J. Lin

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The goal of this study is to determine whether principal component analysis (PCA) can be used to process latitude-time ionospheric TEC data on a monthly basis to identify earthquake associated TEC anomalies earlier than 5 days before a large (M ≥ 6) earthquake. PCA is applied to latitude-time (mean-of-a-month) ionospheric total electron content (TEC) records collected from the Japan GEONET system to detect TEC anomalies associated with 26 earthquakes in Japan (M ≥ 6.0) from 2004 to 2005. According to the results, PCA was able to discriminate clear TEC anomalies in the months when all 26 earthquakes occurred. After reviewing months when no M ≥ 6.0 earthquakes occurred but geomagnetic storm activity was present, it is possible that the maximal principal eigenvalues PCA returned for these 26 earthquakes indicate earthquake associated TEC anomalies. Previously, PCA has been used to discriminate earthquake-associated TEC anomalies recognized by other researchers who found that statistical association between large earthquakes and TEC anomalies could be established in the 5 days before earthquake nucleation; however, since PCA uses the characteristics of principal eigenvalues to determine earthquake related TEC anomalies, it is possible to show that such anomalies existed earlier than this 5-day statistical window. In this paper, this is shown through the application of PCA to one-dimensional TEC data relating to the Kyushu earthquake of 20 March 2005 (M = 6.6). The analysis is applied to daily TEC and reveals a large principal eigenvalue (representative of an earthquake associated anomaly) for March 9, 11 days before the March 20 earthquake.

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Acta Geodaetica et Geophysica
Language English
Size B5
Year of
per Year
per Year
Founder Magyar Tudományos Akadémia
H-1051 Budapest, Hungary, Széchenyi István tér 9.
Publisher Akadémiai Kiadó Springer
Nature Switzerland AG
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
CH-6330 Cham, Switzerland Gewerbestrasse 11.
Chief Executive Officer, Akadémiai Kiadó
ISSN 2213-5812 (Print)
ISSN 2213-5820 (Online)

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