Kanai, Ryuichi;
(2025)
Gaussian Process Regression for Ionosphere-Based Tsunami Warning and Functional History Matching.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
The damage caused by tsunamis is enormous, yet forecasting tsunami heights at the coastlines even after a tsunami has been generated remains challenging. When a tsunami occurs, it generates atmospheric anomalies, which can be detected by GPS satellites. Although interest in this physical phenomenon has been increasing, precise assessments have not been conducted. Hence, for the first time, a detailed analysis of these ionospheric anomalies has been successfully conducted in this study. This allows us to estimate the location of an initial tsunami using only ionospheric data. In addition, in the field of tsunami forecasting research, there is a strong demand for the development of methods capable of predicting tsunami peak timing and speed. To facilitate the utilization of both observed tsunami time-series data and the information derived from their derivative curves, Functional History Matching was developed as a first-ever attempt, enabling the analysis of continuous data. This method employs functional emulators and dimensionality reduction techniques, resulting in highly accurate tsunami forecasting at the coast, with illustrations over India and Japan. Building on these developments, I propose a method that estimates the initial tsunami solely using ionospheric data. This method integrates the newly developed Functional History Matching technique with an established simulation model that simulates ionospheric anomalies, bypassing reliance on ocean buoy data. This approach differs from traditional tsunami forecasting methods in terms of the data used and the underlying physical phenomena. It allows for improved forecasting accuracy and more appropriate issuance of tsunami warnings. These analytical methods can be implemented very quickly during actual tsunami events, with efficient algorithms, and require minimal additional investment as they make use of existing satellites. Therefore, the outcomes of this PhD research are applicable worldwide and can make a substantial contribution to saving lives.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Gaussian Process Regression for Ionosphere-Based Tsunami Warning and Functional History Matching |
| Language: | English |
| Additional information: | Copyright © The Author 2025. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10204193 |
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