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

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