Application of Cabled Offshore Ocean Bottom Tsunami Gauge Data for Real-Time Tsunami Forecasting

Author(s):  
Hiroaki Tsushima ◽  
Ryota Hino ◽  
Hiromi Fujimoto ◽  
Yuichiro Tanioka
Author(s):  
Hiroaki Tsushima ◽  
Ryota Hino ◽  
Hiromi Fujimoto ◽  
Yuichiro Tanioka ◽  
Fumihiko Imamura

Author(s):  
Juh-Whan Lee ◽  
Jennifer L. Irish ◽  
Robert Weiss

Since near-field-generated tsunamis can arrive within a few minutes to coastal communities and cause immense damage to life and property, tsunami forecasting systems should provide not only accurate but also rapid tsunami run-up estimates. For this reason, most of the tsunami forecasting systems rely on pre-computed databases, which can forecast tsunamis rapidly by selecting the most closely matched scenario from the databases. However, earthquakes not included in the database can occur, and the resulting error in the tsunami forecast may be large for these earthquakes. In this study, we present a new method that can forecast near-field tsunami run-up estimates for any combination of earthquake fault parameters on a real topography in near real-time, hereafter called the Tsunami Run-up Response Function (TRRF).Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/tw1D29dDxmY


2012 ◽  
Vol 117 (B2) ◽  
pp. n/a-n/a ◽  
Author(s):  
Yusaku Ohta ◽  
Tatsuya Kobayashi ◽  
Hiroaki Tsushima ◽  
Satoshi Miura ◽  
Ryota Hino ◽  
...  

First Break ◽  
2018 ◽  
Vol 36 (4) ◽  
pp. 55-61 ◽  
Author(s):  
Alex Goertz ◽  
Andreas Wuestefeld

2018 ◽  
Vol 52 (3) ◽  
pp. 100-108 ◽  
Author(s):  
Takeshi Nakamura ◽  
Narumi Takahashi ◽  
Kensuke Suzuki

AbstractThe deployment of real-time permanent ocean-bottom seismic and tsunami observatories is significant for disaster mitigation and prevention during the occurrence of large subduction earthquakes near trough areas. On April 1, 2016, a moderate-sized suboceanic earthquake occurred beneath Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET) stations that were recently deployed in deep ocean-bottom areas near the Nankai Trough in southwest Japan. P-waves arrived at the ocean-bottom station within 4 s of the origin time, which was 6 and 13 s earlier than the arrival of P- and S-waves at a land station in the coastal area, respectively; this implies earlier detection of strong motion than at land stations. However, the waveforms are amplified by sediment layers and even contaminated with acceleration offsets at some stations, which would lead to overestimations during source investigations. Such amplification and offset did not occur at a borehole station connected to DONET. The amplifications caused by the sediment layers and the offset were found to have a considerable spatial variation, not only between the DONET stations and land and borehole stations but also among the DONET stations, implying that the amplitude evaluation could be unstable. Therefore, procedures for correcting or suppressing the amplification and offset problem are required for conducting waveform analyses, such as magnitude estimations and source modeling, during large subduction earthquakes.


Geosciences ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 310 ◽  
Author(s):  
Mayu Inoue ◽  
Yuichiro Tanioka ◽  
Yusuke Yamanaka

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.


2016 ◽  
Vol 50 (3) ◽  
pp. 76-86
Author(s):  
Takeshi Nakamura ◽  
Toshitaka Baba

AbstractWe developed a semi-real-time calculation and data monitoring system that measures pressure perturbations at ocean-bottom pressure-gauge stations deployed off the Kii peninsula in southwest Japan in order to identify tsunami signals associated with earthquakes. The system automatically calculates geodetic deformations and tsunami propagation immediately after getting seismic source information on hypocenter, magnitude, and mechanism. The calculation results for transoceanic tsunamis can be available in approximately 20 s after getting source information to output waveform data by executing the optimized parallel calculation code on our computer server SGI UV2000 with a 32-core processor unit. The system also provides tide-removed and filtered waveform data at ocean-bottom stations, enabling the calculation results to be compared with actual tsunami arrivals. System operations began in July 2015 and have been applied to tsunamigenic earthquakes in the Pacific Ocean. The system is effective in identifying tsunami signals and automatically predicting tsunami propagation in offshore areas, which may be useful for further data analyses on tsunami propagation.


2011 ◽  
Author(s):  
Kai Sun ◽  
Oluwole Ayodotun Omole ◽  
Luigi Alfonso Saputelli ◽  
Fabio Alberto Gonzalez
Keyword(s):  

2016 ◽  
Vol 68 (1) ◽  
Author(s):  
Naotaka Yamamoto ◽  
Shin Aoi ◽  
Kenji Hirata ◽  
Wataru Suzuki ◽  
Takashi Kunugi ◽  
...  

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