Abstract
Sentinel-1 data on the kinematics of the 2017 Xinmo landslide and its surrounds are studied to understand the precursory failure dynamics of a large region with a historical predisposition to landslides. We perform a systematic spatiotemporal analysis over a period of two years to identify high-risk regions and discriminate between their precursory failure dynamics. We found the 2017 Xinmo landslide source to exhibit a unique kinematic signature which can be distinguished, almost a year in advance, from those of other sites of instabilities. Findings pave the way for the development of a new framework that exploits these differences in the dynamics of motions to accurately predict the location and size of a catastrophic landslide, and distinguish it from false alarms and/or smaller land slips early in the pre-failure regime.
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Acknowledgements
A. T. and S. Z. acknowledge support from the U.S. Army International Technology Center Pacific (ITC-PAC) and US DoD High Performance Computing Modernization Program (HPCMP) Contract FA5209-18-C-0002.
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Tordesillas, A., Zhou, S., Di Traglia, F., Intrieri, E. (2021). New Insights into the Spatiotemporal Precursory Failure Dynamics of the 2017 Xinmo Landslide and Its Surrounds. In: Casagli, N., Tofani, V., Sassa, K., Bobrowsky, P.T., Takara, K. (eds) Understanding and Reducing Landslide Disaster Risk. WLF 2020. ICL Contribution to Landslide Disaster Risk Reduction. Springer, Cham. https://doi.org/10.1007/978-3-030-60311-3_39
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DOI: https://doi.org/10.1007/978-3-030-60311-3_39
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