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    Hyperspectral remote sensing for detecting geotechnical problems at ray mine

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    Author
    He, Jingping
    Barton, Isabel
    Affiliation
    Department of Mining and Geological Engineering, University of Arizona
    Issue Date
    2021-10
    Keywords
    Highwall movement
    Hyperspectral remote sensing
    Ray Mine
    Rock mass characterization
    Swelling clay
    
    Metadata
    Show full item record
    Publisher
    Elsevier BV
    Citation
    He, J., & Barton, I. (2021). Hyperspectral remote sensing for detecting geotechnical problems at ray mine. Engineering Geology, 292.
    Journal
    Engineering Geology
    Rights
    © 2021 Elsevier B.V. All rights reserved.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    While many or most geotechnical problems at open-pit mines are related to geological structures or discontinuities, highwall movement and failure can also occur as a consequence of nonstructural geological factors. Nonstructural causes of movement are not amenable to detection by conventional geotechnical sensing techniques such as LiDAR. In this case study, we applied hyperspectral remote sensing for large-scale mapping and detection of minerals at a non-structure-related ground instability in the highwalls of the Ray mine near Tucson, Arizona. The spectral images, obtained and integrated with radar images and the geological map, show that the dominant spectrally active mineral underlying the unstable area is the swelling clay montmorillonite, whereas kaolinite and white mica are more common in more stable parts of the highwall. The montmorillonite is concentrated in an outcropping altered diabase and conglomerate that underlie more competent rocks, providing a potential lift and slip surface. This study shows that hyperspectral remote sensing can aid in geotechnical slope characterization, particularly for nonstructural causes of failure. We provide a brief overview of best practices for hyperspectral remote sensing in geotechnical applications (combining drone- and tripod-mounted sensors, integrating hyperspectral with LiDAR and radar data, and using an iteratively refined spectral library based on site-specific sampling supported by ground truth).
    Note
    24 month embargo; available online 7 July 2021
    ISSN
    0013-7952
    DOI
    10.1016/j.enggeo.2021.106261
    Version
    Final accepted manuscript
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.enggeo.2021.106261
    Scopus Count
    Collections
    UA Faculty Publications

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