Tsunami Data Assimilation of Cabled Ocean Bottom Pressure Records for the 2015 Torishima Volcanic Tsunami Earthquake

Abstract

Abnormal volcanic earthquakes occurring near the volcanic island of Torishima, south of Japan, sometimes generate relatively larger tsunamis compared to the seismic magnitudes. They have a non-double-couple focal mechanism known as compensated linear vector dipole. The unusual earthquake source mechanism poses difficulties in traditional tsunami forecasting method based on seismic parameters. Tsunami data assimilation, a method of tsunami forecast using offshore tsunami observation data and numerical model, avoids the complexities and uncertainties in the tsunami source estimation and makes a tsunami early warning at coastal points of interest. Along Nankai trough, many offshore bottom pressure gauges are installed, and the data are transmitted in real time through submarine cables. Previous synthetic experiments have demonstrated the capability of the data assimilation approach for tsunami forecasting. In this paper, we report the successful application of the tsunami data assimilation to the cabled ocean bottom pressure gauge data for the first time. We assimilated the tsunami data recorded on ocean bottom pressure gauges to retroactively forecast the tsunamis of the 2015 Torishima earthquake. Comparison of the forecasted and observed waveforms at two coastal tide gauges (Kushimoto and Tosashimizu) confirmed that our method could forecast the tsunami amplitude and arrival time accurately, merely based on offshore tsunami observations. We also investigated the relationship between the number of observational stations used for assimilation and the forecasting accuracy and determined which stations were more important in data assimilation.

Simulation of the 2018 Tsunami Due to the Flank Failure of Anak Krakatau Volcano and Implication for Future Observing Systems

“…Since first introduced by Maeda et al (2015), a sequential tsunami data assimilation method has been of interest to many tsunami studies (e.g., Gusman et al, 2016; Mulia et al, 2017; Sheehan et al, 2019; Wang et al, 2019). The main intriguing feature of the method is that the tsunami forecasts can be computed without estimating its source, which can be very advantageous to nonseismic events including landslides.…”

Review on Recent Progress in Near-Field Tsunami Forecasting Using Offshore Tsunami Measurements: Source Inversion and Data Assimilation

“…As long as the linearity is assumed, GFTDA is mathematically equivalent to the previous data assimilation approach. The application to the real data from the 2012 Haida Gwaii earthquake and the 2015 Torishima earthquake revealed that GFTDA achieved the same accuracy as the previous data assimilation approach while reducing the 99% calculation time required to issue a valid tsunami warning (Wang et al, 2017(Wang et al, , 2019c. Here, the accuracy is calculated by the geometric mean ratio of the observed and simulated tsunami amplitude.…”

“…Heidarzadeh et al (2019) discussed the potential deployment of OBPGs in the western Mediterranean Sea based on data assimilation for a tsunami warning system and analyzed the sensitivity of the number and arrangement of stations. Wang et al (2019c) used the existing DONET observational network as an example to investigate the relationship between the number of observational stations used for assimilation and the forecasting accuracy. Yang et al (2019) conducted a synthetic 1-D experiment and found that the station interval of OBPGs should not exceed 50 km when using the optimal interpolation algorithm to guarantee satisfactory forecasting accuracy.…”

Data Assimilation Using High‐Frequency Radar for Tsunami Early Warning: A Case Study of the 2022 Tonga Volcanic Tsunami

“…For instance, S‐net stations provide a sampling rate of 0.1 s and are capable of detecting tsunamis with amplitudes of <1 cm (Kubota et al., 2020). Another OBPG network in Japan, the Dense Oceanfloor Network System for Earthquakes and Tsunamis, provide a sampling rate of 1 s and also can detect tsunamis with amplitudes of <1 cm (Kaneda, 2010; Wang et al., 2019).…”