Probabilistic hazard for seismically induced tsunamis: Accuracy and feasibility of inundation maps

Probabilistic tsunami hazard analysis (PTHA) relies on computationally demanding numerical simulations of tsunami generation, propagation, and non-linear inundation on high-resolution topo-bathymetric models. Here we focus on tsunamis generated by co-seismic sea floor displacement, that is, on Seismic PTHA (SPTHA). A very large number of tsunami simulations are typically needed to incorporate in SPTHA the full expected variability of seismic sources (the aleatory uncertainty). We propose an approach for reducing their number. To this end, we (i) introduce a simplified event tree to achieve an effective and consistent exploration of the seismic source parameter space; (ii) use the computationally inexpensive linear approximation for tsunami propagation to construct a preliminary SPTHA that calculates the probability of maximum offshore tsunami wave height (HMax) at a given target site; (iii) apply a two-stage filtering procedure to these 'linear' SPTHA results, for selecting a reduced set of sources and (iv) calculate 'non-linear' probabilistic inundation maps at the target site, using only the selected sources. We find that the selection of the important sources needed for approximating probabilistic inundation maps can be obtained based on the offshore HMax values only. The filtering procedure is semi-automatic and can be easily repeated for any target sites. We describe and test the performances of our approach with a case study in theMediterranean that considers potential subduction earthquakes on a section of the Hellenic Arc, three target sites on the coast of eastern Sicily and one site on the coast of southern Crete. The comparison between the filtered SPTHA results and those obtained for the full set of sources indicates that our approach allows for a 75-80 per cent reduction of the number of the numerical simulations needed, while preserving the accuracy of probabilistic inundation maps to a reasonable degree.