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《Earthquake》 2008-03
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Study on model of support vector machine for synthetic prediction of seismic precursors

WU An-xu1,ZHANG Yong-xian2,ZHANG Xiao-dong2 LI Ping-an1,MU Hui-yong1,LU Ya-jun1 (1.Earthquake Administration of Beijing Municipality, Beijing 100080; 2.China Earthquake Networks Center, CEA, Beijing 100045, China)  
The principle of support vector machine (SVM) and its regression algorithm is introduced in this paper. The multidimensional samples from theoretical formula are tested by using of SVM. The best model for synthetic prediction of seismic precursors is established according to the SVM algorithm and the seismic precursory anomalies, and it is tested by using of the testing samples. It indicates that the forecast result of the best mode and earthquake magnitude of real seismic examples are basically consistent. It shows that the support vector machine algorithm has an obvious superiority whatever on machine learning or prediction accuracy, and the model for synthetic prediction of seismic precursors based on the SVM theory is feasible, and it can forecast the magnitude of main earthquakes more accurately.
【Fund】: 国家科技攻关项目(2006BAC01B03-04-04;03-02-03);; 地震科学联合基金(A07058)
【CateGory Index】: P315.72
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