Multi-index method using offshore ocean-bottom pressure data for real-time tsunami forecast

It is important to advance preparation for a tsunami disaster, one of the great concerns in Japan. Forecasting tsunami inundation is one such solution, which contributes to perceiving the danger of the tsunami, as the inundation is directly linked with the damage. Therefore, we developed a new real-time tsunami forecast system, aimed at rapidly and accurately forecasting tsunami inundation on land, based on offshore tsunami data observed by the seafloor observation network along the Japan Trench, S-net. The developed system takes a database approach. A database called a tsunami scenario bank was constructed by assuming all the possible tsunami sources affecting the target region and simulating offshore pressure data, coastal tsunami heights, and tsunami inundation. The forecast system searches for suitable tsunami scenarios whose offshore pressure data explain the observed data, based on the multi-index method. The multi-index method can evaluate the resemblance of offshore pressure data by using three indices, which are sensitive to different aspects of the pressure change distribution. When tsunami scenarios meet the criteria of the multi-index method, the system provides forecast information generated from coastal tsunami heights and tsunami inundation of the selected scenarios. A prototype system was constructed for the Pacific coastal region of Chiba prefecture as a target region and has been updated through a test operation. We also investigated the comprehensible visualization and effective disaster response using tsunami forecast information. Through workshops and tabletop exercises with local government officers using the forecast system, timelines and local disaster management plans for tsunamis were tested and revised. This led to the establishment of a standard operating procedure for tsunami disaster response through the use of tsunami observation and forecast information.

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Near-field tsunami inundation forecast method assimilating ocean bottom pressure data: A synthetic test for the 2011 Tohoku-oki tsunami

Physics of The Earth and Planetary Interiors ◽

10.1016/j.pepi.2018.08.006 ◽

2018 ◽

Vol 283 ◽

pp. 82-91 ◽

Cited By ~ 7

Author(s):

Yuichiro Tanioka ◽

Aditya Riadi Gusman

Keyword(s):

Near Field ◽

Ocean Bottom ◽

Bottom Pressure ◽

Pressure Data ◽

Forecast Method ◽

Tsunami Inundation ◽

Ocean Bottom Pressure ◽

Synthetic Test

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Improvement of near-field tsunami forecasting method using ocean-bottom pressure sensor network (S-net)

Earth Planets and Space ◽

10.1186/s40623-020-01268-1 ◽

2020 ◽

Vol 72 (1) ◽

Author(s):

Yuichiro Tanioka

Keyword(s):

Interpolation Method ◽

Japan Trench ◽

Ocean Bottom ◽

Fault Models ◽

Bottom Pressure ◽

Tsunami Forecast ◽

Weighted Interpolation ◽

Ocean Bottom Pressure ◽

Tsunami Forecasting ◽

Forecasting Method

Abstract Since the installation of a dense cabled observation network around the Japan Trench (S-net) by the Japanese government that includes 150 sensors, several tsunami forecasting methods that use the data collected from the ocean floor sensors were developed. One of such methods is the tsunami forecasting method which assimilates the data without any information of earthquakes. The tsunami forecasting method based on the assimilation of the ocean-bottom pressure data near the source area was developed by Tanioka in 2018. However, the method is too simple to be used for an actual station distribution of S-net. To overcome its limitation, we developed an interpolation method to generate the appropriate data at the equally spaced positions for the assimilation from the data observed at sensors in S-net. The method was numerically tested for two large underthrust fault models, a giant earthquake (Mw8.8) and the Nemuro-oki earthquake (Mw8.0) models. Those fault models off Hokkaido in Japan are expected to be ruptured in the future. The weighted interpolation method, in which weights of data are inversely proportional to the square of the distance, showed good results for the tsunami forecast method with the data assimilation. Furthermore, results indicated that the method is applicable to the actual observed data at the S-net stations. The only limitation of the weighted interpolation method is that the computed tsunami wavelengths tend to be longer than the actual tsunamis wavelength.

