open access

https://hdl.handle.net/2115/75720

Method for Near-Real Time Estimation of Tsunami Sources Using Ocean Bottom Pressure Sensor Network (S-Net)

英語

ocean bottom pressure network

2019-07

Geosciences

9(7)

310

A dense cabled observation network, called the seafloor observation network for earthquakes and tsunami along the Japan Trench (S-net), was installed in Japan. This study aimed to develop a near-real time tsunami source estimation technique using the ocean bottom pressure data observed at those sensors in S-net. Synthetic pressure waveforms at those sensors were computed for 64 earthquake tsunami scenarios with magnitude ranging between M8.0 and M8.8. The pressure waveforms within a time window of 500 s after an earthquake were classified into three types. Type 1 has the following pressure waveform characteristic: the pressure decreases and remains low; sensors exhibiting waveforms associated with Type 1 are located inside a co-seismic uplift area. The pressure waveform characteristic of Type 2 is that one up-pulse of a wave is within the time window; sensors exhibiting waveforms associated with Type 2 are located at the edge of the co-seismic uplift area. The other pressure waveforms are classified as Type 3. Subsequently, we developed a method to estimate the uplift area using those three classifications of pressure waveforms at sensors in S-net and a method to estimate earthquake magnitude from the estimated uplift area using a regression line. We systematically applied those methods for two cases of previous large earthquakes: the 1952 Tokachi-oki earthquake (Mw8.2) and the 1968 Tokachi-oki earthquake (Mw8.1). The locations of the large computed uplift areas of the earthquakes were well defined by the estimated ones. The estimated magnitudes of the 1952 and 1968 Tokachi-oki earthquakes from the estimated uplift area were 8.2 and 7.9, respectively; they are almost consistent with the moment magnitudes derived from the source models. Those results indicate that the tsunami source estimation method developed in this study can be used for near-real time tsunami forecasts.

学術雑誌論文

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© 2019 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).

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