Rapid estimation of tsunami source centroid location using a dense offshore observation network

Yamamoto, Naotaka; Hirata, Kenji; Aoi, Shin; Suzuki, Wataru; Nakamura, Hiromitsu; Kunugi, Taiseki

doi:10.1002/2016gl068169

Cited by 23 publications

(24 citation statements)

References 11 publications

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“…To address these issues, one can directly measure tsunamis using ocean bottom pressure gauges (OBPGs) that stream real‐time data. Japan has such networks along the Japan Trench (S‐NET; Yamamoto et al, ) and the Nankai Trough subduction zone (DONET; Kawaguchi et al, ). The Cascadia subduction zone has instruments from Northeast Pacific Time‐Series Undersea Networked Experiments (NEPTUNE)‐Canada (Thomson et al, ) and Ocean Observatories Initiative cabled arrays (Toomey et al, ).…”

Section: Introductionmentioning

confidence: 99%

Tsunami Wavefield Reconstruction and Forecasting Using the Ensemble Kalman Filter

Yang

Dunham

Barnier

et al. 2019

Geophysical Research Letters

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Offshore sensor networks like DONET and S‐NET, providing real‐time estimates of wave height through measurements of pressure changes along the seafloor, are revolutionizing local tsunami early warning. Data assimilation techniques, in particular, optimal interpolation (OI), provide real‐time wavefield reconstructions and forecasts. Here we explore an alternative assimilation method, the ensemble Kalman filter (EnKF), and compare it to OI. The methods are tested on a scenario tsunami in the Cascadia subduction zone, obtained from a 2‐D coupled dynamic earthquake and tsunami simulation. Data assimilation uses a 1‐D linear long‐wave model. We find that EnKF achieves more accurate and stable forecasts than OI, both at the coast and across the entire domain, especially for large station spacing. Although EnKF is more computationally expensive than OI, with development in high‐performance computing, it is a promising candidate for real‐time local tsunami early warning.

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Section: Introductionmentioning

confidence: 99%

Tsunami Wavefield Reconstruction and Forecasting Using the Ensemble Kalman Filter

Yang

Dunham

Barnier

et al. 2019

Geophysical Research Letters

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“…High‐density cabled networks with real‐time data transmission have been installed around Japan, providing data for tsunami data assimilation, for example, the Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET), which is a submarine cabled real‐time seafloor observatory network located around the Nankai trough (Kawaguchi et al, ). Another example is the Seafloor Observation Network for Earthquakes and Tsunamis (S‐net), which is a new network of cabled pressure gauges that is being installed around the Japan Trench (Uehira et al, ; Yamamoto et al, ). In the Cascadia subduction zone, the Cascadia Initiative Community Experiment deployed an array of pressure gauges on the seafloor (Incorporated Research Institutions for Seismology Ocean Bottom Seismograph Instrument Pool, ; Sheehan et al, ); although this network did not send data in real time, the recorded tsunami data can be used to evaluate tsunami forecasting methods (Gusman, Mulia, et al, ).…”

Section: Introductionmentioning

confidence: 99%

Green's Function‐Based Tsunami Data Assimilation: A Fast Data Assimilation Approach Toward Tsunami Early Warning

Wang

Satake

Maeda

et al. 2017

Geophysical Research Letters

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We propose a new tsunami data assimilation approach based on Green's functions to reduce the computation time for tsunami early warning. Green's Function‐based Tsunami Data Assimilation (GFTDA) forecasts the waveforms at points of interest (PoIs) by superposition of Green's functions between observation stations and PoIs. Unlike the previous assimilation approach, GFTDA does not require the calculation of the tsunami wavefield for the whole region during the assimilation process, because the Green's functions have been calculated in advance. The forecasted waveforms can be calculated by a simple matrix manipulation. The application to the tsunami waveforms recorded by the bottom pressure gauges of the Cascadia Initiative from the 2012 Haida Gwaii earthquake reveals that GFTDA achieves the same accuracy as the previous assimilation approach while reducing the time required to issue a valid tsunami warning.

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“…They successfully forecast the coastal tsunami height within 10 min after the occurrence of an earthquake by concentrating on predicting the tsunami scale. In addition, Yamamoto et al (2016) proposed a rapid method to estimate the tsunami source location using a dense offshore observation network. They claimed that this location could be estimated within a few minutes after the occurrence of an earthquake by using the tsunami centroid location (TCL).…”

Section: Introductionmentioning

confidence: 99%

“…Although direct measurements of tsunamis are used in their method, the required estimation time is comparable to that achieved using real-time GNSS data. It is possible to replace the earthquake location and magnitude with the tsunami source location estimated by Yamamoto et al (2016) and the tsunami scale estimated by Baba et al (2014) for the initial tsunami warning. Moreover, Maeda et al (2015) proposed a tsunami forecast method based on a data assimilation technique using a dense offshore observation network rather than using seismic source parameters or the initial height of the sea surface.…”

Section: Introductionmentioning

confidence: 99%

Multi-index method using offshore ocean-bottom pressure data for real-time tsunami forecast

et al. 2016

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We developed a real-time tsunami forecast method using only pressure data collected from the bottom of the ocean via a dense offshore observation network. The key feature of the method is rapid matching between offshore tsunami observations and pre-calculated offshore tsunami spatial distributions. We first calculate the tsunami waveforms at offshore stations and the maximum coastal tsunami heights from any possible tsunami source model and register them in the proposed Tsunami Scenario Bank (TSB). When a tsunami occurs, we use multiple indices to quickly select dozens of appropriate tsunami scenarios that can explain the offshore observations. At the same time, the maximum coastal tsunami heights coupled with the selected tsunami scenarios are forecast. We apply three indices, which are the correlation coefficient and two kinds of variance reductions normalized by the L2-norm of either the observation or calculation, to match the observed spatial distributions with the pre-calculated spatial distributions in the TSB. We examine the ability of our method to select appropriate tsunami scenarios by conducting synthetic tests using a scenario based on "pseudo-observations. " For these tests, we construct a tentative TSB, which contains tsunami waveforms at locations in the Seafloor Observation Network for Earthquakes and Tsunamis along the Japan Trench and maximum coastal tsunami heights, using about 2000 tsunami source models along the Japan Trench. Based on the test results, we confirm that the method can select appropriate tsunami scenarios within a certain precision by using the two kinds of variance reductions, which are sensitive to the tsunami size, and the correlation coefficient, which is sensitive to the tsunami source location. In this paper, we present the results and discuss the characteristics and behavior of the multi-index method. The addition of tsunami inundation components to the TSB is expected to enable the application of this method to real-time tsunami inundation forecasts in the near future.

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Rapid estimation of tsunami source centroid location using a dense offshore observation network

Cited by 23 publications

References 11 publications

Tsunami Wavefield Reconstruction and Forecasting Using the Ensemble Kalman Filter

Tsunami Wavefield Reconstruction and Forecasting Using the Ensemble Kalman Filter

Green's Function‐Based Tsunami Data Assimilation: A Fast Data Assimilation Approach Toward Tsunami Early Warning

Multi-index method using offshore ocean-bottom pressure data for real-time tsunami forecast

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