Probabilistic Quantification of Tsunami Currents in Karachi Port, Makran Subduction Zone, using Statistical Emulation

Gopinathan, Devaraj; Heidarzadeh, Mohammad; Guillas, Serge

doi:10.1002/essoar.10502534.2

Cited by 4 publications

(10 citation statements)

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“…The Okada equations are implemented based on the dMODELS 2 code (Battaglia et al, 2012;Battaglia et al, 2013). More details on the implementation of the slip profile and sediment amplification may be found in Gopinathan et al (2020).…”

Section: Earthquake Sourcementioning

confidence: 99%

“…An extensive error analysis of the code has also recently been carried out (Giles et al, 2020a). Owing to its computational efficiency it has been used extensively by the tsunami modeling community, in particular for tasks which require a large number of runs, such as sensitivity analyses or uncertainty quantification studies (Salmanidou et al, 2017;Gopinathan et al, 2020). The capabilities of the GPU version of the code at performing faster than real time simulations have also been recently highlighted (Giles et al, 2020b).…”

Section: Volna-op2mentioning

confidence: 99%

“…For the offshore region the cell size is dependent on the bathymetry, while in the area of interest a fixed cell size is used. Full details on the customized mesh sizing procedure can be found in Gopinathan et al (2020). For a consistent numerical method (Giles et al, 2020a), the numerical error decreases with increasing mesh resolution.…”

Section: Non-uniform Meshesmentioning

confidence: 99%

“…By providing an approximation of the simulator, along with uncertainties in its approximation to validate quality, the GP emulator allows for gains of orders of magnitude in computational costs. Tsunami GP emulation has supplied ranges of prediction for tsunamis generated by earthquakes and landslides over the North Atlantic, the Western Indian Ocean and Cascadia (Salmanidou et al, 2017;Guillas et al, 2018;Salmanidou et al, 2019;Gopinathan et al, 2020). We choose the GP emulator due to its versatility and our efficiently parallelized Multiple-Output Gaussian Process emulator (MOGP) 3 code.…”

Section: Emulatormentioning

confidence: 99%

“…Statistical surrogate models approximate the functional of more expensive deterministic models. They have been utilized successfully in a large variety of fields such as biological systems (Oyebamiji et al, 2017), climate models (Castruccio et al, 2014), atmospheric dispersion (Girard et al, 2016), or building energy models (Kristensen et al, 2017), but pertaining to this work they have been leveraged to carry out tsunami sensitivity studies and uncertainty quantification (Salmanidou et al, 2017;Guillas et al, 2018;Gopinathan et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Faster Than Real Time Tsunami Warning with Associated Hazard Uncertainties

et al. 2021

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Tsunamis are unpredictable events and catastrophic in their potential for destruction of human lives and economy. The unpredictability of their occurrence poses a challenge to the tsunami community, as it is difficult to obtain from the tsunamigenic records estimates of recurrence rates and severity. Accurate and efficient mathematical/computational modeling is thus called upon to provide tsunami forecasts and hazard assessments. Compounding this challenge for warning centres is the physical nature of tsunamis, which can travel at extremely high speeds in the open ocean or be generated close to the shoreline. Thus, tsunami forecasts must be not only accurate but also delivered under severe time constraints. In the immediate aftermath of a tsunamigenic earthquake event, there are uncertainties in the source such as location, rupture geometry, depth, magnitude. Ideally, these uncertainties should be represented in a tsunami warning. However in practice, quantifying the uncertainties in the hazard intensity (i.e., maximum tsunami amplitude) due to the uncertainties in the source is not feasible, since it requires a large number of high resolution simulations. We approximate the functionally complex and computationally expensive high resolution tsunami simulations with a simple and cheap statistical emulator. A workflow integrating the entire chain of components from the tsunami source to quantification of hazard uncertainties is developed here - quantification of uncertainties in tsunamigenic earthquake sources, high resolution simulation of tsunami scenarios using the GPU version of Volna-OP2 on a non-uniform mesh for an ensemble of sources, construction of an emulator using the simulations as training data, and prediction of hazard intensities with associated uncertainties using the emulator. Thus, using the massively parallelized finite volume tsunami code Volna-OP2 as the heart of the workflow, we use statistical emulation to compute uncertainties in hazard intensity at locations of interest. Such an integration also balances the trade-off between computationally expensive simulations and desired accuracy of uncertainties, within given time constraints. The developed workflow is fully generic and independent of the source (1945 Makran earthquake) studied here.

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Section: Earthquake Sourcementioning

confidence: 99%

Section: Volna-op2mentioning

confidence: 99%

Section: Non-uniform Meshesmentioning

confidence: 99%

Section: Emulatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Faster Than Real Time Tsunami Warning with Associated Hazard Uncertainties

et al. 2021

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Efficient probabilistic prediction of tsunami inundation considering random tsunami sources and the failure probability of seawalls

Fukutani

Yasuda

Yamanaka

2023

Stoch Environ Res Risk Assess

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Probabilistic tsunami inundation assessment ordinarily requires many inundation simulations that consider various uncertainties; thus, the computational cost is very high. In recent years, active research has been conducted to reduce the computational cost. In this study, the number of random tsunami sources was reduced to 20% of the original number by applying proper orthogonal decomposition (POD) to tsunami inundation depth distributions obtained from random tsunami sources. Additionally, the failure degree of seawalls was stochastically assessed, and its impact was incorporated into the evaluation model for tsunami inundation hazards because this factor has a significant impact on the tsunami inundation depth assessment for land areas. Although the randomness of the slip distribution in tsunami sources has been studied extensively in the past, the idea of simultaneously modelling the failure degree of seawalls is a novel feature of this study. Finally, tsunami inundation distribution maps were developed to represent the probability of occurrence of different inundation depths for the next 50 years and 10 years by using a number of tsunami inundation distributions that consider the randomness of the tsunami sources and the failure probability of the seawalls.

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Efficient probabilistic tsunami inundation prediction considering random tsunami sources and failure probability of seawalls

Fukutani

Yasuda

Yamanaka

2022

Preprint

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Probabilistic tsunami inundation assessment ordinarily requires many inundation simulations that consider various uncertainties; thus, the computational cost is very high. In recent years, active research has been conducted to reduce the computational cost. In this study, we successfully reduced the number of random tsunami sources to 20% of the original numbers by applying proper orthogonal decomposition (POD) to tsunami inundation depth distributions obtained from random tsunami sources. Additionally, we stochastically treated the failure degree of seawalls and incorporated its impact into the evaluation model for tsunami inundation hazard because the failure degree of seawalls has a significant impact on the tsunami inundation depth assessment for land areas. Although the randomness of the slip distribution in tsunami sources has been studied extensively in the past, the idea of simultaneously modelling the failure degree of seawalls is an outstanding feature of this study. Finally, we developed tsunami inundation distribution maps to represent the probability of occurrence of inundation depth for the next 50 years and 10 years by using a number of tsunami inundation distributions that consider the randomness of the tsunami sources and the failure probability of the seawalls.

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Probabilistic Quantification of Tsunami Currents in Karachi Port, Makran Subduction Zone, using Statistical Emulation

Cited by 4 publications

References 0 publications

Faster Than Real Time Tsunami Warning with Associated Hazard Uncertainties

Faster Than Real Time Tsunami Warning with Associated Hazard Uncertainties

Efficient probabilistic prediction of tsunami inundation considering random tsunami sources and the failure probability of seawalls

Efficient probabilistic tsunami inundation prediction considering random tsunami sources and failure probability of seawalls

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