Successive estimation of a tsunami wavefield without earthquake source data: A data assimilation approach toward real‐time tsunami forecasting

Maeda, Takuto; Obara, Kazushige; Shinohara, Masanao; Kanazawa, Toshihiko; Uehira, Kenji

doi:10.1002/2015gl065588

Cited by 127 publications

(158 citation statements)

References 27 publications

Supporting

Mentioning

151

Contrasting

Order By: Relevance

“…Recently, Maeda et al (2015) have developed a method to forecast coastal tsunamis by assimilating tsunami observations continuously without information on the initial ocean surface displacement. Gusman et al (2016) applied this method to the tsunami generated by the 2012 Haida Gwaii earthquake and computed the tsunami wave field successfully by assimilating the tsunami waveforms observed at the ocean bottom pressure gauge network in Cascadia.…”

Section: Introductionmentioning

confidence: 99%

Tsunami Simulation Method Assimilating Ocean Bottom Pressure Data Near a Tsunami Source Region

Tanioka

2017

Pure Appl. Geophys.

View full text Add to dashboard Cite

Abstract-A new method was developed to reproduce the tsunami height distribution in and around the source area, at a certain time, from a large number of ocean bottom pressure sensors, without information on an earthquake source. A dense cabled observation network called S-NET, which consists of 150 ocean bottom pressure sensors, was installed recently along a wide portion of the seafloor off Kanto, Tohoku, and Hokkaido in Japan. However, in the source area, the ocean bottom pressure sensors cannot observe directly an initial ocean surface displacement. Therefore, we developed the new method. The method was tested and functioned well for a synthetic tsunami from a simple rectangular fault with an ocean bottom pressure sensor network using 10 arc-min, or 20 km, intervals. For a test case that is more realistic, ocean bottom pressure sensors with 15 arc-min intervals along the north-south direction and sensors with 30 arc-min intervals along the east-west direction were used. In the test case, the method also functioned well enough to reproduce the tsunami height field in general. These results indicated that the method could be used for tsunami early warning by estimating the tsunami height field just after a great earthquake without the need for earthquake source information.

show abstract

Section: Introductionmentioning

confidence: 99%

Tsunami Simulation Method Assimilating Ocean Bottom Pressure Data Near a Tsunami Source Region

Tanioka

2017

Pure Appl. Geophys.

View full text Add to dashboard Cite

show abstract

“…We performed the assimilation of the LP wavefield observed in the strong-motion networks using the optimum interpolation technique (e.g., Kalnay, 2003), which has been applied to shaking intensity (Hoshiba & Aoki, 2015) and tsunami (Gusman et al, 2016;Maeda et al, 2015;Wang et al, 2017) forecasts.…”

Section: Data Assimilation Of the Observed Lp Wavefield Via 3-d Fdm Smentioning

confidence: 99%

“…A code for calculating the weight matrix of the optimum interpolation can be found at GitHub (https:// github.com/takuto-maeda/tdac) together with the tsunami data assimilation model (Maeda et al, 2015). bosai.go.jp).…”

Section: Acknowledgmentsmentioning

confidence: 99%

Early Forecast of Long‐Period Ground Motions via Data Assimilation of Observed Ground Motions and Wave Propagation Simulations

Furumura

Maeda

Oba

2019

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

We propose an efficient approach for the early forecast of long‐period (> 3–10 s) ground motions generated in sedimentary basins by large earthquakes based on the data assimilation of observed ground motions and finite‐difference method simulations of seismic wave propagation in a 3‐D heterogeneous structure. This approach uses the dense K‐NET and KiK‐net nationwide networks in Japan and a high‐performance supercomputer to perform forecasts using the assimilated wavefields at speeds much faster than the actual wave propagation speed. Therefore, an early alert can be issued prior to the occurrence of strong motions in basins due to large, distant earthquakes. We validated the effectiveness of this data‐assimilation‐based forecast approach via numerical tests for the early forecast of long‐period ground motions in central Tokyo using the observed waveform data from the Mw6.6 2007 Off Niigata and Mw9.0 2011 Off Tohoku earthquakes.

show abstract

“…Tsunami forecast systems that use offshore tsunami observations have been developed by the National Oceanic and Atmospheric Administration (NOAA) (e.g., Tang et al 2009Tang et al , 2012 and the Japan Meteorological Agency (JMA) (e.g., Tsushima et al 2009Tsushima et al , 2011Tsushima et al , 2012. Recently, other reliable forecast methods that use dense offshore observation networks have been also suggested (Baba et al 2014;Maeda et al 2015). Forecast systems using tsunami observation can be reliable but require substantially longer times, because it typically takes tens of minutes for a tsunami to reach to the nearest offshore stations (e.g., Tang et al 2012;Wei et al 2013).…”

Section: Introductionmentioning

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

View full text Add to dashboard Cite

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).

show abstract

Successive estimation of a tsunami wavefield without earthquake source data: A data assimilation approach toward real‐time tsunami forecasting

Cited by 127 publications

References 27 publications

Tsunami Simulation Method Assimilating Ocean Bottom Pressure Data Near a Tsunami Source Region

Tsunami Simulation Method Assimilating Ocean Bottom Pressure Data Near a Tsunami Source Region

Early Forecast of Long‐Period Ground Motions via Data Assimilation of Observed Ground Motions and Wave Propagation Simulations

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

Contact Info

Product

Resources

About