Is it possible to detect earlier ionospheric precursors before large earthquakes using principal component analysis (PCA)?
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The goal of this study was to determine whether principal component analysis (PCA) can be used to process GPS ionospheric total electron content (TEC) data on a monthly basis to identify early earthquake-associated TEC anomalies. PCA is applied to GPS (mean value of a month) ionospheric TEC records collected from the Japan GEONET system to detect TEC anomalies associated with 10 earthquakes in Japan (M ≥ 6.0) from 2006 to 2007. According to the results, PCA was able to discriminate clear TEC anomalies in the months when all 10 earthquakes occurred. After reviewing the months when no M ≥ 6.0 earthquake occurred but the geomagnetic storm activity was present, it is possible that the maximal principal eigenvalues PCA returned for these 10 earthquakes indicate earthquake-associated TEC anomalies. Previously, PCA has been used to discriminate earthquake-associated TEC anomalies recognized by other researchers who found that a statistical association between large earthquakes and TEC anomalies could be established in the 5 days before earthquake nucleation and in 24 h before earthquake; 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 earthquake of 17 February 2007 (M = 6.0). The analysis is applied to daily TEC and reveals a large principal eigenvalue (representative of an earthquake-associated anomaly) for 02 February, 15 days before the 17 February earthquake.
KeywordsPrincipal component analysis (PCA) GPS ionospheric total electron content (TEC) records GEONET system
The author is grateful to Dr. Louis (L.Y.) Tsai of the Graduate Institute of Applied Geology and Dr. H.W. Chen of the Institute of Geophysics, National Central University, and Dr Jyh-Shing Roger Jang of the Department of Computer Science, National Tsing-Hua University for their useful references and data support.
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