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.
Author(s): Yuchen Wang
Series: Springer Theses
Publisher: Springer
Year: 2022
Language: English
Pages: 107
City: Singapore
Supervisor’s Foreword
Abstract
Acknowledgements
Contents
Abbreviations
1 Introduction
1.1 Tsunami Early Warning
1.2 Numerical Modeling of Tsunami Propagation
1.3 Tsunami Data Assimilation Approach
1.3.1 Four-Dimensional Variational Assimilation and Kalman Filter
1.3.2 Optimal Interpolation
1.4 Network of Offshore Bottom Pressure Gauges
1.5 Real-Time Tsunami Detection
1.6 Objectives
References
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
2.3.1 Observed Tsunami Data
2.3.2 Assimilation Setting
2.3.3 Results
2.4 Adoption of Linear Dispersive Model—2004 Off the Kii Peninsula Earthquake
2.4.1 2004 Off the Kii Peninsula Earthquake
2.4.2 Synthetic Tsunami Data
2.4.3 Assimilation Setting
2.4.4 Results
2.5 Application to Real-Time Data—2015 Torishima Volcanic Tsunami Earthquake
2.5.1 Observed Tsunami Data
2.5.2 Assimilation Setting
2.5.3 Results
2.5.4 Accuracy and Number of Stations
2.6 Discussion
References
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
3.2.1 Synthetic Tsunami Data
3.2.2 Assimilation Setting and Results
3.2.3 Effects of Interpolation Interval
3.3 Test with Real Data—2009 Dusky Sound Earthquake
3.3.1 Observed Tsunami Data
3.3.2 Assimilation Setting
3.3.3 Results
3.4 Application to Far-Field Event—2015 Illapel Earthquake
3.4.1 Observed and Synthetic Tsunami Data
3.4.2 Assimilation Setting
3.4.3 Results
3.5 Discussion
References
4 Real-Time Tsunami Detection Based on Ensemble Empirical Mode Decomposition (EEMD)
4.1 EEMD
4.2 Validation Test—2016 Fukushima Earthquake
4.2.1 Observed Tsunami Data
4.2.2 Results
4.2.3 Evaluation of Detection: Comparison with Post-Processed Waveforms
4.3 Discussion
4.3.1 Applications to Extreme Cases
4.3.2 False Alarms and Missed Alarms
References
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
5.2.1 Tsunami Records
5.2.2 Waveform Processing
5.2.3 Tsunami Data Assimilation
5.3 Results
5.3.1 Extracted Tsunami Signals
5.3.2 Forecasted Tsunami Waveforms at Tide Gauges
5.4 Discussion
5.4.1 Adoption of a Real-Time Detection Algorithm
5.4.2 Results of the Synthetic Experiment
5.4.3 Data Assimilation at Ayukawa with Assumed OBPGs
References
6 Tsunami Early Warning System Using Data Assimilation of Offshore Data
6.1 Practical Implementation
6.2 Future Improvements
References
7 Summary
Curriculum Vitae: Yuchen Wang
