11:45 〜 12:00
[SCG54-11] 東北地方太平洋沖地震後に設置されたS-netを利用した新しい津波即時予測手法の開発
キーワード：津波、即時予測
The 2011 Tohoku-oki earthquake (Mw9.0) generated a large tsunami along the Pacific coast of northern Japan. Although the Japan Meteorological Agency (JMA) issued a major tsunami warning along the Pacific coast of Japan immediately after the earthquake, the tsunami caused a catastrophic disaster with approximately 19,000 casualties. After this event, the development of a more accurate and rapid tsunami warning system became a high-priority focus area in Japan. For this purpose, the Japanese government installed a dense cabled observation network, called the seafloor observation network for earthquakes and tsunami around the Japan Trench (S-net), in 2017. The network is operated by the National Research Institute for Earth Science and Disaster Resilience (NEID). In this network, 150 observation stations consisting of ocean-bottom pressure sensors and seismometers are connected by cables at the 30-km intervals.
Tsunami forecasting methods that use the data collected from the ocean floor sensors were recently developed. Tsushima et al. (2012) developed a method called tsunami Forecasting based on Inversion for initial sea Surface Height (tFISH) which estimates the initial sea surface deformation from tsunami waveforms observed at ocean-bottom pressure gauges. Moreover, new tsunami computation methods based on the assimilation of tsunami observations without the tsunami source information were developed by Maeda et al. (2015), Tanioka (2018), and Tanioka and Gusman (2018). Gusman et al. (2016) applied the method developed by Maeda et al. (2015) to the tsunami generated by the 2012 Haida Gwaii earthquake and computed the tsunami wave field successfully by assimilating data observed at ocean-bottom pressure sensors in Cascadia. However, it is difficult to apply this method with ocean-bottom pressure data within the source area. The near-field tsunami inundation forecasting method developed by Tanioka (2018) and Tanioka and Gusman (2018) uses the time derivative of the pressure waveforms observed at the ocean-bottom pressure sensors near the source area. Therefore, tsunami computation by assimilating data can be performed without any tsunami source information as soon as the earthquake or tsunami generation is completed. Tanioka and Gusman (2018) tested ocean-bottom pressure sensors equally distributed at 15 min intervals or approximately 30 km apart. In reality, the S-net ocean-bottom sensors are not installed at uniform intervals, particularly in the north–south direction, and have a lesser number than those tested by Tanioka and Gusman (2018). They finally concluded that it is necessary to improve their method using the exact locations of the S-net sensors for real-time tsunami inundation forecasts.
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.
References
Tanioka Y (2018) Tsunami simulation method assimilating ocean bottom pressure data near a tsunami source region. Pure Appl Geophys 175(2):721–729
Tanioka Y, Gusman AR (2018) Near-field tsunami inundation forecast method assimilating ocean bottom pressure data: a synthetic test for the 2011 Tohoku-oki tsunami. Phys Earth Planet Int. 283:82–91.
Tsunami forecasting methods that use the data collected from the ocean floor sensors were recently developed. Tsushima et al. (2012) developed a method called tsunami Forecasting based on Inversion for initial sea Surface Height (tFISH) which estimates the initial sea surface deformation from tsunami waveforms observed at ocean-bottom pressure gauges. Moreover, new tsunami computation methods based on the assimilation of tsunami observations without the tsunami source information were developed by Maeda et al. (2015), Tanioka (2018), and Tanioka and Gusman (2018). Gusman et al. (2016) applied the method developed by Maeda et al. (2015) to the tsunami generated by the 2012 Haida Gwaii earthquake and computed the tsunami wave field successfully by assimilating data observed at ocean-bottom pressure sensors in Cascadia. However, it is difficult to apply this method with ocean-bottom pressure data within the source area. The near-field tsunami inundation forecasting method developed by Tanioka (2018) and Tanioka and Gusman (2018) uses the time derivative of the pressure waveforms observed at the ocean-bottom pressure sensors near the source area. Therefore, tsunami computation by assimilating data can be performed without any tsunami source information as soon as the earthquake or tsunami generation is completed. Tanioka and Gusman (2018) tested ocean-bottom pressure sensors equally distributed at 15 min intervals or approximately 30 km apart. In reality, the S-net ocean-bottom sensors are not installed at uniform intervals, particularly in the north–south direction, and have a lesser number than those tested by Tanioka and Gusman (2018). They finally concluded that it is necessary to improve their method using the exact locations of the S-net sensors for real-time tsunami inundation forecasts.
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.
References
Tanioka Y (2018) Tsunami simulation method assimilating ocean bottom pressure data near a tsunami source region. Pure Appl Geophys 175(2):721–729
Tanioka Y, Gusman AR (2018) Near-field tsunami inundation forecast method assimilating ocean bottom pressure data: a synthetic test for the 2011 Tohoku-oki tsunami. Phys Earth Planet Int. 283:82–91.