Time reverse imaging for far‐field tsunami forecasting: 2011 Tohoku earthquake case study

Hossen, M. J.; Cummins, Phil R.; Dettmer, Jan; Baba, Toshitaka

doi:10.1002/2015gl065868

Cited by 26 publications

(34 citation statements)

References 41 publications

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“…Here we extend the work of Hossen, Cummins, Dettmer et al () by developing an efficient but stable algorithm to determine the amplitude of each of a set of point sea surface displacements by taking into account not only autocorrelations but also cross correlations among GFs from all the source grid points s i . These grid points are centered on “source patches” forming a rectangular tessellation of source region chosen so that it encompasses the rupture area of the earthquake.…”

Section: Theorymentioning

confidence: 99%

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Complete Implementation of the Green's Function Based Time Reverse Imaging and Sensitivity Analysis of Reversed Time Tsunami Source Inversion

Hossen

Cummins

Satake

2017

Geophysical Research Letters

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In recent studies, the Time Reverse Imaging (TRI) method has been implemented in tsunami science to estimate tsunami source models. A TRI algorithm called Green's Function Based Time Reverse Imaging (GFTRI) was previously developed to reconstruct the source model by using source inversion as a guide to appropriately scale time‐reversed images of the tsunami source. In this article, we consider a more complete approach to source inversion using reversed time images of the tsunami source by considering cross correlations among Green's functions. As a consequence, the performance of the method improves significantly. We tested this new algorithm using data from the 2011 Japan tsunami. Our results show that the method is capable of extracting more details of the source and providing excellent waveform fits at all stations, including those not used in the source imaging. We have also studied the sensitivity analysis of reversed time tsunami source inversion and found that the method is less sensitive to the number of stations, once a minimum number of stations is utilized. Moreover, this new approach is able to estimate the tsunami source with reasonable accuracy using data available soon after an earthquake, which indicates that it has potential to be used in both near‐ and far‐field tsunami forecasting.

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Section: Theorymentioning

confidence: 99%

“…In Hossen, Cummins, Dettmer et al (), it is assumed that the sources at s i and

s_{i^{'}}

are sufficiently separated in space that there is zero correlation between their respective GFs. But they are not actually that well separated; instead, they are overlapped by 50% because of the cosine‐tapered box function used to create them.…”

Section: Theorymentioning

confidence: 99%

Complete Implementation of the Green's Function Based Time Reverse Imaging and Sensitivity Analysis of Reversed Time Tsunami Source Inversion

Hossen

Cummins

Satake

2017

Geophysical Research Letters

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“…Various methods have been applied in the past for early tsunami warning, namely, the Method of Splitting Tsunami model (Titov et al, 2005), tsunami Forecasting based on Inversion for initial sea-Surface Height (Tsushima et al, 2009), Near-field Tsunami Inundation Forecasting (Gusman et al, 2014), and Time Reverse Imaging (Hossen et al, 2015). Various methods have been applied in the past for early tsunami warning, namely, the Method of Splitting Tsunami model (Titov et al, 2005), tsunami Forecasting based on Inversion for initial sea-Surface Height (Tsushima et al, 2009), Near-field Tsunami Inundation Forecasting (Gusman et al, 2014), and Time Reverse Imaging (Hossen et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Tsunami Data Assimilation Without a Dense Observation Network

Wang

Maeda

Satake

et al. 2019

Geophysical Research Letters

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The tsunami data assimilation method enables tsunami forecasting directly from observations, without the need of estimating tsunami sources. However, it requires a dense observation network to produce desirable results. Here we propose a modified method of tsunami data assimilation for regions with a sparse observation network. The method utilizes interpolated waveforms at virtual stations. The tsunami waveforms at the virtual stations between two existing observation stations are estimated by shifting arrival times with the linear interpolation of observed arrival times and by correcting the amplitudes for their water depths. In our new data assimilation approach, we employ the Optimal Interpolation algorithm to both the real observations and virtual stations, in order to construct a complete wavefront of tsunami propagation. The application to the 2004 Sumatra‐Andaman earthquake and the 2009 Dusky Sound, New Zealand, earthquake reveals that addition of virtual stations greatly helps improve the tsunami forecasting accuracy.

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“…We used the following two-dimensional cosine function (Hossen et al 2015b) for the initial seasurface height:…”

Section: Smooth Bathymetry Modelmentioning

confidence: 99%

An effective absorbing boundary condition for linear long-wave and linear dispersive-wave tsunami simulations

2016

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We numerically simulated the propagation of tsunami waves with finite difference methods by using perfectly matched layer (PML) boundary conditions to effectively eliminate artificial reflections from model boundaries. The PML method damps the tsunami height and velocity of seawater only in directions perpendicular to the boundary. Although the additional terms required to implement the PML conditions make the use of the PML technique difficult for linear dispersive tsunami waves, we have proposed an empirical extension of the PML method for modeling dispersive tsunami waves. Even for heterogeneous, realistic bathymetries, numerical tests demonstrated that the PML boundary condition dramatically decreased artificial reflections from model boundaries compared to the use of traditional boundary conditions. The use of PML boundary conditions for numerical modeling of tsunamis is especially useful because it facilitates use of the later phases of tsunamis that would otherwise be compromised by artifacts caused by reflections from model boundaries.

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Time reverse imaging for far‐field tsunami forecasting: 2011 Tohoku earthquake case study

Cited by 26 publications

References 41 publications

Complete Implementation of the Green's Function Based Time Reverse Imaging and Sensitivity Analysis of Reversed Time Tsunami Source Inversion

Complete Implementation of the Green's Function Based Time Reverse Imaging and Sensitivity Analysis of Reversed Time Tsunami Source Inversion

Tsunami Data Assimilation Without a Dense Observation Network

An effective absorbing boundary condition for linear long-wave and linear dispersive-wave tsunami simulations

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