利用相关因子预测中国大陆强震的支持向量机方法

Abstract

Abstract: There is a strong and complicated nonlinear relation between earthquake activity in China′s mainland and the Sun activity, the Earth rotation and global earthquake activity. In this paper, using the sunspot numbers, the Earth rotation rate variation data and the total strain release of the globe earthquakes of M≥7.0 as prediction factors, a classification model of Support Vector Machine is used to forecast earthquake intensity in China′s mainland. The forecast effect of the model is relatively good, which shows the classification of Support Vector Machine is a relatively effective method for forecasting earthquake intensity in China′s mainland.

Key words: Support Vector Machine, Sunspot, Earth rotation, Global strain release, Prediction of strong earthquakes in China′s mainland

CLC Number:

P315.5

LI Zhi-xiong, YUAN Xi-wen, QIU Xue-qiang, WANG Zhi-cheng. Using correlation factors to forecast strong earth- quakes in China′s mainland with Support Vector Machine[J]. EARTHQUAKE, 2007, 27(1): 33-38.

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Using correlation factors to forecast strong earth- quakes in China′s mainland with Support Vector Machine

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