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JDR Vol.12 No.4 pp. 766-774
doi: 10.20965/jdr.2017.p0766
(2017)

Paper:

Real-Time Tsunami Prediction System Using DONET

Narumi Takahashi*1,†, Kentaro Imai*2, Masanobu Ishibashi*3, Kentaro Sueki*2, Ryoko Obayashi*2, Tatsuo Tanabe*4, Fumiyasu Tamazawa*4, Toshitaka Baba*5, and Yoshiyuki Kaneda*6

*1National Research Institute for Earth Science and Disaster Resilience
3-1 Tennodai, Tsukuba 305-0006, Japan

Corresponding author

*2Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

*3Wakayama Prefecture, Wakayama, Japan

*4NTT data CCS corporation, Tokyo, Japan

*5Tokushima University, Tokushima, Japan

*6Kagawa University, Takamatsu, Japan

Received:
February 15, 2017
Accepted:
May 29, 2017
Online released:
July 28, 2017
Published:
August 1, 2017
Keywords:
tsunami, real-time prediction, DONET
Abstract

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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Last updated on Oct. 20, 2017