Mapping Hydrocarbons and Rare Earth Elements at Various Scales through Imaging Spectroscopy



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Remote sensing techniques can play a critical role in geologic interpretation for mineral and energy exploration in unusual deposits. Hyperspectral cameras measure how a geophysical variable changes across wavelengths in the visible to short-wave infrared portion of the spectrum to map surface compositional variation. For two sites in this investigation, close-range ground-based imaging spectroscopy of hand samples with centimeter to sub-millimeter spatial resolution is used to establish preliminary information about the spatial distribution of natural resources within a geological formation. This information can then be compared with mineralogical maps of lateral rock exposures derived from airborne and spaceborne data. The first site is Fort McMurray in Alberta, Canada. The fluorescence characteristics of a bituminous sandstone depend on varying concentrations of light and heavy hydrocarbons, enabling imaging spectroscopy to distinguish zones of optimal yield for crude oil extraction. In the first chapter, multiple images of tar sand samples are collected under different wavelengths of ultraviolet illumination and normalized with respect to the fluorescence patterns of Spectralon diffuse reflectance material. Three classification methods are used to distinguish between bitumen, Spectralon, and a non-fluorescent slate background. Spectral indices useful for indicating concentrated bitumen in tar sands are proposed. The second site is the Sulfide Queen mine in Mountain Pass, California, which contains economic deposits of bastnäsite within a carbonatite body intruding a metamorphic host rock that have been mined for rare earth elements (REEs) critical to components in high technology devices. In the second chapter, a new spectral index based on reflectance measurements from hyperspectral data collected under visible illumination is created to map the relative REE abundances across three museum samples known to consist primarily of bastnäsite from the mine. In the third chapter, that index and its two new modified versions are applied to eleven ore samples of varying composition from known geolocations across the mine. To determine how changes in spectral patterns across different scales affect the perceived distribution of rare earth elements, these three indices are also applied to data collected by eight airborne and spaceborne imaging spectrometers over the Sulfide Queen mine and the nearby Ivanpah Dry Lake.



Imaging spectroscopy, spectral angle mapper classification, support vector machine classification, artificial neural network classification, spectral indices, hydrocarbons, fluorescence, rare earth elements, reflectance, geochemistry


Portions of this document appear in: O. C. A. Gadea and S. D. Khan, 2022, “Hyperspectral fluorescence imaging: robust detection of petroleum in porous sedimentary rock formations”, Interpretation, vol. 10, pp. SB1–SB15; and in: O. C. A. Gadea and S. D. Khan, 2023, “Detection of bastnäsite-rich veins in rare earth element ores through hyperspectral imaging”, IEEE Geosci. Remote. Sens. Lett., vol. 20, Art. no. 5502204.