Abstract
This paper illustrates how an emulator (or meta-model) of a tsunami code can be a useful tool to evaluate or qualify tsunami hazard levels associated with both specific and unknown tsunamigenic seismic sources. The meta-models are statistical tools permitting to drastically reduce the computational time necessary for tsunami simulations. As a consequence they can be used to explore the tsunamigenic potential of a seismic zone, by taking into account an extended set of tsunami scenarios. We illustrate these concepts by studying the tsunamis generated by the Azores-Gibraltar Plate Boundary (AGPB) and potentially impacting the French Atlantic Coast. We first analyze the impact of two realistic scenarios corresponding to potential sources of the 1755-Lisbon tsunami (when uncertainty on seismic parameters is considered). We then show how meta-models could permit to qualify the tsunamis generated by this seismic area. All the results are finally discussed in light of tsunami hazard issued by the TSUMAPS-NEAM research project available online (http://ai2lab.org/tsumapsneam/interactive-hazard-curve-tool/). From this methodological study, it appears that tsunami hazard issued by TSUMAPS-NEAM research project is envelop, even when compared to all the likely and unlikely tsunami scenarios generated in the AGPB area.
Highlights
The evaluation of tsunami impact requires accurate simulation results for planning and risk assessment purposes because of the severe consequences which could be associated to this kind of event
The research work presented in this paper was performed in order to test the interest of uncertainty quantification (UQ) for the analysis and the qualification of the deterministic tsunami hazard assessment (DTHA) generated by earthquakes
Tsunami hazard evaluated through UQ can permit the exploration of tsunamigenic potential of a poorly known seismic zone, as well as a qualification of probabilistic tsunami hazard assessment (PTHA)
Summary
The evaluation of tsunami impact requires accurate simulation results for planning and risk assessment purposes because of the severe consequences which could be associated to this kind of event. Considering that tsunami phenomena involve a large span of parameters at different spatial and temporal scales (Behrens and Dias, 2015), even a single run of a tsunami numerical model can Abbreviations: μ, shear modulus; [N(m(x), s2(x))], Gaussian process of mean “m(x)” and variance “s2(x)”; [M(x)], kriging surrogate; {X,Y}, are the coordinates of the design simulations used for kriging parameters evaluation; C(.), covariance kernel; CEA, commissariat à l’énergie atomique et aux énergies alternatives; D [m], average slip along the rupture surface; DTHA, deterministic tsunami hazard assessment; GSA, global sensitivity analysis; L [m], length of the rupture surface; MCS, maximum credible scenario; MCS_h, tsunami hazard level issued by an exploration of a very wide range of tsunamigenic scenarios; Mo, seismic moment; Mw, seismic moment magnitude; MSE, mean squared error; PTHA, probabilistic tsunami hazard assessment; R2, squared correlation coefficient; RMSE, root mean squared error; UQ, uncertainty quantifications; W [m], width of the rupture surface. A comprehensive review of the use of meta-models in environmental research was proposed by Razavi et al (2012a) for the interested reader
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