Hyperspectral remote sensing for detecting geotechnical problems at ray mine
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Final Accepted Manuscript
Affiliation
Department of Mining and Geological Engineering, University of ArizonaIssue Date
2021-10Keywords
Highwall movementHyperspectral remote sensing
Ray Mine
Rock mass characterization
Swelling clay
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Elsevier BVCitation
He, J., & Barton, I. (2021). Hyperspectral remote sensing for detecting geotechnical problems at ray mine. Engineering Geology, 292.Journal
Engineering GeologyRights
© 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 2021ISSN
0013-7952Version
Final accepted manuscriptae974a485f413a2113503eed53cd6c53
10.1016/j.enggeo.2021.106261
