ID | 118752 |
Author |
Takahashi, Narumi
National Research Institute for Earth Science and Disaster Resilience
Imai, Kentaro
Japan Agency for Marine-Earth Science and Technology
Ishibashi, Masanobu
Wakayama Prefecture
Sueki, Kentaro
Japan Agency for Marine-Earth Science and Technology
Obayashi, Ryoko
Japan Agency for Marine-Earth Science and Technology
Tanabe, Tatsuo
NTT data CCS corporation
Tamazawa, Fumiyasu
NTT data CCS corporation
Baba, Toshitaka
Tokushima University
Tokushima University Educator and Researcher Directory
KAKEN Search Researchers
Kaneda, Yoshiyuki
Kagawa University
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Keywords | tsunami
real-time prediction
DONET
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Content Type |
Journal Article
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Description | We constructed a real-time tsunami prediction system using the Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET). This system predicts the arrival time of a tsunami, the maximum tsunami height, and the inundation area around coastal target points by extracting the proper fault models from 1,506 models based on the principle of tsunami amplification. Since DONET2, installed in the Nankai earthquake rupture zone, was constructed in 2016, it has been used in addition to DONET1 installed in the Tonankai earthquake rupture zone; we revised the system using both DONET1 and DONET2 to improve the accuracy of tsunami prediction. We introduced a few methods to improve the prediction accuracy. One is the selection of proper fault models from the entire set of models considering the estimated direction of the hypocenter using seismic and tsunami data. Another is the dynamic selection of the proper DONET observatories: only DONET observatories located between the prediction point and tsunami source are used for prediction. Last is preparation for the linked occurrence of double tsunamis with a time-lag. We describe the real-time tsunami prediction system using DONET and its implementation for the Shikoku area.
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Journal Title |
Journal of Disaster Research
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ISSN | 18812473
18838030
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Publisher | Fuji Technology Press
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Volume | 12
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Issue | 4
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Start Page | 766
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End Page | 774
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Published Date | 2017-08-01
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Rights | This is an Open Access article distributed under the terms of the Creative Commons Attribution-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nd/4.0/).
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DOI (Published Version) | |
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language |
eng
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departments |
Science and Technology
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