Large-scale, high-speed tsunami prediction for the Great Nankai Trough Earthquake on the K computer

Baba, Toshitaka; Ando, Kazuto; Matsuoka, Daisuke; Hyodo, Mamoru; Hori, Takane; Takahashi, Narumi; Obayashi, Ryoko; Imato, Yoshiyuki; Kitamura, Dai; Uehara, Hiroki; Kato, Toshihiro; Saka, Ryotaro

doi:10.1177/1094342015584090

Cited by 31 publications

(19 citation statements)

References 11 publications

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“…A calculated map of the inundation area with height will be required for the forecast products as well. This will be possible using latest supercomputing systems that have shown abilities to facilitate real-time tsunami forecasts with suitable inundation by high-resolution (< 10 m) simulation (Oishi et al 2015;Musa et al 2015;Baba et al 2016). (2) We discuss in detail the uncertainties of the tsunami forecasting based on seismic data alone, especially for great earthquakes.…”

Section: Discussion For Future Perspectivesmentioning

confidence: 99%

Near-field tsunami forecast system based on near real-time seismic moment tensor estimation in the regions of Indonesia, the Philippines, and Chile

et al. 2016

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We have developed a near-field tsunami forecast system based on an automatic centroid moment tensor (CMT) estimation using regional broadband seismic observation networks in the regions of Indonesia, the Philippines, and Chile. The automatic procedure of the CMT estimation has been implemented to estimate tsunamigenic earthquakes. A tsunami propagation simulation model is used for the forecast and hindcast. A rectangular fault model based on the estimated CMT is employed to represent the initial condition of tsunami height. The forecast system considers uncertainties due to two possible fault planes and two possible scaling laws and thus shows four possible scenarios with these associated uncertainties for each estimated CMT. The system requires approximately 15 min to estimate the CMT after the occurrence of an earthquake and approximately another 15 min to make the tsunami forecast results including the maximum tsunami height and its arrival time at the epicentral region and near-field coasts available. The retrospectively forecasted tsunamis were evaluated by the deep-sea pressure and tide gauge observations, for the past eight tsunamis (M w 7.5-8.6) that occurred throughout the regional seismic networks. The forecasts ranged from half to double the amplitudes of the deep-sea pressure observations and ranged mostly within the same order of magnitude as the maximum heights of the tide gauge observations. It was found that the forecast uncertainties increased for greater earthquakes (e.g., M w > 8) because the tsunami source was no longer approximated as a point source for such earthquakes. The forecast results for the coasts nearest to the epicenter should be carefully used because the coasts often experience the highest tsunamis with the shortest arrival time (e.g., <30 min).

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Section: Discussion For Future Perspectivesmentioning

confidence: 99%

Near-field tsunami forecast system based on near real-time seismic moment tensor estimation in the regions of Indonesia, the Philippines, and Chile

et al. 2016

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“…For modelling tsunami propagation, the numerical code JAGURS is used (Baba et al 2015(Baba et al , 2016. The code solves the nonlinear dispersive long-wave equations using a leapfrog (staggered-grid) scheme on Finite Differences (Baba et al 2015).…”

Section: Numerical Modelling Of Tsunami and Slump Sourcesmentioning

confidence: 99%

Probabilistic Landslide-Generated Tsunamis in the Indus Canyon, NW Indian Ocean, Using Statistical Emulation

Salmanidou

Heidarzadeh

Guillas

2019

Pure Appl. Geophys.

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The Indus Canyon in the northwestern Indian Ocean has been reported to be the site of numerous submarine mass failures in the past. This study is the first to investigate potential tsunami hazards associated with such mass failures in this region. We employed statistical emulation, i.e. surrogate modelling, to efficiently quantify uncertainties associated with slump-generated tsunamis at the slopes of the canyon. We simulated 60 slump scenarios with thickness of 100-300 m, width of 6-10.5 km, travel distances of 500-2000 m and submergence depth of 250-450 m. These scenarios were then used to train the emulator and predict 500,000 trial scenarios in order to study probabilistically the tsunami hazard over the near field. Due to narrow-deep canyon walls and the shallow continental shelf in the adjacent regions (\100 m water depth), the tsunami propagation has a unique pattern as an ellipse stretched in the NE-SW direction. The results show that the most likely tsunami amplitudes and velocities are approximately 0.2-1.0 m and 2.5-13 m/s, respectively, which can potentially impact vessels and maritime facilities. We demonstrate that the emulator-based approach is an important tool for probabilistic hazard analysis since it can generate thousands of tsunami scenarios in few seconds, compared to days of computations on High Performance Computing facilities for a single run of the dispersive tsunami solver that we use here.

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“…To simulate a tsunami from the fault models, we used the tsunami simulation code JAGURS (Baba et al 2016). We simulated the tsunami propagation over a period Fig.…”

Section: Simulationmentioning

confidence: 99%

A nonlinear parametric model based on a power law relationship for predicting the coastal tsunami height

et al. 2019

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When a subduction-zone earthquake occurs, the tsunami height must be predicted to cope with the damage generated by the tsunami. Therefore, tsunami height prediction methods have been studied using simulation data acquired by large-scale calculations. In this research, we consider the existence of a nonlinear power law relationship between the water pressure gauge data observed by the Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET) and the coastal tsunami height. Using this relationship, we propose a nonlinear parametric model and conduct a prediction experiment to compare the accuracy of the proposed method with those of previous methods and implement particular improvements to the extrapolation accuracy.

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Large-scale, high-speed tsunami prediction for the Great Nankai Trough Earthquake on the K computer

Cited by 31 publications

References 11 publications

Near-field tsunami forecast system based on near real-time seismic moment tensor estimation in the regions of Indonesia, the Philippines, and Chile

Near-field tsunami forecast system based on near real-time seismic moment tensor estimation in the regions of Indonesia, the Philippines, and Chile

Probabilistic Landslide-Generated Tsunamis in the Indus Canyon, NW Indian Ocean, Using Statistical Emulation

A nonlinear parametric model based on a power law relationship for predicting the coastal tsunami height

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