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Tsunami Data Assimilation for Early Warning

  • Book
  • © 2022

Overview

Authors:
  1. Yuchen Wang

    1. Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

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  • Is nominated as an outstanding Ph.D. thesis by The University of Tokyo, Tokyo, Japan
  • Presents a new tsunami data assimilation approach based on Green’s function
  • Uses virtual stations to reduce the engineering cost of installing offshore network for data assimilation

Part of the book series: Springer Theses (Springer Theses)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xvii
    Download chapter PDF

  2. Introduction

    • Yuchen Wang
    Pages 1-17

  3. Green’s Function-Based Tsunami Data Assimilation (GFTDA)

    • Yuchen Wang
    Pages 19-43

  4. Tsunami Data Assimilation with Interpolated Virtual Stations

    • Yuchen Wang
    Pages 45-62

  5. Real-Time Tsunami Detection Based on Ensemble Empirical Mode Decomposition (EEMD)

    • Yuchen Wang
    Pages 63-76

  6. Real-Time Tsunami Data Assimilation of S-Net Pressure Gauge Records During the 2016 Fukushima Earthquake

    • Yuchen Wang
    Pages 77-88

  7. Tsunami Early Warning System Using Data Assimilation of Offshore Data

    • Yuchen Wang
    Pages 89-91

  8. Summary

    • Yuchen Wang
    Pages 93-94

  9. Back Matter

    Pages 95-97
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Keywords

About this book

This book focuses on proposing a tsunami early warning system using data assimilation of offshore data. First, Green’s 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 Green’s 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.

Authors and Affiliations

  • Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

    Yuchen Wang

About the author

Dr. Yuchen Wang is a postdoctoral researcher at Japan Agency for Marine-Earth Science and Technology. He received the bachelor’s degree in physics at Peking University. He received the master’s degree and Ph.D. degree in earth and planetary science at the University of Tokyo. His research interest is giant earthquakes and tsunamis. He has been working on tsunami early warning for disaster mitigation. He improved data assimilation algorithm to achieve a rapid and accuracy tsunami forecast. He has published 21 peer-reviewed journal articles and worked as the reviewer for 9 journals including Nature Communications, Journal of Geophysical Research: Solid Earth, and Natural Hazards and Earth System Sciences. He is the principal investigator of the KAKENHI 19J20203 on tsunami data assimilation sponsored by the Japan Society for the Promotion of Science. His research is in collaboration with researchers all over the world.

Bibliographic Information

  • Book Title: Tsunami Data Assimilation for Early Warning

  • Authors: Yuchen Wang

  • Series Title: Springer Theses

  • DOI: https://doi.org/10.1007/978-981-19-7339-0

  • Publisher: Springer Singapore

  • eBook Packages: Earth and Environmental Science, Earth and Environmental Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022

  • Hardcover ISBN: 978-981-19-7338-3Published: 27 October 2022

  • Softcover ISBN: 978-981-19-7341-3Published: 28 October 2023

  • eBook ISBN: 978-981-19-7339-0Published: 26 October 2022

  • Series ISSN: 2190-5053

  • Series E-ISSN: 2190-5061

  • Edition Number: 1

  • Number of Pages: XVII, 97

  • Number of Illustrations: 3 b/w illustrations, 45 illustrations in colour

  • Topics: Natural Hazards, Geophysics/Geodesy

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