Real-Time Tsunami Data Assimilation of S-Net Pressure Gauge Records during the 2016 Fukushima Earthquake

Abstract

Abstract The 2016 Fukushima earthquake (M 7.4) generated a moderate tsunami, which was recorded by the offshore pressure gauges of the Seafloor Observation Network for Earthquakes and Tsunamis (S-net). We used 28 S-net pressure gauge records for tsunami data assimilation and forecasted the tsunami waveforms at four tide gauges on the Sanriku coast. The S-net raw records were processed using two different methods. In the first method, we removed the tidal components by polynomial fitting and applied a low-pass filter. In the second method, we used a real-time tsunami detection algorithm based on ensemble empirical mode decomposition to extract the tsunami signals, imitating real-time operations for tsunami early warning. The forecast accuracy scores of the two detection methods are 60% and 74%, respectively, for a time window of 35 min, but they improve to 89% and 94% if we neglect the stations with imperfect modeling or insufficient offshore observations. Hence, the tsunami data assimilation approach can be put into practice with the help of the real-time tsunami detection algorithm.