Additonal authors: Gabriel, Matthew J.. Book title: Proceedings of the 58th Conference of Metallurgists Hosting Copper 2019. Chapter: . Chapter title:
Process control, mine-to-mill optimization, and other mining operations depend on having robust, comprehensive mineralogical and chemical characterization. However, current characterization methods rely primarily on analyzing samples from trenches, channels, blast holes, and drill holes and interpolating between them, and are not always sensitive to some minerals (such as clays) that cause major problems for mining and mineral processing. Continuous characterization is possible with techniques such as hyperspectral remote sensing, which measures reflectance or absorption of light in the visible to near infrared (VNIR) and short-wave infrared (SWIR) regions of the electromagnetic spectrum. Absorption and reflectance features are characteristic of different chemical bond types, so each spectrum corresponds to a different mineral or material. In remote sensing, each pixel in the scanned area has both a location and a spectrum. This makes hyperspectral remote sensing potentially useful for characterizing mine site materials.
In this study, we tested a hyperspectral remote sensing system as a method of characterizing the mineralogy and material types at mine and metallurgical sites. Our test site was a small open-pit copper mine and leach operation in the southwestern U.S. We scanned mine highwalls, dump piles, leach pads, and nearby rock outcrops with drone- and tripod-mounted VNIR-SWIR spectrometers. We collected ground-truth data through geologic mapping, sampling, and spot analyses with a portable short-wave SWIR instrument.
The results show that the hyperspectral remote sensing system that we tested is capable of distinguishing the types and distribution of spectrally active minerals and other materials in diverse mining settings, including showing wet and dry areas of leach pads; clay and carbonate type and distribution on mine highwalls, leach pads, dumps, and rock outcrops; plant type and distribution; fault zones, bedding planes, and other geological features; and qualitative spectral matches between dump piles and areas of the mine highwalls. Hyperspectral sensing proved particularly sensitive to the presence and type of clay and carbonate minerals, which cause geotechnical and metallurgical problems such as highwall failure, gangue acid consumption, and flotation sliming. Examples will be presented and discussed at the conference.
Areas of continuing research include automating and streamlining the data reduction process, integrating hyperspectral data with block modeling and mine planning software, and testing the results of hyperspectral sensing in different geological environments.
