dc.creatorNavarrete, P.
dc.creatorCienfuegos, Rodrigo
dc.creatorSatake, K.
dc.creatorWang, Y.
dc.creatorUrrutia, A.
dc.creatorBenavente Bravo, Roberto
dc.creatorCatalán, P. A.
dc.creatorCrempien, J.
dc.creatorMulia, I.
dc.date2021-05-10T18:16:30Z
dc.date2021-05-10T18:16:30Z
dc.date2020-02
dc.identifierGeophysical Journal International, Volume 221, Issue 3, June 2020, pp. 1640–1650
dc.identifier1365-246X
dc.identifierhttp://repositoriodigital.ucsc.cl/handle/25022009/2242
dc.identifier10.1093/gji/ggaa098
dc.descriptionArtículo de publicación ISI
dc.descriptionWe propose a method for defining the optimal locations of a network of tsunameters in view of near real-time tsunami forecasting using sea surface data assimilation in the near and middle fields, just outside of the source region. The method requires first the application of the empirical orthogonal function analysis to identify the potential initial locations, followed by an optimization heuristic that minimizes a cost-benefit function to narrow down the number of stations. We apply the method to a synthetic case of the 2015 Mw8.4 Illapel Chile earthquake and show that it is possible to obtain an accurate tsunami forecast for wave heights at near coastal points, not too close to the source, from assimilating data from three tsunameters during 14 min, but with a minimum average time lag of nearly 5 min between simulated and forecasted waveforms. Additional tests show that the time lag is reduced for tsunami sources that are located just outside of the area covered by the tsunameter network. The latter suggests that sea surface data assimilation from a sparse network of stations could be a strong complement for the fastest tsunami early warning systems based on pre-modelled seismic scenarios.
dc.languageen
dc.publisherOxford University Press
dc.sourcehttps://doi.org/10.1093/gji/ggaa098
dc.subjectTsunami
dc.subjectWave propagation
dc.subjectNumerical techniques
dc.subjectSubduction zone
dc.subjectOceans
dc.subjectInstrumentation
dc.titleSea surface network optimization for tsunami forecasting in the near field: application to the 2015 Illapel earthquake
dc.typeArticle


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