Highlights Time series prediction is an interesting topic in the research of earthquake. This paper describes a new intelligence approach to detect the TEC anomalies. The genetic algorithm (GA) detected a considerable number of anomalous occurrences. The results indicates that GA can be an appropriate tool for TEC anomaly detection.
Abstract On 6 February 2013, at 12:12:27 local time (01:12:27 UTC) a seismic event registering Mw 8.0 struck the Solomon Islands, located at the boundaries of the Australian and Pacific tectonic plates. Time series prediction is an important and widely interesting topic in the research of earthquake precursors. This paper describes a new computational intelligence approach to detect the unusual variations of the total electron content (TEC) seismo-ionospheric anomalies induced by the powerful Solomon earthquake using genetic algorithm (GA). The GA detected a considerable number of anomalous occurrences on earthquake day and also 7 and 8days prior to the earthquake in a period of high geomagnetic activities. In this study, also the detected TEC anomalies using the proposed method are compared to the results dealing with the observed TEC anomalies by applying the mean, median, wavelet, Kalman filter, ARIMA, neural network and support vector machine methods. The accordance in the final results of all eight methods is a convincing indication for the efficiency of the GA method. It indicates that GA can be an appropriate non-parametric tool for anomaly detection in a non linear time series showing the seismo-ionospheric precursors variations.
Genetic algorithm for TEC seismo-ionospheric anomalies detection around the time of the Solomon (Mw =8.0) earthquake of 06 February 2013
Advances in Space Research ; 52 , 4 ; 581-590
2013-04-15
10 pages
Article (Journal)
Electronic Resource
English
Ionospher , Earthquake , TEC
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