5:15 PM - 6:30 PM
[STT55-P02] Development of tsunami Green’s function database based on linear dispersive-wave theory and its application to real-time tsunami forecasting
Keywords:K computer, Real-time tsunami forecasting, Database, Linear dispersive-wave theory, Disaster mitigation
In numerical simulations of tsunami forecasting, we assumed the 1933 Sanriku earthquake (magnitude 8.4) as a target event. This earthquake is an outer-rise normal-faulting event. Since the seafloor deformation is abundant in short wavelength and water depth in the source is great, the resultant tsunami waveforms are dispersive. To produce synthetic observation, we assumed earthquake faulting model proposed by Kanamori (1971) and then calculate the tsunami propagation based on nonlinear dispersive-wave theory. Then, we estimated initial tsunami height distribution using the synthetic data at offshore stations to forecast coastal tsunami waveforms. In tFISH inversion with LLW DB, significant source artifact appeared, while the artifact disappeared by applying our DSP DB. At coastal points around which offshore tsunami stations are few, better forecasting results were obtained with DSP Green functions than with LLW ones. This indicates that use of DSP Green functions is important to improve tsunami source estimation and tsunami prediction for dispersive event. Next, we assumed one of the huge Nankai-trough earthquakes proposed by the Cabinet Office: an earthquake with huge slip off Kochi Prefecture. To simplify situation, we neglect finiteness of rupture velocity in production of synthetic observations. It is noteworthy that the resulting synthetic data are less dispersive. Then, we compared the predicted tsunami waveforms at coastal points based on DSP DB and those based on LLW DB. As a result, these show good agreement. This result indicates the possibility that DSP DB works well for both dispersive and non-dispersive events. To clarify this point, we will perform more performance tests in future.
This research used computational resources of the K computer provided by the RIKEN Advanced Institute for Computational Science through the HPCI System Research project (Project ID: hp150216).