Pre-seismic anomalies from optical satellite observations: a review
Detecting various anomalies using optical satellite data prior to strong earthquakes is key to understanding and forecasting earthquake activities because of its recognition of thermal-radiation-related phenomena in seismic preparation phases. Data from satellite observations serve as a powerful...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2018-04-01
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Series: | Natural Hazards and Earth System Sciences |
Online Access: | https://www.nat-hazards-earth-syst-sci.net/18/1013/2018/nhess-18-1013-2018.pdf |
Summary: | Detecting various anomalies using optical satellite data
prior to strong earthquakes is key to understanding and forecasting
earthquake activities because of its recognition of thermal-radiation-related phenomena in seismic preparation phases. Data from
satellite observations serve as a powerful tool in monitoring earthquake
preparation areas at a global scale and in a nearly real-time manner. Over
the past several decades, many new different data sources have been utilized
in this field, and progressive anomaly detection approaches have been
developed. This paper reviews the progress and development of
pre-seismic anomaly detection technology in this decade. First, precursor
parameters, including parameters from the top of the atmosphere, in the
atmosphere, and on the Earth's surface, are stated and discussed. Second,
different anomaly detection methods, which are used to extract anomalous
signals that probably indicate future seismic events, are presented.
Finally, certain critical problems with the current research are
highlighted, and new developing trends and perspectives for future work are
discussed. The development of Earth observation satellites and anomaly
detection algorithms can enrich available information sources, provide
advanced tools for multilevel earthquake monitoring, and improve short- and
medium-term forecasting, which play a large and growing role in
pre-seismic anomaly detection research. |
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ISSN: | 1561-8633 1684-9981 |