Parallel Implementation of Dispersive Tsunami Wave Modeling with a Nesting Algorithm for the 2011 Tohoku Tsunami

Baba, Toshitaka; Takahashi, Narumi; Kaneda, Yasufumi; Ando, Kazuto; Matsuoka, Daisuke; Kato, Toshihiro

doi:10.1007/s00024-015-1049-2

Cited by 181 publications

(132 citation statements)

References 35 publications

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“…The nonlinear tsunami simulation assumes a constant coefficient (0.025) for the bottom friction (Adriano et al 2016a). The combination of dispersion and wave nonlinearity generates that the wave front of a tsunami propagating on a shallow bathymetry becomes steep (Baba et al 2015). These nonlinear effects are not included in our simulation.…”

Section: Tsunami Simulation Modelmentioning

confidence: 99%

Tsunami Source Inversion Using Tide Gauge and DART Tsunami Waveforms of the 2017 Mw8.2 Mexico Earthquake

Adriano

Fujii

Koshimura

et al. 2017

Pure Appl. Geophys.

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Abstract-On September 8, 2017 (UTC), a normal-fault earthquake occurred 87 km off the southeast coast of Mexico. This earthquake generated a tsunami that was recorded at coastal tide gauge and offshore buoy stations. First, we conducted a numerical tsunami simulation using a single-fault model to understand the tsunami characteristics near the rupture area, focusing on the nearby tide gauge stations. Second, the tsunami source of this event was estimated from inversion of tsunami waveforms recorded at six coastal stations and three buoys located in the deep ocean. Using the aftershock distribution within 1 day following the main shock, the fault plane orientation had a northeast dip direction (strike ¼ 320 , dip ¼ 77 , and rake ¼ À92 ). The results of the tsunami waveform inversion revealed that the fault area was 240 km 9 90 km in size with most of the largest slip occurring on the middle and deepest segments of the fault. The maximum slip was 6.03 m from a 30 9 30 km 2 segment that was 64.82 km deep at the center of the fault area. The estimated slip distribution showed that the main asperity was at the center of the fault area. The second asperity with an average slip of 5.5 m was found on the northwest-most segments. The estimated slip distribution yielded a seismic moment of 2:9 Â 10 21 Nm (Mw = 8.24), which was calculated assuming an average rigidity of 7 Â 10 10 N/m 2 .

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Section: Tsunami Simulation Modelmentioning

confidence: 99%

Tsunami Source Inversion Using Tide Gauge and DART Tsunami Waveforms of the 2017 Mw8.2 Mexico Earthquake

Adriano

Fujii

Koshimura

et al. 2017

Pure Appl. Geophys.

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“…In the present study, we used the framework of a global tsunami simulation Saito 2013, 2016), but the simulations will be suitably carried out in the regional domain in the vicinity of the SWIFT seismic networks in Indonesia/the Philippines, and Chile, in order to efficiently reduce the computation time with the high grid resolution. Tsunami simulation modeling that includes nonlinear advection with inundation, seafloor friction, and wave dispersion may be also necessary for accurate forecast although the comprehensive modeling requires additional computational time (e.g., Saito et al 2014;Miyoshi et al 2015;Baba et al 2015). A calculated map of the inundation area with height will be required for the forecast products as well.…”

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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“…1 We input the computed crustal deformation data to the nonlinear shallow water equation below and perform hydrodynamic computation of tsunami [Saito and Furumura, 2009;Baba et al, 2015].…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we use the¯nite di®erence scheme with the spherical coordinate system, leapfrog scheme, and staggered grid for the discretizing of Eq. (2) [Baba et al, 2015]. The code is parallelized by hybrid parallelization of MPI & OpenMP, and it enables the rapid performance of tsunami analyses.…”

Section: Methodsmentioning

confidence: 99%

Tsunami Analysis Method with High-Fidelity Crustal Structure and Geometry Model

Ichimura

Agata

Hori

et al. 2017

J. Earthquake and Tsunami

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Higher¯delity sea°oor topography and crustal structure models have become available with accumulation of observation data. Previous studies have shown that the consideration of such high-¯delity models produces signi¯cant e®ects, in some cases, on crustal deformation results that are used as inputs for tsunami analysis. However, it is di±cult to apply high-¯delity model of crustal deformation computations to tsunami computations because of large computational costs. In this paper, we propose a new crustal deformation computation method for estimating inputs for tsunami computations, which is based on a¯nite element analysis method with remarkable reduction of computation costs by e±cient use of the arithmetic space and the solution space. This¯nite element analysis method enables us to conduct 10 2À3 -times crustal deformation computations using high-¯delity models with a degree of freedom on the order of 10 8 for the 2011 Tohoku earthquake example. Tsunami computations with typical settings are conducted as an application example to present the advantages and characteristics of the proposed method. Comparisons between results of the proposed and the conventional method

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Parallel Implementation of Dispersive Tsunami Wave Modeling with a Nesting Algorithm for the 2011 Tohoku Tsunami

Cited by 181 publications

References 35 publications

Tsunami Source Inversion Using Tide Gauge and DART Tsunami Waveforms of the 2017 Mw8.2 Mexico Earthquake

Tsunami Source Inversion Using Tide Gauge and DART Tsunami Waveforms of the 2017 Mw8.2 Mexico Earthquake

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

Tsunami Analysis Method with High-Fidelity Crustal Structure and Geometry Model

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