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Highlights • Longwave IR spectra were collected from twelve REE-bearing samples. • Stretching and bending modes of the X-O (X = C, Si, P) groups are observed. • No specific features were found for the direct detection of the REEs. Abstract Rare earth elements (REEs) are a group of metals essential to high technology industries. This high demand, combined with a high supply risk, has led to an understanding that REEs are critical to society. Despite the potential that hyperspectral imaging (HSI) data offers for a fast and non-invasive characterization of the REEs, it is still poorly understood whether REEs have some information in the long-wave infrared (LWIR; 8–12 μm) wavelength range that can be used for their identification. To partially fill this gap, we have investigated the spectroscopy of twelve REE-bearing mineral samples using relatively high spatial and spectral resolution LWIR hyperspectral imaging data. These samples were formerly characterized using electron probe microanalysis (EPMA), scanning electron microscopy (SEM), and hyperspectral imaging data acquired in the 0.4–2.5 μm wavelength range. Results from these analyses were compared to and used to guide the analysis of the HSI data recorded in the LWIR range. This information was further compared to a reference spectral library of rare earth oxides. Our findings suggest that the spectral features of the samples can generally be traced to the asymmetric degenerate stretching and bending modes of the X-O (X = C, Si, P) groups. Moreover and contrary to what has been observed in the shorter wavelengths, there are no definitive spectral features in the LWIR wavelength region that could be assigned to any specific REE. [ABSTRACT FROM AUTHOR]

Natural samples of the rare earth element (REE)-bearing silicate minerals cerite, mosandrite, kainosite, zircon, and eudialyte were studied using reflectance spectroscopy in the visible to short-wave infrared regions (500 to 2500 nm) and further characterized by scanning electron microscopy and electron microprobe analysis. Spectral features of these minerals are driven primarily by 4f-4f intraconfigurational electronic transitions of trivalent lanthanides, as well as 5f-5f electronic transitions of uranium and vibrational overtones and combinations of H2O and OH–. Spectra of eudialyte are also impacted by relative amounts of IVFe2+ and VFe2+. Respective spectra of these REE-bearing silicate minerals are sufficiently distinct to enable spectral classification. Spectral variability (e.g., band depths and locations) of some specific REE-related absorptions, such as an Er3+- and Yb3+-related absorption near 978 nm and Nd3+-related absorptions near 746, 803, and 875 nm, are interpreted to be driven by cation site differences in the crystal structures. This work adds to the growing understanding of REE-bearing mineral reflectance spectroscopy, which facilitates detection, identification, and quantification of REE-bearing silicate minerals in remote sensing applications. This is especially relevant for hyperspectral imaging spectroscopy with high spatial resolutions where the spectral response of a pixel becomes increasingly dominated by mineralogy rather than lithology. [ABSTRACT FROM AUTHOR]

Reflectance spectroscopy in the visible to short-wave infrared regions (500 to 2500 nm) was carried out using natural samples of the rare earth element (REE) phosphate minerals monazite, xenotime, and britholite. Samples were characterized by scanning electron microscopy and electron microprobe analysis. Absorption band positions were recorded with their probable origins, and spectral variability among the samples is discussed. Spectral features of these minerals are driven primarily by 4f-4f intraconfigurational electronic transitions of trivalent lanthanides. The distinct REE distributions of monazite, xenotime, and britholite drive their bulk spectral patterns, which in turn are sufficiently distinct to enable spectral classification. Spectral variability of some specific REE-related absorptions are interpreted to be driven by differences of the coordination polyhedra for the lanthanide cations between the crystal structures. Spectra of these minerals were also compared against carbonatitehosted REE bearing fluorapatite. The work presented here strengthens the growing foundation for the interpretation of reflectance spectra of these REE phosphate minerals and enables exploitation of the observed features by the remote sensing community for detection, identification, and quantification of REE phosphate minerals. This is especially relevant for hyperspectral imaging spectroscopy with high spatial resolution, where the spectral response of a pixel becomes increasingly dominated by mineralogy. [ABSTRACT FROM AUTHOR]

This study is the first of two companion papers using hyperspectral data to generate predictive models of oil sand ore and froth characteristics as a potential new means for process control. In Alberta, Canada, shallow oil sands deposits are accessed by surface mining and crushed ore is transported to a processing plant for extraction of bitumen using flotation processes. The ore displays considerable variability in clay, bitumen, and fines which affects their behavior in flotation units. Using oil sand ore spanning a range of bitumen and fines characteristics, flotation experiments were performed to generate froth in a batch extractor to determine ore processability (e.g., separation performance) and froth characteristics (color, bitumen, solids). From hyperspectral observations of ore, models can predict the %bitumen content and %fines (particle passing at 44 and 3.9 µm) of ore but the models with highest r2 (>0.96) predict the solids/bitumen of froth and the processability of ore. Spectral observations collected on ore upstream of the separation vessels could therefore offer a first order assessment of froth quality for an ore blend before the ore enters the plant. These models could also potentially be used to monitor and control the performance of the blending process as another means to control the performance of the flotation process. [ABSTRACT FROM AUTHOR]

This is the second part of a study of predictive models of oil sand ore and froth characteristics using infrared hyperspectral data as a potential new means for process control. In Alberta, Canada, bitumen in shallow oil sands deposits is accessed by surface mining and then extracted from ore using flotation processes. The ore displays variability in the clay, bitumen, and fines content and this variability affects the separability and product quality in flotation units. Flotation experiments were performed on a set of ore samples of different types to generate froth and determine the ore processability (e.g., separation performance) and froth characteristics (bitumen and solids content, fines distribution). We show that point spectra and spectral imagery of good quality can be acquired rapidly (<1 s and <15 s, respectively) and these capture spectral features diagnostic of bitumen and solids. Ensuing models can predict the solids/bitumen (r2 = 0.88) and the %fines and ultrafines (particle passing at 3.9 and 0.5 µm) content of froth (r2 = 0.8 and 0.9, respectively). The latter model could be used to reject froth with a high solids content. Alternately, the strength of the illite-smectite absorption observed in froth could be used to retain all the samples above a pre-defined processability. Given that point spectrometers can currently be acquired for less than half the cost of an imaging system, we recommend the use of the former for future trials in operating environments. [ABSTRACT FROM AUTHOR]

The early identification of natural oil seepages or accidental leaks onshore is not a well explored theme in the literature. Location and mapping of contaminated areas can indicate the presence of a subsurface reservoir and guide direct exploration operations in the oil industry. Superficial occurrences of hydrocarbons (HCs) can also indicate damage to pipelines. Locating small leaks can guide containment, cleaning and repair operations. Remote sensing tools for such purposes usually focus on the effects that oil in the soil causes on the spectral signatures of vegetation. Studies investigating the spectral characteristics of soil and HC mixtures are not prevalent. This study comprises the conception and analysis of a spectral library of mixtures of several mineral substrates impregnated with various concentrations of light to heavy oils (API varying from 19.4 to 41.9), using data acquired with an automated hyperspectral imaging station and processed with wavelets. The wavelet transform allowed the extraction of key spectral features and the discard of secondary information (e.g. noise and continuum), resulting in hyperspectral imagery products that proved suitable to separate different phases in the soil-HC mixture, as well as to identify the type and concentration of HC in the soil. This comprehensive spectral library and the acquired information regarding spectral characteristics of soil-hydrocarbon mixtures, opens opportunities for the development of new processing methods for the direct mapping of onshore seeps and leaks using current and future orbital hyperspectral (Hyperion; EnMap; HyspIRI) and multispectral (WordView-3) sensors. [ABSTRACT FROM AUTHOR]