Abstract
The ocean floor network system for earthquakes and tsunamis is one of the effective tools for the early detection of large earthquakes on plate boundaries and the tsunamis they generate. The Dense Oceanfloor Network system for Earthquakes and Tsunamis (DONET) was installed in the first rupture areas of the 1944 Tonankai and 1946 Nankai earthquakes. The DONET around the Nankai Trough, a site of huge earthquakes that have caused severe damage, has the potential to detect the genesis of a tsunami. We developed a real-time tsunami prediction system for local communities that takes advantage of the features of DONET, and we have already made it available to several local governments and a commercial company. The outputs of the prediction are the tsunami arrival time, its height, its inundation area, and inundation depth. The system makes real-time monitoring of tsunamis possible. The system should be conceptually applicable to the Nankai Trough area, which has characteristics consistent with the assumptions the system makes about tsunami propagation, crustal activities, and coastal communities. Here, we describe the conceptual basis of the system, the features used to ensure the accuracy of predictions, and the policies used to develop and implement them.
Highlights
The 2011 magnitude (M) nine Tohoku earthquake off the Pacific coast and the huge tsunami it generated caused severe damage to coastal areas of Japan
Crustal activities, including tsunamis, can continue to be observed even if one of the sensors experiences problems. These safeguards are indispensable for an ocean floor network system because continuity without missing data is important to their implementation and use in societies
We introduced the composition of the crustal uplift into the pressure gauge data of the ocean floor network system (Figure 6)
Summary
The 2011 magnitude (M) nine Tohoku earthquake off the Pacific coast and the huge tsunami it generated caused severe damage to coastal areas of Japan. Crustal activities, including tsunamis, can continue to be observed even if one of the sensors experiences problems These safeguards are indispensable for an ocean floor network system because continuity without missing data is important to their implementation and use in societies. Yamamoto et al (2016) [15] have prepared a tsunami database using many fault models set on the plate boundary and have selected tsunami scenarios based on multiple indices of observed and calculated waveforms. Their method selects common fault models based on all of the data in the ocean floor network system. Makinoshima et al (2021) [20] have proposed a tsunami forecasting approach using convolutional neural networks through numerical experiments
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