To assess the risk of tsunamis from outer-rise earthquakes, we carried out tsunami simulations using 33 simple rectangular fault models with 60°dip angles based on marine seismic observations and surveys of the Japan Trench. The largest tsunami resulting from these models, produced by a M w 8.7 normal-faulting event on a fault 332 km long, had a maximum height of 27.0 m. We tested variations of the predictions due to the uncertainties in the assumed parameters. Because the actual dip angles of the Japan Trench outer-rise faults range from 45°to 75°, we calculated tsunamis from earthquakes on fault models with 45°, 60°, and 75°dip angles. We also tested a compound fault model with 75°dip in the upper half and 45°dip in the lower half. Rake angles were varied by ±15°. We also tested models consisting of small subfaults with dimensions of about 60 km, models using other earthquake scaling laws, models with heterogeneous slips, and models incorporating dispersive tsunami effects. Predicted tsunami heights changed by 10-15% for heterogeneous slips, up to 10% for varying dip angles, about 5-10% from considering tsunami dispersion, about 2% from varying rake angles, and about 1% from using the model with small subfaults. The use of different earthquake scaling laws changed predicted tsunami heights by about 50% on average for the 33 fault models. We emphasize that the earthquake scaling law used in tsunami predictions for outer-rise earthquakes should be chosen with great care. To improve early warning of shaking and tsunamis due to offshore earthquakes, a large-scale cabled observatory, the Seafloor Observation Network for Earthquakes and Tsunamis along the Japan Trench (S-net) was deployed in 2011 (NIED, 2019). S-net is equipped with about 150 ocean-bottom pressure gauges that can register the passage of tsunamis before their arrival at the coast and seven GPS-equipped buoys that can measure changes in sea level (Kato et al., 2005) have been installed less than 20 km off the coast. Real-time tsunami prediction systems have been developed that rely on offshore tsunami observation data. The tFISH method (Tsushima et al., 2009, 2012) estimates the initial sea surface deformation by inverting pressure data in real time. Maeda et al. (2015) and Wang et al. (2017) have applied data assimilation techniques to the pressure data to improve predictions of tsunami propagation.
