Geometallurgical assessment of orebodies in the mining industry typically relies on bench-scale or lab-based characterization techniques. In this study, we investigate drone- and tripod-based field hyperspectral imaging as a potential addition to the geometallurgy toolkit in multiple applications. This pilot study tests hyperspectral imaging for large-scale mineral mapping in and around the active Lisbon Valley copper mine, including natural exposures, previously producing U-V mines, highwalls, dumps, and leaching sites. Tests include different (supervised and unsupervised) mineral data classification methods, varying mineral spectral reference libraries, comparison with ground-truth geological and spectroscopic mapping and sampling, and integration with LiDAR data.
The results show that hyperspectral scans can produce spatially registered maps of the distribution of different spectrally active mineral types over dumps, highwalls, leach pads, and natural outcrops. Clays, other phyllosilicates, carbonates, and sulfates showed up particularly well. The sensor was also able to distinguish dry from lixiviant-saturated areas and map different clay types on the leach pads, and shows promise for differentiating types and health of vegetation. These results suggest that hyperspectral imaging, if coupled with robust ground-truthing, can be a useful complement to existing geometallurgical techniques in the mining industry, such as geological mapping, blast hole sampling and automated mineralogy identifications, and handheld spectrometry.
In particular, hyperspectral imaging has promise for mapping the distribution of acid-consuming minerals; mapping the distribution of swelling, sliming, and heap-blinding clays; and pinpointing problem areas on heap leach pad surfaces.
