Synthetic analysis of the efficacy of the S-net system in tsunami forecasting

Abstract

The Seafloor Observation Network for Earthquakes and Tsunamis along the Japan Trench (S-net) is presently the world’s largest network of ocean bottom pressure sensors for real-time tsunami monitoring. This paper analyzes the efficacy of such a vast system in tsunami forecasting through exhaustive synthetic experiments. We consider 1500 hypothetical tsunami scenarios from megathrust earthquakes with magnitudes ranging from Mw 7.7–9.1. We employ a stochastic slip model to emulate heterogeneous slip patterns on specified 240 subfaults over the plate interface of the Japan Trench subduction zone and its vicinity. Subsequently, the associated tsunamis in terms of maximum coastal tsunami heights are evaluated along the 50-m isobath by means of a Green’s function summation. To produce tsunami forecasts, we utilize a tsunami inversion from virtually observed waveforms at the S-net stations. Remarkably, forecasts accuracy of approximately 99% can be achieved using tsunami data within an interval of 3 to 5 min after the earthquake (2-min length), owing to the exceedingly dense observation points. Additionally, we apply an optimization technique to determine the optimal combination of stations with respect to earthquake magnitudes. The results show that the minimum requisite number of stations to maintain the accuracy attained by the existing network configuration decreases from 130 to 90 when the earthquake size increases from Mw 7.7 to 9.1.

Improving the Constraint on theMw7.1 2016 Off‐Fukushima Shallow Normal‐Faulting Earthquake With the High Azimuthal Coverage Tsunami Data From the S‐Net Wide and Dense Network: Implication for the Stress Regime in the Tohoku Overriding Plate

“…Recent studies have started to utilize S‐net ocean‐bottom seismometers to investigate the seismotectonics and geodynamics in the Tohoku subduction zone (Dhakal et al., 2021; Hua et al., 2020; Matsubara et al., 2019; Nishikawa et al., 2019; Sawazaki & Nakamura, 2020; Takagi et al., 2019, 2020; Tanaka et al., 2019; Uchida et al., 2020; Yu & Zhao, 2020). The S‐net also incorporates ocean‐bottom pressure gauges (OBPGs), which are expected to be utilized for tsunami forecasts (e.g., Aoi et al., 2019; Inoue et al., 2019; Mulia & Satake, 2021; Tanioka, 2020; Tsushima & Yamamoto, 2020; Wang & Satake, 2021; Yamamoto, Aoi, et al., 2016; Yamamoto, Hirata, et al., 2016). The other potential contributions to the earth sciences of the S‐net OBPG have also been demonstrated, such as understanding the wave propagation process in the ocean as well as the rupture process of subseafloor earthquakes (Kubota, Saito, & Suzuki, 2020; Kubota et al., 2021; Saito & Kubota, 2020; Saito et al., 2021).…”

Machine learning-based tsunami inundation prediction derived from offshore observations

“…To determine the appropriate number of scenarios for the megathrust earthquakes, we first apply Green’s functions summation technique 26 , 27 described in the Method section. With this technique, we can efficiently generate tsunami waveforms at S-net stations from many earthquake slip scenarios before simulating the inundation.…”

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

“…Nevertheless, because the propagation speed of tsunami is slower than that of seismic waves and crustal movements, it takes a long time to record enough tsunami waveforms to estimate the tsunami source accurately, even at offshore stations . While this lag time can be shortened by enhancing the offshore tsunami observation network (Mulia & Satake, 2021;Tsushima et al, 2012b), the use of other observation data may lead to the improvement of tsunami prediction accuracy in the early stage after the earthquake (Melgar & Bock, 2013;Wei et al, 2014). Melgar and Bock (2013) proposed a method for near-field tsunami forecasting based on joint inversion for slip distribution using tsunami data and coseismic deformation data from GNSS and ground-motion accelerometers.…”