2:45 PM - 3:00 PM
[HDS10-05] Detection of Undetected Non-Seismic Tsunamis Using DONET Ocean Bottom Pressure Records
Keywords:tsunami, non-seismic tsunami, auto-detection, seafloor observation network
In Japan, large-scale real-time seafloor observation networks have been established since around 2011 to quickly detect subduction zone earthquakes and associated tsunamis. Online water pressure gauges installed at each station of the seafloor observation network enable us to detect weak tsunamis, which has been difficult with conventional sparse coastal tide gauges.
It is expected that some unknown tsunami events, which have not been detected as tsunamis due to their small amplitude, are included in the data obtained from the ocean bottom pressure gauges. When the unknown events occur repeatedly, similar waveforms are recorded more than once at the same observation point. Since the correlation coefficient can quantify the similarity of waveforms, repeated events can be detected by calculating the correlation coefficient between data from different time series at the same observation point.
In this study, we applied the “network cross-correlation method” to the ocean bottom pressure gauge data to detect unknown tsunami events, and searched extensively for similar waveforms. We analyzed DONET ocean bottom pressure gauge records (frequency band 0.001-0.01Hz) from 2016 through 2023, we succeeded in detecting a large number of unknown events. Since no tsunami-generating earthquakes coincide with the observed events, they are considered non-seismic tsunamis. Submarine landslides, submarine volcanic activities, and meteorological disturbances are known to cause non-seismic tsunami. In our analysis, the most of the detected non-seismic tsunamis are likely meteorological tsunamis originating from meteorological disturbances.
By applying our method, it may be possible to automatically detect non-seismic tsunamis in the future, which has been difficult with conventional methods. We plan to conduct a more detailed analysis of the detected events and investigate the causes of their occurrence.
It is expected that some unknown tsunami events, which have not been detected as tsunamis due to their small amplitude, are included in the data obtained from the ocean bottom pressure gauges. When the unknown events occur repeatedly, similar waveforms are recorded more than once at the same observation point. Since the correlation coefficient can quantify the similarity of waveforms, repeated events can be detected by calculating the correlation coefficient between data from different time series at the same observation point.
In this study, we applied the “network cross-correlation method” to the ocean bottom pressure gauge data to detect unknown tsunami events, and searched extensively for similar waveforms. We analyzed DONET ocean bottom pressure gauge records (frequency band 0.001-0.01Hz) from 2016 through 2023, we succeeded in detecting a large number of unknown events. Since no tsunami-generating earthquakes coincide with the observed events, they are considered non-seismic tsunamis. Submarine landslides, submarine volcanic activities, and meteorological disturbances are known to cause non-seismic tsunami. In our analysis, the most of the detected non-seismic tsunamis are likely meteorological tsunamis originating from meteorological disturbances.
By applying our method, it may be possible to automatically detect non-seismic tsunamis in the future, which has been difficult with conventional methods. We plan to conduct a more detailed analysis of the detected events and investigate the causes of their occurrence.