@article{1450788,
      recid = {1450788},
      author = {Wang, Yuchen.},
      title = {Tsunami data assimilation for early warning /},
      publisher = {Springer,},
      address = {Singapore :},
      pages = {1 online resource},
      year = {2022},
      note = {"Doctoral Thesis accepted by The University of Tokyo,  Tokyo, Japan."},
      abstract = {This 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.},
      url = {http://library.usi.edu/record/1450788},
      doi = {https://doi.org/10.1007/978-981-19-7339-0},
}