Literature DB >> 26392612

New computational methods in tsunami science.

J Behrens1, F Dias2.   

Abstract

Tsunamis are rare events with severe consequences. This generates a high demand on accurate simulation results for planning and risk assessment purposes because of the low availability of actual data from historic events. On the other hand, validation of simulation tools becomes very difficult with such a low amount of real-world data. Tsunami phenomena involve a large span of spatial and temporal scales-from ocean basin scales of [Formula: see text] to local coastal wave interactions of [Formula: see text] or even [Formula: see text], or from resonating wave phenomena with durations of [Formula: see text] to rupture with time periods of [Formula: see text]. The scale gap of five orders of magnitude in each dimension makes accurate modelling very demanding, with a number of approaches being taken to work around the impossibility of direct numerical simulations. Along with the mentioned multi-scale characteristic, the tsunami wave has a multitude of different phases, corresponding to different wave regimes and associated equation sets. While in the deep ocean, wave propagation can be approximated relatively accurately by linear shallow-water theory, the transition to a bore or solitary wave train in shelf areas and then into a breaking wave in coastal regions demands appropriate mathematical and numerical treatments. The short duration and unpredictability of tsunami events pose another challenging requirement to tsunami simulation approaches. An accurate forecast is sought within seconds with very limited data available. Thus, efficiency in numerical solution processes and at the same time the consideration of uncertainty play a big role in tsunami modelling applied for forecasting purposes.
© 2015 The Author(s).

Entities:  

Keywords:  Galerkin methods; finite-volume methods; numerical methods; statistical emulator; tsunamis

Year:  2015        PMID: 26392612     DOI: 10.1098/rsta.2014.0382

Source DB:  PubMed          Journal:  Philos Trans A Math Phys Eng Sci        ISSN: 1364-503X            Impact factor:   4.226


  2 in total

1.  Discriminating the occurrence of inundation in tsunami early warning with one-dimensional convolutional neural networks.

Authors:  Jorge Núñez; Patricio A Catalán; Carlos Valle; Natalia Zamora; Alvaro Valderrama
Journal:  Sci Rep       Date:  2022-06-20       Impact factor: 4.996

2.  Statistical emulation of landslide-induced tsunamis at the Rockall Bank, NE Atlantic.

Authors:  D M Salmanidou; S Guillas; A Georgiopoulou; F Dias
Journal:  Proc Math Phys Eng Sci       Date:  2017-04-12       Impact factor: 2.704

  2 in total

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