Tsunami Data Assimilation Without a Dense Observation Network

Wang, Yuchen; Maeda, Takuto; Satake, Kenji; Heidarzadeh, Mohammad; Su, H.; Sheehan, A. F.; Gusman, Aditya Riadi

doi:10.1029/2018gl080930

Cited by 25 publications

(21 citation statements)

References 42 publications

(95 reference statements)

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“…However, this result may not indicate that the scenario 4 (involving two OBPGs) will give the same forecast accuracy for other tsunamis with different sizes and strike angles. Our experience (Wang et al 2019) showed that the larger the tsunami size and wavelength, the more OBPGs is required for accurate TDA. Clearly, detailed sensitivity analyses considering various earthquake scenarios (magnitude, Fig.…”

Section: Resultsmentioning

confidence: 96%

Potential deployment of offshore bottom pressure gauges and adoption of data assimilation for tsunami warning system in the western Mediterranean Sea

et al. 2019

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Western Mediterranean Basin (WMB) is among tsunamigenic zones with numerous historical records of tsunami damage and deaths. Most recently, a moderate tsunami on 21 May 2003 offshore Algeria, North Africa, was a fresh call for strengthening tsunami warning capabilities in this enclosed water basin. Here, we propose to deploy offshore bottom pressure gauges (OBPGs) and to adopt the framework of a tsunami data assimilation (TDA) approach for providing timely tsunami forecasts. We demonstrate the potential enhancement of the tsunami warning system through the case study of the 2003 Algeria tsunami. Four scenarios of OBPG arrangements involving 10, 5, 3 and 2 gauges are considered. The offshore gauges are located at distances of 120-300 km from the North African coast. The warning lead times are 20, 30, 48 and 55 min for four points of interest considered in this study: Ibiza, Palma, Sant Antoni and Barcelona, respectively. The forecast accuracies are in the range of 69-85% for the four OBPG scenarios revealing acceptable accuracies for tsunami warnings. We conclude that installation of OBPGs in the WMB can be helpful for providing successful and timely tsunami forecasts. We note that the OBPG scenarios proposed in this study are applicable only for the case of the 2003 Algeria tsunami. Further studies including sensitivity analyses (e.g., number of OBPG stations; earthquake magnitude, strike, epicenter) are required in order to determine OBPG arrangements that could be useful for various earthquake scenarios in the WMB.

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Section: Resultsmentioning

confidence: 96%

Potential deployment of offshore bottom pressure gauges and adoption of data assimilation for tsunami warning system in the western Mediterranean Sea

et al. 2019

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“…The tsunami DA method proposed by Maeda et al (2015) for simulating tsunami wavefields in real-time is based on the optimal interpolation method, which has a lower computational cost than the more advanced method using the ensemble Kalman filter, under the assumption that the system is linear. Even though the optimal interpolation method is simple, however, the DA approach showed good agreement with real tsunami data (Gusman et al 2016;Heidarzadeh et al 2019;Wang et al 2017Wang et al , 2019a and the synthetic case (Mulia et al 2017). In the numerical simulation, the tsunami wavefield at the nth time step is represented as x n η n�t, x, y , M n�t, x, y , N n�t, x, y , where η is the tsunami height, M and N are the depthintegrated tsunami fluxes in the x and y directions and t is the time step.…”

Section: Data Assimilationmentioning

confidence: 95%

“…ε f and ε O are the errors of the forward simulation and observations, respectively, while ε f T and ε OT are the corresponding transpose matrices. We assume the Gaussian-distributed errors to compute both covariance matrices, with a characteristics distance of 20 km (Maeda et al 2015;Wang et al 2019a).…”

