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Abstract Landslides commonly occur in areas with steep topography and abundant precipitation and pose a significant hazard to local communities. Some of the largest known landslides occur in Alaska, including several that caused local tsunamis. Many landslides may have gone undetected in remote areas due to lack of observations. Here, we develop a semiautomated workflow using both seismic and geodetic observations to detect, locate, validate, and characterize landslides in Alaska. Seismic observations have shown promise in continuously monitoring landslide occurrence, while remote sensing techniques are well suited for verification and high‐resolution imaging of landslides. We validate our procedure using the 28 June 2016, Lamplugh Glacier landslide. We also present observations of a previously unknown landslide occurred on 22 September 2017 in the Wrangell Mountains region. The Wrangell Mountains landslide generated a coherent surface wavefield recorded across Alaska and the contiguous United States. We used Sentinel‐1 Synthetic Aperture Radar and Sentinel‐2 optical imagery to map the respective mass deposit. To investigate the landslide dynamics, we inverted regional seismic surface wave data for a centroid single force failure model. Our model suggests that the Wrangell Mountains landslide lasted for about 140 s and had two subevents involving at least five distinct stages. We estimate that the landslide had displaced 3.1–13.4 million tons of rocks over a distance of ∼2 km. Our results suggest that combining seismic and geodetic observations can vastly improve the detection and characterization of landslides in remote areas in Alaska and elsewhere, providing new insights into the landslide dynamics.
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Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning
Abstract The continuously growing amount of seismic data collected worldwide is outpacing our abilities for analysis, since to date, such datasets have been analyzed in a human-expert-intensive, supervised fashion. Moreover, analyses that are conducted can be strongly biased by the standard models employed by seismologists. In response to both of these challenges, we develop a new unsupervised machine learning framework for detecting and clustering seismic signals in continuous seismic records. Our approach combines a deep scattering network and a Gaussian mixture model to cluster seismic signal segments and detect novel structures. To illustrate the power of the framework, we analyze seismic data acquired during the June 2017 Nuugaatsiaq, Greenland landslide. We demonstrate the blind detection and recovery of the repeating precursory seismicity that was recorded before the main landslide rupture, which suggests that our approach could lead to more informative forecasting of the seismic activity in seismogenic areas.
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- Award ID(s):
- 1815143
- PAR ID:
- 10298832
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41467-020-17841-x
