支持向量机方法在地震短期预测中的应用

支持向量机方法是基于统计学习理论提出的一种机器学习方法,在处理小样本、非线性问题方面有着很强的优势。而地震的孕育发生过程是一个复杂的非线性动力学系统,地震数据时间序列的变化更呈现非线性、不规则等特征。本文系统地分析了天津及其周边地区多种前兆现象,选取反映2至3个月短期情况的前兆测项,使用支持向量机分类方法建立地震综合预测模型,初步结果表明这种方法在地震短期预测中有一定的应用前景。

An Application of Support Vector Machine Method to Short-term Earthquake Predication

Support Vector Machine（SVM） is a new machine leaning method based on statistical learning theory.This method has obvious advantages in processing small-sample and nonlinear problems.As a matter of fact,the generation of earthquake is a very complicated nonlinear dynamic problem and the earthquake data manifest the nonlinear and irregular characteristics.This paper analyzed the seismic precursor of Tianjin city and neighborhood systematically and presented a synthetic earthquake predication model with a method of SVM classification by employing the seismic precursor information which reflects the short-term situation of 2～3 months.The analysis results show that this method is effective and has a good application future.