Discriminating ore and waste in a porphyry copper deposit using short-wavelength infrared (SWIR) hyperspectral imagery

A recent study by Dalm et al. (2014) showed that alteration mineralogy acquired using SWIR point spectrometry could be linked to copper grade distribution for a group of samples from a South American copper mine. Since it was expected that SWIR hyperspectral imagery can provide more detailed information about the alteration mineralogy of these ores, we investigated whether this technique can be used to improve upon the indirect characterization of copper grades. Maps showing the distributions of SWIR-active minerals, white mica crystallinity, white mica composition, and chlorite composition were produced from SWIR hyperspectral images of 43 samples from the Dalm et al. (2014) study. Subsequently, a principle component analysis (PCA) was applied to the relative mineral abundances and the average white mica crystallinity and composition that were extracted from these maps. The PCA showed that this mineralogical data could be used to discriminate a significant portion of the samples with sub-economic copper grades. Furthermore, the study showed that SWIR hyperspectral imaging has the following advantages over SWIR point spectrometry: minerals that are present in relatively low quantities can be detected, the SWIR-active mineralogical composition at the surface of a sample can be quantified, and the texture of samples, such as grain sizes and cross-cutting vein structures, can be characterized. However, these advantages did not improve upon the indirect characterization of copper grades that was achieved using SWIR point spectrometry. This was attributed to the relatively small size of the sample set and the high textural variability between samples.