Residential College | false |
Status | 已發表Published |
Island-based GNSS-IR network for tsunami detecting and warning | |
Li, Linlin1; Qiu, Qiang2,3; Ye, Mai2; Peng, Dongju4; Hsu, Ya Ju5; Wang, Peitao6; Shi, Huabin7; Larson, Kristine M.8; Zhang, Peizhen1 | |
2024-06-01 | |
Source Publication | Coastal Engineering |
ISSN | 0378-3839 |
Volume | 190Pages:104501 |
Abstract | Deep-sea tsunami detection relies on Deep-ocean Assessment and Reporting of Tsunamis (DART), GNSS buoys, and cabled Ocean-Bottom Pressure (OBP) gauges, which are very expensive and difficult to maintain, and often suffer from vandalism or negligent damage. Here, we exploit the potential of establishing a less expensive and more robust island-based geodetic network for tsunami detecting, source reconstruction and warning. The network locates at the coastline of islands and uses a new technique: GNSS Interferometric Reflectometry (GNSS-IR). GNSS-IR retrieves sea levels from combination of the direct and reflected signals from the sea surface sent by satellites. To test the feasibility and efficiency of such a new geodetic network, we use the South China Sea region as an example, and compare its performance in reconciling the variable slip distribution on the Manila megathrust with the previously designed deep-sea monitoring system, i.e., DARTs and planned cable-based OBP gauges. We find that the newly designed GNSS-IR network could work equally well as the cabled OBP network in detecting tsunamis if the stations are built in strategically chosen locations. Combining GNSS-IR with a Kalman filter approach, we demonstrate that carefully situated coastal GNSS stations at global remote deep-ocean islands could function similarly to conventional tide gauges but with advantages of simultaneously measuring relative sea-level and land-height changes, meanwhile suffering lower risk from damaging sea-level events and potential vandalism. |
Keyword | Gnss-ir Kalman Filter Sea Level Measurement Tsunami Warning |
DOI | 10.1016/j.coastaleng.2024.104501 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Engineering |
WOS Subject | Engineering, Civil ; Engineering, Ocean |
WOS ID | WOS:001222393100001 |
Publisher | ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS |
Scopus ID | 2-s2.0-85188539363 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | Faculty of Science and Technology THE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU) |
Corresponding Author | Qiu, Qiang |
Affiliation | 1.Guangdong Provincial Key Laboratory of Geodynamics and Geohazards, School of Earth Sciences and Engineering, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, China 2.CAS Key Laboratory of Ocean and Marginal Sea Geology, South China Sea Institute of Oceanology, Innovation Academy of South China Sea Ecology and Environmental Engineering, Chinese Academy of Sciences, Guangzhou, 510301, China 3.China-Pakistan Joint Research Center on Earth Science, CAS-HEC, Islamabad, 45320, Pakistan 4.Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China 5.Institute of Earth Sciences, Academia Sinica, Taipei, Taiwan 6.Key Laboratory of Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Ministry of Natural Resources, Beijing, China 7.Faculty of Science and Technology, University of Macau, Macao 8.Institute of Geodesy and Geoinformation, Bonn University, Bonn, Germany |
Recommended Citation GB/T 7714 | Li, Linlin,Qiu, Qiang,Ye, Mai,et al. Island-based GNSS-IR network for tsunami detecting and warning[J]. Coastal Engineering, 2024, 190, 104501. |
APA | Li, Linlin., Qiu, Qiang., Ye, Mai., Peng, Dongju., Hsu, Ya Ju., Wang, Peitao., Shi, Huabin., Larson, Kristine M.., & Zhang, Peizhen (2024). Island-based GNSS-IR network for tsunami detecting and warning. Coastal Engineering, 190, 104501. |
MLA | Li, Linlin,et al."Island-based GNSS-IR network for tsunami detecting and warning".Coastal Engineering 190(2024):104501. |
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