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| Titel |
Support vector machines for TEC seismo-ionospheric anomalies detection |
| VerfasserIn |
M. Akhoondzadeh |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
0992-7689
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| Digitales Dokument |
URL |
| Erschienen |
In: Annales Geophysicae ; 31, no. 2 ; Nr. 31, no. 2 (2013-02-06), S.173-186 |
| Datensatznummer |
250017748
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| Publikation (Nr.) |
 copernicus.org/angeo-31-173-2013.pdf |
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| Zusammenfassung |
| Using time series prediction methods, it is possible to pursue the behaviors
of earthquake precursors in the future and to announce early warnings when
the differences between the predicted value and the observed value exceed
the predefined threshold value. Support Vector Machines (SVMs) are widely
used due to their many advantages for classification and regression tasks.
This study is concerned with investigating the Total Electron Content (TEC)
time series by using a SVM to detect seismo-ionospheric anomalous variations
induced by the three powerful earthquakes of Tohoku (11 March 2011), Haiti
(12 January 2010) and Samoa (29 September 2009). The duration of TEC time
series dataset is 49, 46 and 71 days, for Tohoku, Haiti and Samoa
earthquakes, respectively, with each at time resolution of 2 h. In the case of
Tohoku earthquake, the results show that the difference between the
predicted value obtained from the SVM method and the observed value reaches
the maximum value (i.e., 129.31 TECU) at earthquake time in a period of high geomagnetic
activities. The SVM method detected a considerable number of anomalous
occurrences 1 and 2 days prior to the Haiti earthquake and also 1 and 5 days
before the Samoa earthquake in a period of low geomagnetic activities. In
order to show that the method is acting sensibly with regard to the results
extracted during nonevent and event TEC data, i.e., to perform some
null-hypothesis tests in which the methods would also be calibrated, the same
period of data from the previous year of the Samoa earthquake date has been
taken into the account. Further to this, in this study, the detected TEC
anomalies using the SVM method were compared to the previous results
(Akhoondzadeh and Saradjian, 2011; Akhoondzadeh, 2012) obtained from the
mean, median, wavelet and Kalman filter methods. The SVM detected anomalies
are similar to those detected using the previous methods. It can be
concluded that SVM can be a suitable learning method to detect the novelty
changes of a nonlinear time series such as variations of earthquake
precursors. |
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