Alternative to non-linear model for simulating tsunami inundation in real-time

Mulia, Iyan E.; Gusman, Aditya Riadi; Satake, Kenji

doi:10.1093/gji/ggy238

Cited by 22 publications

(25 citation statements)

References 41 publications

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“…To test the proposed method, we use the same tsunami source scenarios as in Gusman et al (2014) and Mulia et al (2018) (Figure 1a). A total of 56 points as the top center of the assumed faults is distributed along the Japan Trench, with the overall moment magnitude ranging between 8.0 and 9.0 (0.1 interval).…”

Section: Precalculated Scenariosmentioning

confidence: 99%

“…Contrarily, software solutions to the aforesaid problem include techniques based on precalculation (Greenslade & Titov, 2008; Tang et al, 2009; Titov, 2009). The use of a precalculated tsunami inundation database can be an alternative for those with limited computational resources (Allen & Greenslade, 2010; Bernard & Titov, 2015; Gusman et al, 2014; Mulia et al, 2018; Setiyono et al, 2017). Gusman et al (2014) and Setiyono et al (2017) analyzed misfits between precalculated and real‐time simulated tsunami waveforms as a basis to search for matching inundation scenarios in the database.…”

Section: Introductionmentioning

confidence: 99%

“…Gusman et al (2014) and Setiyono et al (2017) analyzed misfits between precalculated and real‐time simulated tsunami waveforms as a basis to search for matching inundation scenarios in the database. More recently, a method proposed by Mulia et al (2018) produced high‐resolution inundation forecasts based on spatial patterns of maximum simulated tsunami elevations in a low‐resolution domain. Here, we improve upon such studies by taking advantage of neural networks modeling and deep learning applied to the same type of database, similar to a study by Fauzi and Mizutani (2019).…”

Section: Introductionmentioning

confidence: 99%

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Applying a Deep Learning Algorithm to Tsunami Inundation Database of Megathrust Earthquakes

2020

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We explore recent developments in computer science on deep learning to estimate high-resolution tsunami inundation from a quick low-resolution computation result. Deep network architecture is capable of storing large information acquired via a training/learning process by pairing low-and high-resolution deterministic simulation results from precalculated hypothetical scenarios. In a real case, with a real-time source estimate and linear simulation computed on a relatively low-grid resolution, optimized network parameters can be used to rapidly and accurately transform low-resolution simulation outputs to higher-resolution grids. We generate 532 source scenarios of interplate earthquakes (Mw 8-9) along the Japan Trench subduction zone to simulate tsunamis and utilize a precalculated library for deep learning. To test the proposed method, we consider a realistic source model inferred from static ground displacements and offshore tsunami data presumably available in real-time and compare forecasted inundation heights against observations in Rikuzentakata and Otsuchi cities associated with the 2011 Tohoku-oki tsunami. Our results show that the proposed method exhibits comparable accuracy to the conventional physics-based simulation but achieves approximately 90% reduction of real-time computational efforts. Thus, it has good potential as a future tsunami forecasting algorithm.

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Section: Precalculated Scenariosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Applying a Deep Learning Algorithm to Tsunami Inundation Database of Megathrust Earthquakes

2020

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“…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). However, these studies are not free from uncertainty in estimating the tsunami source.…”

Section: Introductionmentioning

confidence: 99%

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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“…Even after hav-ing a finite fault model (FFM), the simulation of the tsunami propagation can take several hours depending on the desired level of resolution. This is the reason why the tsunami forecasts of many of the current warning systems are based on pre-computed scenarios (Reymond et al, 2012;Gusman et al, 2014;Mulia et al, 2018). Chile and Japan use this methodology for that purpose (https://www.jma.go.jp/jma/ en/News/lists/tsunamisystem2006mar.pdf, last access: 5 December 2018).…”

Section: Introductionmentioning

confidence: 99%

Speeding up tsunami forecasting to boost tsunami warning in Chile

Fuentes

Arriola

Riquelme

et al. 2019

Nat. Hazards Earth Syst. Sci.

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Abstract. Despite the occurrence of several large earthquakes during the last decade, Chile continues to have a great tsunamigenic potential. This arises as a consequence of the large amount of strain accumulated along a subduction zone that runs parallel to its long coast, and a distance from the trench to the coast of no more than 100 km. These conditions make it difficult to implement real-time tsunami forecasting. Chile issues local tsunami warnings based on preliminary estimations of the hypocenter location and magnitude of the seismic sources, combined with a database of pre-computed tsunami scenarios. Finite fault modeling, however, does not provide an estimation of the slip distribution before the first tsunami wave arrival, so all pre-computed tsunami scenarios assume a uniform slip distribution. We implemented a processing scheme that minimizes this time gap by assuming an elliptical slip distribution, thereby not having to wait for the more time-consuming finite fault model computations.We then solve the linear shallow water equations to obtain a rapid estimation of the run-up distribution in the near field. Our results show that, at a certain water depth, our linear method captures most of the complexity of the run-up heights in terms of shape and amplitude when compared with a fully nonlinear tsunami model. In addition, we can estimate the run-up distribution in quasi-real-time as soon as the results of seismic finite fault modeling become available.

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Alternative to non-linear model for simulating tsunami inundation in real-time

Cited by 22 publications

References 41 publications

Applying a Deep Learning Algorithm to Tsunami Inundation Database of Megathrust Earthquakes

Applying a Deep Learning Algorithm to Tsunami Inundation Database of Megathrust Earthquakes

Potential of deep predictive coding networks for spatiotemporal tsunami wavefield prediction

Speeding up tsunami forecasting to boost tsunami warning in Chile

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