Publication Date:
2019-03-06
Description:
Site-specific seismic probabilistic tsunami hazard analysis (SPTHA) is a computationally demanding task, as it requires, in principle, a huge number of high-resolution numerical simulations for producing probabilistic inundation maps. We implemented an efficient and robust methodology using a filtering procedure to reduce the number of numerical simulations needed while still allowing for a full treatment of aleatory and epistemic uncertainty. Moreover, to avoid biases in tsunami hazard assessment, we developed a strategy to identify and separately treat tsunamis generated by near-field earthquakes. Indeed, the coseismic deformation produced by local earthquakes necessarily affects tsunami intensity, depending on the scenario size, mechanism and position, as coastal uplift or subsidence tends to diminish or increase the tsunami hazard, respectively. Therefore, we proposed two parallel filtering schemes in the far- and the near-field, based on the similarity of offshore tsunamis and hazard curves and on the similarity of the coseismic fields, respectively. This becomes mandatory as offshore tsunami amplitudes can not represent a proxy for the coastal inundation in the case of near-field sources. We applied the method to an illustrative use case at the Milazzo oil refinery (Sicily, Italy). We demonstrate that a blind filtering procedure can not properly account for local sources and would lead to a nonrepresentative selection of important scenarios. For the specific source–target configuration, this results in an overestimation of the tsunami hazard, which turns out to be correlated to dominant coastal uplift. Different settings could produce either the opposite or a mixed behavior along the coastline. However, we show that the effects of the coseismic deformation due to local sources can not be neglected and a suitable correction has to be employed when assessing local-scale SPTHA, irrespective of the specific signs of coastal displacement.
Print ISSN:
1561-8633
Electronic ISSN:
1684-9981
Topics:
Geography
,
Geosciences
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