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Titel |
Principal component analysis and singular spectrum analysis of ULF geomagnetic data associated with earthquakes |
VerfasserIn |
A. Serita, K. Hattori, C. Yoshino, M. Hayakawa, N. Isezaki |
Medientyp |
Artikel
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Sprache |
Englisch
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ISSN |
1561-8633
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Digitales Dokument |
URL |
Erschienen |
In: Natural Hazards and Earth System Science ; 5, no. 5 ; Nr. 5, no. 5 (2005-09-19), S.685-689 |
Datensatznummer |
250002794
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Publikation (Nr.) |
copernicus.org/nhess-5-685-2005.pdf |
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Zusammenfassung |
In order to extract any ULF signature associated with earthquakes, the
principal component analysis (PCA) and singular spectral analysis (SSA) have
been performed to investigate the possibility of discrimination of signals
from different sources (geomagnetic variation, artificial noise, and the
other sources (earthquake-related ULF emissions)). We adopt PCA to the time
series data observed at closely separated stations, Seikoshi (SKS),
Mochikoshi (MCK), and Kamo (KAM). In order to remove the most intense signal
like the first principal component, we make the differential data sets of
filtered 0.01Hz SKS-KAM and MCK-KAM in NS component and 0.01 Hz band. The
major findings are as follows. (1) It is important to apply principal
component analysis and singular spectral analysis simultaneously. SSA gives
the structure of signals and the number of sensors for PCA is estimated.
This makes the results convincing. (2) There is a significant advantage
using PCA with differential data sets of filtered (0.01 Hz band) SKS-KAM and
MCK-KAM in NS component for removing the most intense signal like global
variation (solar-terrestrial interaction). This provides that the anomalous
changes in the second principal component appeared more sharply. And the
contribution of the second principal component is 20–40%. It is large
enough to prove mathematical accuracy of the signal. Further application is
required to accumulate events. These facts demonstrate the possibility of
monitoring the crustal activity by using the PCA and SSA. |
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