TY - JOUR
T1 - Hyperspectral remote sensing for detecting geotechnical problems at ray mine
AU - He, Jingping
AU - Barton, Isabel
N1 - Funding Information:
Moe Momayez (UA) contributed many helpful suggestions, and Mark Barton (UA) provided access to ASD equipment. Chad Williams (UA) collected, reduced, and provided radar data. We also appreciate funding support from Asarco LLC, the Lowell Institute for Mineral Resources , and the University of Arizona . Headwall Photonics provided equipment and expertise to acquire data sets and process raw data. Particular thanks to Carson Roberts, Will Rock, and Charlie Kepler (Headwall Photonics); and Sterling Cook and Jeff Cornoyer (Asarco) for their assistance.
Funding Information:
Moe Momayez (UA) contributed many helpful suggestions, and Mark Barton (UA) provided access to ASD equipment. Chad Williams (UA) collected, reduced, and provided radar data. We also appreciate funding support from Asarco LLC, the Lowell Institute for Mineral Resources, and the University of Arizona. Headwall Photonics provided equipment and expertise to acquire data sets and process raw data. Particular thanks to Carson Roberts, Will Rock, and Charlie Kepler (Headwall Photonics); and Sterling Cook and Jeff Cornoyer (Asarco) for their assistance.
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/10
Y1 - 2021/10
N2 - 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).
AB - 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).
KW - Highwall movement
KW - Hyperspectral remote sensing
KW - Ray Mine
KW - Rock mass characterization
KW - Swelling clay
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U2 - 10.1016/j.enggeo.2021.106261
DO - 10.1016/j.enggeo.2021.106261
M3 - Article
AN - SCOPUS:85109515496
VL - 292
JO - Engineering Geology
JF - Engineering Geology
SN - 0013-7952
M1 - 106261
ER -