flow paths and vectors towards base metal deposits in sedimentary basins.

Novel methods for mineral exploration play an important role in future resource exploration. The methods have to be environmentally friendly, socially accepted and cost effective by integrating multidisciplinary methodologies. We investigate the potential of passive, ambient noise tomography combined with 3D airborne electromagnetics for mineral exploration in Geyer, Germany. We show that the combination of the two geophysical data sets has promising potential for future mineral exploration.

We present a library of high-resolution laser-induced fluorescence (LiF) reference spectra using the Smithsonian rare earth phosphate standards for electron microprobe analysis. With the recurring interest in rare earth elements (REEs), LiF may provide a powerful tool for their rapid and accurate identification. Applications of the spectral LiF library to natural materials such as rocks could complement the spectroscopy-based toolkit for innovative, non-invasive exploration technologies.