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

Abstract

AbstractIn this chapter, I combined the tsunami data assimilation approach with the real-time tsunami detection algorithm. The tsunami of the 2016 Fukushima earthquake was recorded by the offshore pressure gauges of the Seafloor Observation Network for Earthquakes and Tsunamis (S-net). I 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, I removed the tidal components by polynomial fitting and applied a low-pass filter. In the second, I used the real-time tsunami detection algorithm based on EEMD to extract tsunami signals, imitating real-time operations for tsunami early warning. The scores of forecast accuracy of the two detection methods were 60% and 74%, respectively, for a time window of 35 min, which improved to 89% and 94%, respectively, if stations with imperfect modeling or insufficient offshore observations were omitted. Hence, the proposed tsunami data assimilation approach can be put into practice with the help of the real-time tsunami detection algorithm.KeywordsEnsemble empirical mode decompositionReal-time tsunami detection algorithmLast-moment IMF2