Successive estimation of a tsunami wavefield without earthquake source data: A data assimilation approach toward real-time tsunami forecasting

We propose a tsunami forecasting method based on a data assimilation technique designed for dense tsunameter networks. Rather than using seismic source parameters or initial sea surface height as the initial condition of for a tsunami forecasting, it estimates the current tsunami wavefield (tsunami height and tsunami velocity) in real time by repeatedly assimilating dense tsunami data into a numerical simulation. Numerical experiments were performed using a simple 1-D station array and the 2-D layout of the new S-net tsunameter network around the Japan Trench. Treating a synthetic tsunami calculated by the finite-difference method as observed data, the data assimilation reproduced the assumed tsunami wavefield before the tsunami struck the coastline. Because the method estimates the full tsunami wavefield, including velocity, these wavefields can be used as initial conditions for other tsunami simulations to calculate inundation or runup for real-time forecasting.