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Improvement of Near-field Tsunami Forecasting Method Using Ocean Bottom Pressure Sensor Network (S-net)

10.21203/rs.3.rs-37911/v2 ◽

2020 ◽

Author(s):

Yuichiro Tanioka

Keyword(s):

Interpolation Method ◽

Japan Trench ◽

Ocean Bottom ◽

Fault Models ◽

Bottom Pressure ◽

Tsunami Forecast ◽

Weighted Interpolation ◽

Ocean Bottom Pressure ◽

Tsunami Forecasting ◽

Forecasting Method

Abstract Since the installation of a dense cabled observation network around the Japan Trench (S-net) by the Japanese government that includes 150 sensors, several tsunami forecasting methods that use the data collected from the ocean floor sensors were developed. One of such methods is the tsunami forecasting method which assimilates the data without any information of earthquakes. The tsunami forecasting method based on the assimilation of the ocean-bottom pressure data near the source area was developed by Tanioka in 2018. However, the method is too simple to be used for an actual station distribution of S-net. To overcome its limitation, we developed an interpolation method to generate the appropriate data at the equally spaced positions for the assimilation from the data observed at sensors in S-net. The method was numerically tested for two large underthrust fault models, a giant earthquake (Mw8.8) and the Nemuro-oki earthquake (Mw8.0) models. Those fault models off Hokkaido in Japan are expected to be ruptured in the future. The weighted interpolation method, in which weights of data are inversely proportional to the square of the distance, showed good results for the tsunami forecast method with the data assimilation. Furthermore, results indicated that the method is applicable to the actual observed data at the S-net stations. The only limitation of the weighted interpolation method is that the computed tsunami wavelengths tend to be longer than the actual tsunamis wavelength.

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Improvement of Near-field Tsunami Forecasting Method Using Ocean Bottom Pressure Sensor Network (S-net)

10.21203/rs.3.rs-37911/v1 ◽

2020 ◽

Author(s):

Yuichiro Tanioka

Keyword(s):

Interpolation Method ◽

Japan Trench ◽

Ocean Bottom ◽

Fault Models ◽

Bottom Pressure ◽

Tsunami Forecast ◽

Weighted Interpolation ◽

Ocean Bottom Pressure ◽

Tsunami Forecasting ◽

Forecasting Method

Abstract Since the installation of a dense cabled observation network around the Japan Trench (S-net) by the Japanese government that includs 150 sensors, several tsunami forecasting methods that use the data collected from the ocean floor sensors were developed. One of such methods is the tsunami forecasting method which assimilates the data without any information of earthquakes. The tsunami forecasting method based on the assimilation of the ocean-bottom pressure data near the source area was developed by Tanioka in 2018. However, the method is too simple to be used for an actual station distribution of S-net. To overcome its limitation, we developed an interpolation method to generate the appropriate data at the equally spaced positions for the assimilation from the data observed at sensors in S-net. The method was numerically tested for two large underthrust fault models, a giant earthquake (Mw8.8) and the Nemuro-oki earthquake (Mw8.0) models. Those fault models off Hokkaido in Japan are expected to be ruptured in the future. The weighted interpolation method, in which weights of data are inversely proportional to the square of the distance, showed good results for the tsunami forecast method with the data assimilation. Furthermore, results indicated that the method is applicable to the actual observed data at the S-net stations. The only limitation of the weighted interpolation method is that the computed tsunami wavelengths tend to be longer than the actual tsunamis wavelength.

Download Full-text

Improvement of Near-field Tsunami Forecasting Method Using Ocean Bottom Pressure Sensor Network (S-net)

10.21203/rs.3.rs-37911/v3 ◽

2020 ◽

Author(s):

Yuichiro Tanioka

Keyword(s):

Interpolation Method ◽

Japan Trench ◽

Ocean Bottom ◽

Fault Models ◽

Bottom Pressure ◽

Tsunami Forecast ◽

Weighted Interpolation ◽

Ocean Bottom Pressure ◽

Tsunami Forecasting ◽

Forecasting Method

Abstract Since the installation of a dense cabled observation network around the Japan Trench (S-net) by the Japanese government that includes 150 sensors, several tsunami forecasting methods that use the data collected from the ocean floor sensors were developed. One of such methods is the tsunami forecasting method which assimilates the data without any information of earthquakes. The tsunami forecasting method based on the assimilation of the ocean-bottom pressure data near the source area was developed by Tanioka in 2018. However, the method is too simple to be used for an actual station distribution of S-net. To overcome its limitation, we developed an interpolation method to generate the appropriate data at the equally spaced positions for the assimilation from the data observed at sensors in S-net. The method was numerically tested for two large underthrust fault models, a giant earthquake (Mw8.8) and the Nemuro-oki earthquake (Mw8.0) models. Those fault models off Hokkaido in Japan are expected to be ruptured in the future. The weighted interpolation method, in which weights of data are inversely proportional to the square of the distance, showed good results for the tsunami forecast method with the data assimilation. Furthermore, results indicated that the method is applicable to the actual observed data at the S-net stations. The only limitation of the weighted interpolation method is that the computed tsunami wavelengths tend to be longer than the actual tsunamis wavelength.

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