The nonlinear predictability of the electrotelluric field variations data analyzed with support vector machines as an earthquake precursor.
This work investigates the nonlinear predictability of the Electro Telluric Field (ETF) variations data in order to develop new intelligent tools for the difficult task of earthquake prediction. Support Vector Machines trained on a signal window have been used to predict the next sample. We observe a significant increase at this short-term unpredictability of the ETF signal at about two weeks time period before the major earthquakes that took place in regions near the recording devices. The unpredictability increase can be attributed to a quick time variation of the dynamics that produce the ETF signal due to the earthquake generation process. Thus, this increase can be taken into advantage for signaling for an increased possibility of a large earthquake within the next few days in the neighboring region of the recording station.
Ifantis
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Technological Educational Institute of Patras, Department of Electrical Engineering, 26334 Patras, Greece. ifantisa@teipat.gr
Papadimitriou
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D003627:Data Interpretation, Statistical; D004190:Disasters; D004784:Environmental Monitoring / Q000295:instrumentation; D017711:Nonlinear Dynamics; D011336:Probability; D011827:Radiation; D012815:Signal Processing, Computer-Assisted; D013816:Thermodynamics; D013997:Time Factors