Abstract
AbstractIn this chapter, I proposed a real-time tsunami detection algorithm using Ensemble Empirical Mode Decomposition (EEMD). EEMD adaptively decomposes a time series into a set of Intrinsic Mode Functions (IMFs). The tsunami signals of the Offshore Bottom Pressure Gauge (OBPG) can be automatically separated from the tidal components, seismic waves, and background noise. Unlike traditional tsunami detection methods, the new algorithm does not require tidal predictions. The application to the actual data of cabled OBPGs off the Tohoku coast showed that it successfully detected the tsunami from the 2016 Fukushima earthquake (M 7.4). The method was also applied to the extremely large tsunami from the 2011 Tohoku earthquake (M 9.0) and extremely small tsunami from the 1998 off Sanriku earthquake (M 6.4). The algorithm detected the former, which caused devastating damage, whereas it did not detect the latter micro tsunami, which was not noticed on the coast. The algorithm was also tested for a month-long OBPG data, and no false alarm occurred. Hence, the algorithm could detect tsunami arrival with a short detection delay and accurately characterize the tsunami amplitude.KeywordsIntrinsic mode functionPost-processed waveformFalse alarmMissed alarm
Published Version
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