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Abstract
Offshore probabilistic tsunami hazard assessments (PTHAs) are increasingly available for earthquake generated tsunamis. They provide standardized representations of tsunami scenarios, their uncertain occurrence-rates, and models of the deep ocean waveforms. To quantify onshore hazards it is natural to combine this information with a site-specific inundation model, but this is computationally challenging to do accurately, especially if accounting for uncertainties in the offshore PTHA. This study reviews an efficient Monte Carlo method recently proposed to solve this problem. The efficiency comes from preferential sampling of scenarios that are likely important near the site of interest, using a user-defined importance measure derived from the offshore PTHA. The theory of importance sampling enables this to be done without biasing the final results. Techniques are presented to help design and test Monte Carlo schemes for a site of interest (before inundation modelling) and to quantify errors in the final results (after inundation modelling). The methods are illustrated with examples from studies in Tongatapu and Western Australia.References
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Copyright (c) 2023 Gareth Davies