Section: Data Assimilationmentioning

confidence: 99%

“…Even though the parameters can also be calculated using the waveform inversion method (Satake 1989), it still requires many trials and errors to find the best-fit fault configuration. With the vast deployment of offshore observational stations in recent years, many studies prefer tsunami forecasting systems that estimate sea surface deformation instead of the earthquake source mechanisms (Maeda et al 2015;Tsushima et al 2011;Wang et al 2019a). Several studies have proposed comprehensive forecasting systems that use a tsunami database to forecast tsunami inundation caused by a near-field earthquake (Fauzi and Mizutani 2020;Gusman et al 2014;Mulia et al 2018;Setiyono et al 2017).…”

Section: Introductionmentioning

confidence: 99%

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Potential of deep predictive coding networks for spatiotemporal tsunami wavefield prediction

Fauzi

Mizutani

2020

Geosci. Lett.

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Data assimilation is a powerful tool for directly forecasting tsunami wavefields from the waveforms recorded at dense observational stations like S-Net without the need to know the earthquake source parameters. However, this method requires a high computational load and a quick warning is essential when a tsunami threat is near. We propose a new approach based on a deep predictive coding network for forecasting spatiotemporal tsunami wavefields. Unlike the previous data assimilation method, which continuously computes the wavefield when observed data are available, we use only a short sequence from previously assimilated wavefields to forecast the future wavefield. Since the predictions are computed through matrix multiplication, the future wavefield can be estimated in seconds. We apply the proposed method to simple bathymetry and the 2011 Tohoku tsunami. The results show that our proposed method is very fast (1.6 s for 32 frames of prediction with 1-min interval) and comparable to the previous data assimilation. Therefore, the proposed method is promising for integration with data assimilation to reduce the computational cost.

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“…Here ε f represents the errors in numerical forecast between two grids, and ε O represents the observational errors of observation stations. We used the Gaussian‐distributed errors to compute the error covariance matrices, with a characteristic distance of 20 km (Maeda et al, ; Wang et al, ).…”

Section: Methodsmentioning

confidence: 99%

Tsunami Data Assimilation of Cabled Ocean Bottom Pressure Records for the 2015 Torishima Volcanic Tsunami Earthquake

et al. 2019

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Abnormal volcanic earthquakes occurring near the volcanic island of Torishima, south of Japan, sometimes generate relatively larger tsunamis compared to the seismic magnitudes. They have a non-double-couple focal mechanism known as compensated linear vector dipole. The unusual earthquake source mechanism poses difficulties in traditional tsunami forecasting method based on seismic parameters. Tsunami data assimilation, a method of tsunami forecast using offshore tsunami observation data and numerical model, avoids the complexities and uncertainties in the tsunami source estimation and makes a tsunami early warning at coastal points of interest. Along Nankai trough, many offshore bottom pressure gauges are installed, and the data are transmitted in real time through submarine cables. Previous synthetic experiments have demonstrated the capability of the data assimilation approach for tsunami forecasting. In this paper, we report the successful application of the tsunami data assimilation to the cabled ocean bottom pressure gauge data for the first time. We assimilated the tsunami data recorded on ocean bottom pressure gauges to retroactively forecast the tsunamis of the 2015 Torishima earthquake. Comparison of the forecasted and observed waveforms at two coastal tide gauges (Kushimoto and Tosashimizu) confirmed that our method could forecast the tsunami amplitude and arrival time accurately, merely based on offshore tsunami observations. We also investigated the relationship between the number of observational stations used for assimilation and the forecasting accuracy and determined which stations were more important in data assimilation.

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Tsunami Data Assimilation Without a Dense Observation Network

Cited by 25 publications

References 42 publications

Potential deployment of offshore bottom pressure gauges and adoption of data assimilation for tsunami warning system in the western Mediterranean Sea

Potential deployment of offshore bottom pressure gauges and adoption of data assimilation for tsunami warning system in the western Mediterranean Sea

Potential of deep predictive coding networks for spatiotemporal tsunami wavefield prediction

Tsunami Data Assimilation of Cabled Ocean Bottom Pressure Records for the 2015 Torishima Volcanic Tsunami Earthquake

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