Faster Than Real Time Tsunami Warning with Associated Hazard Uncertainties

Abstract

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.

Probabilistic tsunami forecasting for early warning

“…Following the 2004 Indian Ocean tsunami, the cost of "insist(ing) on certainty" was highlighted 33 . Despite subsequent attempts to define methods to quantify tsunami forecast uncertainty [34][35][36][37][38] , operational tsunami forecasting in TEWSs is still nonprobabilistic (http://www.ioc-tsunami.org/). Specifically, Tsunami Service Providers (TSPs) worldwide adopt Decision Matrices (DMs, look-up tables linking earthquake parameters with alert levels) or Envelopes (ENVs, selecting a local maximum over a selection of scenarios), or consider one or a few Best-Matching Scenarios (BMSs, scenarios matching the seismic and/ or tsunami data available at the time of the estimation) to define initial alert levels 16,32,[39][40][41][42][43][44][45] .…”

Probabilistic Tsunami Hazard and Risk Analysis: A Review of Research Gaps

“…However, the fastest HPC simulation workflows (e.g., de la Asunción et al, 2013;Oishi et al, 2015;Macías et al, 2017;Musa et al, 2018) still require typically 10-60 min to simulate tsunami inundation at a scale of tens of meters, rendering them unsuitable for extensive PTRA studies with up to millions of scenarios (Basili et al, 2021). To overcome this "challenge of scales", modeling approximations are presently necessary for PTHA feasibility and can either involve 1) largely reducing the number of inundation scenarios (e.g., González et al, 2009;Lorito et al, 2015;Volpe et al, 2019;Williamson et al, 2020), 2) use of approximate models or statistics such as amplification factors (e.g., Løvholt et al, 2012;Kriebel et al, 2017;Gailler et al, 2018;Glimsdal et al, 2019), or 3) machine learning-based tsunami emulators (e.g., Sarri et al, 2012;Salmanidou et al, 2017;Giles et al, 2020).…”

Rapid and Quantitative Uncertainty Estimation of Coseismic Slip Distribution for Large Interplate Earthquakes Using Real-time GNSS Data and Its Application to Tsunami Inundation Prediction

“…The Japan Meteorological Agency (JMA) introduced the announcement of tsunami warnings with more qualitative expressions (JMA 2012). In addition, several studies have shown a method for adding uncertainty information to tsunami prediction (e.g., Sraj et al 2014;Fukutani et al 2015;Dettmer et al 2016;Goda and Song 2016;Gibbons et al 2020;Goda et al 2020;Mulia et al 2020;Giles et al 2021). Dettmer et al (2016) evaluated the uncertainty of the initial sea surface displacement in the 2011 Tohoku-Oki earthquake deduced from tsunami waveforms using a transdimensional algorithm based on a wavelet decomposition of the displacement field.…”

“…Goda et al (2020) presented an extensive tsunami hazard assessment for Nankai-Tonankai trough events using 1000 kinematic earthquake rupture models for Monte Carlo tsunami simulations. Giles et al (2021) proposed a workflow that integrates the entire chain of components from the tsunami source to quantify hazard uncertainties by approximating the functionally complex and computationally expensive high-resolution tsunami simulations with a simple and cheap statistical emulator. On the other hand, in the damage estimation system due to tsunami inundation, it is currently difficult to evaluate the impact of the uncertainty of fault models on tsunami prediction.…”