10:45 AM - 11:00 AM
[S17-02] Tsunami Data Assimilation of S-net Pressure Gauge Records
We processed the S-net raw records by two methods. In the first method, we removed the tidal components by polynomial interpolation, and applied a low-pass filter. This method is widely used in retrospective studies for waveform post-processing. However, the acausal digital filters require future data to filter the past data. So this method is not applicable in real-time operation. In the second method, we used the Ensemble Empirical Mode Decomposition (EEMD) real-time tsunami detection algorithm to extract the tsunami signals (Wang et al., 2020 SRL). EEMD decomposes the input data into several Intrinsic Mode Functions (IMFs), and separates the tsunami signals, seismic waves and tidal components automatically. The IMF2 represents the tsunami signals that are detected in real time. Then, the processed waveforms at 28 S-net pressure gauges were used for data assimilation.
The waveforms forecasted by assimilation with digital filter and EEMD were both consistent with the real observations in the first tsunami peak at coastal tide gauges. Though the assimilation results with digital filter had a better performance in the following waveforms, we note that only the EEMD results can be obtained in real-time operation. In summary, the data assimilation of S-net pressure gauge records is able to forecast the tsunami waveforms accurately, and the tsunami data assimilation approach can be really put into practice with the help of EEMD real-time detection algorithm.