The University of Southampton
University of Southampton Institutional Repository

Modeling and detection of hydrodynamic trends for advancing early-tsunami warnings

Modeling and detection of hydrodynamic trends for advancing early-tsunami warnings
Modeling and detection of hydrodynamic trends for advancing early-tsunami warnings
The automated detection of tsunamigenic signals at oceanic observation stations is highly desirable for the advancement of current tsunami early warning systems. These are supported with matching methods using large numbers of tsunami wave propagation modeling scenarios. New techniques using real-time scanning of hydrodynamic signals around a network of stations in an open ocean have been developed for the detection of tsunamis. Spectral ratios with respect to background signals and their levels of similarity across stations were investigated. The new developed algorithms will be wrapped as a reporting web service for the TRIDEC tsunami early warning system in the future.
Sabeur, Zoheir
74b55ff0-94cc-4624-84d5-bb816a7c9be6
Arbab-Zavar, Banafshe
40e175ea-6557-47c6-b759-318d7e24984b
Wachter, Joachim
dd64b467-5c06-47a5-a9a5-2e5be7c29584
Hammitzsch, Martin
febfa7c3-4b42-4336-bcc3-1f3aa6855c0c
Lowe, Peter
f7a73cd3-65f1-4699-976e-81d13a785a73
Lendholdt, Matthias
d4b475ff-7cd8-4c24-b637-cf8ca49e87e6
Armigliato, Alberto
a62dab5c-c513-4930-8684-8926d8840a02
Pagnoni, Gianluca
4e6e2667-111a-413f-9c1b-858be5fe4b12
Tinti, Stefano
ea477461-6a37-4460-b6a1-340d0ddf0de3
Omira, Rachid
74ee6eef-e787-49c0-9e42-adbf7799039b
Sabeur, Zoheir
74b55ff0-94cc-4624-84d5-bb816a7c9be6
Arbab-Zavar, Banafshe
40e175ea-6557-47c6-b759-318d7e24984b
Wachter, Joachim
dd64b467-5c06-47a5-a9a5-2e5be7c29584
Hammitzsch, Martin
febfa7c3-4b42-4336-bcc3-1f3aa6855c0c
Lowe, Peter
f7a73cd3-65f1-4699-976e-81d13a785a73
Lendholdt, Matthias
d4b475ff-7cd8-4c24-b637-cf8ca49e87e6
Armigliato, Alberto
a62dab5c-c513-4930-8684-8926d8840a02
Pagnoni, Gianluca
4e6e2667-111a-413f-9c1b-858be5fe4b12
Tinti, Stefano
ea477461-6a37-4460-b6a1-340d0ddf0de3
Omira, Rachid
74ee6eef-e787-49c0-9e42-adbf7799039b

Sabeur, Zoheir, Arbab-Zavar, Banafshe, Wachter, Joachim, Hammitzsch, Martin, Lowe, Peter, Lendholdt, Matthias, Armigliato, Alberto, Pagnoni, Gianluca, Tinti, Stefano and Omira, Rachid (2013) Modeling and detection of hydrodynamic trends for advancing early-tsunami warnings. ISOPE 2013, United States. 30 Jun - 05 Jul 2013.

Record type: Conference or Workshop Item (Paper)

Abstract

The automated detection of tsunamigenic signals at oceanic observation stations is highly desirable for the advancement of current tsunami early warning systems. These are supported with matching methods using large numbers of tsunami wave propagation modeling scenarios. New techniques using real-time scanning of hydrodynamic signals around a network of stations in an open ocean have been developed for the detection of tsunamis. Spectral ratios with respect to background signals and their levels of similarity across stations were investigated. The new developed algorithms will be wrapped as a reporting web service for the TRIDEC tsunami early warning system in the future.

Text
TRIDEC-ISOPE2013-ZAS-BAZ-Template-FNL-2013-02-28_pageadjust - Accepted Manuscript
Download (1MB)
Text
359365.pdf - Other
Download (1MB)

More information

Published date: July 2013
Venue - Dates: ISOPE 2013, United States, 2013-06-30 - 2013-07-05
Organisations: IT Innovation

Identifiers

Local EPrints ID: 359365
URI: https://eprints.soton.ac.uk/id/eprint/359365
PURE UUID: 83624eef-753b-4201-9de4-ddcddbd35c63
ORCID for Zoheir Sabeur: ORCID iD orcid.org/0000-0003-4325-4871

Catalogue record

Date deposited: 30 Oct 2013 14:26
Last modified: 19 Jul 2019 21:23

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×