USGS Digital Spectral Library splib06a

Clark, R. N.; Swayze, Gregg A.; Wise, R.; Livo, Keith E.; Hoefen, Todd M.; Kokaly, Raymond F.; Sutley, S.J.

doi:10.3133/ds231

Cited by 686 publications

(596 citation statements)

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“…The origin of the dip around 2350 nm is less clear. It does not seem to be associated with any of the minerals listed in the spectral library of Clark et al (2007). It may be related to a combination of C-H asymmetric (2300-2320 nm) and symmetric (2340-2360 nm) stretch and bending (Workman and Weyer, 2008), emanating from organic compounds other than humified materials, which seem to lack absorptions in those regions (Serbin et al, 2009).…”

Section: Resultsmentioning

confidence: 94%

Accounting for surface roughness effects in the near-infrared reflectance sensing of soils

Jacobson

Laba

et al. 2009

Geoderma

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Section: Resultsmentioning

confidence: 94%

Accounting for surface roughness effects in the near-infrared reflectance sensing of soils

Jacobson

Laba

et al. 2009

Geoderma

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“…6b). Seven of eight kyanite mine waste samples (MT-2-MT-7) are flat, featureless spectra ranging from 3 to 7% reflectance, consistent with typical magnetite spectra in the near and shortwave infrared (700-2,500 nm) (Clark et al 2007;Grove et al 1992;Hunt et al 1971). For these samples, very slight increases in reflectance (*1%) in the visible region (400-700 nm) are likely caused by impurities in the waste stream mentioned above (e.g., kyanite, quartz, titanium dioxide) that contribute to a higher overall reflectance in the samples.…”

Section: Remote Sensingmentioning

confidence: 58%

An investigation of aspects of mine waste from a kyanite mine, Central Virginia, USA

et al. 2009

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Kyanite Mining Corporation, located in Dillwyn, Virginia has been in operation for over 50 years and their local operation is the largest kyanite mine in the world. As part of the processing at this location, a magnetic separate is generated and a minimum estimation of 3.57 million tons of waste has accumulated. Currently no use for the magnetic separate has been identified. We investigated eight representative samples of the magnetic mine waste which varied in color from a dark tan to black, to determine if the waste could be recycled as an ore or could be used as an environmental media. Mineralogical investigations indicate the composition of the magnetic mine waste is dominated by magnetite, kyanite, lesser amounts of hematite and charcoal. Magnetite occurs as fine grained crystals and as inclusions in kyanite. Hematite occurs largely as botryoidal textures, as discrete grains, and as coatings on kyanite grains. Fe-oxide spheres ranging in diameter from approximately 5-100 lm are common and may compose up to 10% in some samples. Titanium dioxide was rarely observed as coatings on silicate mineral grains. Energy dispersive spectroscopy analysis on magnetite crystals indicates they have end-member compositions. Bulk property investigations indicate that grain size distributions of samples are primarily unimodal with 20-40% of material being between 0.600 and 0.250 mm.Hydraulic conductivity values of samples investigated vary between 0.0036 and 0.0077 cm/s and are broadly consistent with those expected of sands with similar grain size distributions. In addition to the magnetic waste stream a light blue, water soluble, amorphous Cu sulfate occurs as a coating on surfaces of boulders. The coating is composed of rounded interlocking particles 5-60 lm in diameter. This material is of some environmental concern for freshwater invertebrates, but can be managed using sorption media. Hyperspectral data were gathered of the magnetic separate, kyanite ore samples, and the light blue Cu sulfate. The signatures of the kyanite ore, the blue mineral coating, and a mixture of the two provide spectral fingerprints that an imaging spectrometer could exploit for rapid detection and subsequent environmental monitoring.

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“…The halite spectrum was obtained from the USGS mineral spectral library (Clark et al 2007). MF and MTMF were used to derive halite mineral distribution maps.…”

Section: Methodsmentioning

confidence: 99%