Tsunami Data Assimilation for Early Warning

1. Auflage 2022
ISBN: 978-981-19-7341-3
Verlag: Springer

Buch, Englisch, 97 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 189 g

Reihe: Springer Theses

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.

Zielgruppe

Research

Autoren/Hrsg.

Wang, Yuchen

Fachgebiete

• Geowissenschaften Umweltwissenschaften Naturgewalten & Katastrophen
• Geowissenschaften Geologie Geophysik

Inhaltsverzeichnis

Chapter 1   Introduction

1.1 Tsunami Early Warning

1.2 Numerical Modeling of Tsunami Propagation

1.3 Tsunami Data Assimilation Approach

1.4 Network of Offshore Bottom Pressure Gauges

1.5 Real-time Tsunami Detection

1.6 Objectives

Chapter 2 Green’s Function-based Tsunami Data Assimilation (GFTDA)

2.1 Principles of GFTDA

2.2 Assimilation Process and Mathematical Equivalence

2.3 Validation Test—2012 Haida Gwaii Earthquake

2.4 Adoption of Linear Dispersive Model—2004 off the Kii Peninsula Earthquake

2.5 Application to Real-time Data—2015 Torishima Volcanic Tsunami Earthquake

2.6 Discussion

Chapter 3   Tsunami Data Assimilation with Interpolated Virtual Stations

3.1 Linear Interpolation with Huygens–Fresnel Principle

3.2 Test with Synthetic Data—2004 Sumatra–Andaman Earthquake

3.3 Test with Real Data—2009 Dusky Sound Earthquake

3.4 Application to Far-field Event—2015 Illapel Earthquake

3.5 Discussion

Chapter 4   Real-Time Tsunami Detection based on Ensemble Empirical Mode Decomposition (EEMD)

4.1 EEMD

4.2 Validation Test—2016 Fukushima Earthquake

4.3 Discussion

Chapter 5   Real-time Tsunami Data Assimilation of S-net Pressure Gauge Records during the 2016 Fukushima Earthquake

5.1 Introduction

5.2 Data and Methods

5.3 Results

5.4 Discussion

Chapter 6   Tsunami Early Warning System Using Data Assimilation of Offshore Data

6.1 Practical Implementation

6.2 Future Improvements

Chapter 7   Summary

Über Autor(innen)

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.