001450788 000__ 03577cam\a2200457\a\4500
001450788 001__ 1450788
001450788 003__ OCoLC
001450788 005__ 20230310004543.0
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001450788 008__ 221031s2022\\\\si\\\\\\ob\\\\000\0\eng\d
001450788 020__ $$a9789811973390$$q(electronic bk.)
001450788 020__ $$a9811973393$$q(electronic bk.)
001450788 020__ $$z9811973385
001450788 020__ $$z9789811973383
001450788 0247_ $$a10.1007/978-981-19-7339-0$$2doi
001450788 035__ $$aSP(OCoLC)1349309757
001450788 040__ $$aYDX$$beng$$cYDX$$dGW5XE$$dEBLCP$$dOCLCF$$dOCLCQ
001450788 049__ $$aISEA
001450788 050_4 $$aGC221.2
001450788 08204 $$a551.46/37$$223/eng/20221101
001450788 1001_ $$aWang, Yuchen.
001450788 24510 $$aTsunami data assimilation for early warning /$$cYuchen Wang.
001450788 260__ $$aSingapore :$$bSpringer,$$c2022.
001450788 300__ $$a1 online resource
001450788 4901_ $$aSpringer theses,$$x2190-5061
001450788 500__ $$a"Doctoral Thesis accepted by The University of Tokyo, Tokyo, Japan."
001450788 504__ $$aIncludes bibliographical references.
001450788 5050_ $$aIntroduction -- Greens Function-based Tsunami Data Assimilation (GFTDA) -- Tsunami Data Assimilation with Interpolated Virtual Stations -- Real-Time Tsunami Detection based on Ensemble Empirical Mode Decomposition (EEMD) -- Real-time Tsunami Data Assimilation of S-net Pressure Gauge Records during the 2016 Fukushima Earthquake -- Tsunami Early Warning System Using Data Assimilation of Offshore Data -- Summary.
001450788 506__ $$aAccess limited to authorized users.
001450788 520__ $$aThis book focuses on proposing a tsunami early warning system using data assimilation of offshore data. First, Greens Function-based Tsunami Data Assimilation (GFTDA) is proposed to reduce the computation time for assimilation. It can forecast the waveform at Points of Interest (PoIs) by superposing Greens functions between observational stations and PoIs. GFTDA achieves an equivalently high accuracy of tsunami forecasting to the previous approaches, while saving sufficient time to achieve an early warning. Second, a modified tsunami data assimilation method is explored for regions with a sparse observation network. The method uses interpolated waveforms at virtual stations to construct the complete wavefront for tsunami propagation. Its application to the 2009 Dusky Sound, New Zealand earthquake, and the 2015 Illapel earthquake revealed that adopting virtual stations greatly improved the tsunami forecasting accuracy for regions without a dense observation network. Finally, a real-time tsunami detection algorithm using Ensemble Empirical Mode Decomposition (EEMD) is presented. The tsunami signals of the offshore bottom pressure gauge can be automatically separated from the tidal components, seismic waves, and background noise. The algorithm could detect tsunami arrival with a short detection delay and accurately characterize the tsunami amplitude. Furthermore, the tsunami data assimilation approach is combined with the real-time tsunami detection algorithm, which is applied to the tsunami of the 2016 Fukushima earthquake. The proposed tsunami data assimilation approach can be put into practice with the help of the real-time tsunami detection algorithm.
001450788 650_0 $$aTsunamis.
001450788 655_0 $$aElectronic books.
001450788 77608 $$iPrint version: $$z9811973385$$z9789811973383$$w(OCoLC)1345512732
001450788 830_0 $$aSpringer theses,$$x2190-5061
001450788 852__ $$bebk
001450788 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-7339-0$$zOnline Access$$91397441.1
001450788 909CO $$ooai:library.usi.edu:1450788$$pGLOBAL_SET
001450788 980__ $$aBIB
001450788 980__ $$aEBOOK
001450788 982__ $$aEbook
001450788 983__ $$aOnline
001450788 994__ $$a92$$bISE