Automated Approaches for Capturing Localized Tsunami Response-Application to the French Coastlines

Local bathymetry and onshore features can have a substantial effect on the spatial variability of the hazard from an incoming tsunami. In a warning context, being able to provide localized tsunami forecasts at strategic locations would therefore help mitigate the damage. Despite the recent advancements in computing powers and the development of highly efficient tsunami codes, capturing this local variability can oftentimes be infeasible in a warning setting. Traditional high-resolution simulations which can capture these localized effects are often too costly to run on-the-fly. Alternative approaches that capture the localized response to an incoming tsunami, which are based upon using the maximum wave heights from a computationally cheap regional forecast, are developed here. These alternative approaches are envisaged to aid in a warning center's ability at providing extremely rapid localized forecasts. The approaches focus upon two different methods: transfer functions and machine learning techniques. The transfer function is based upon a recent extension to the established Green's Law. The extended version introduces local amplification parameters, with the aim of capturing the neglected localized effects. An automated approach which optimizes for these local amplification parameters is outlined and the performance of the transfer function is explored. A machine learning model is also trained and used to predict the localized tsunami hazard. Its performance is compared to the extended Green's Law approach for a site along the French coast. These developed methods showcase promising techniques that a tsunami warning center could use to provide high-resolution warnings.

Automated summary

This study investigates how to capture the spatial variability of an incoming tsunami and proposes two alternative methods: transfer functions and machine learning techniques. The transfer function is based on an extension of Green's Law that introduces local amplification parameters. A machine learning model is trained to predict the localized tsunami hazard. These methods aid in providing high-resolution tsunami warnings.