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地震 ›› 2005, Vol. 25 ›› Issue (4): 26-32.

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中国大陆强震时间序列预测的支持向量机方法

王炜1, 刘悦2, 李国正2, 吴耿锋2, 林命周1, 马钦忠1, 赵利飞1   

  1. 1.上海市地震局, 上海 200062;
    2.上海大学计算机工程与科学学院, 上海 200072
  • 收稿日期:2005-03-22 修回日期:2005-05-25 出版日期:2005-10-31 发布日期:2021-11-10
  • 作者简介:王炜(1947-), 男, 江苏南京人, 研究员, 主要从事地震学、 地震预报等研究。
  • 基金资助:
    地震科学联合基金项目(104090)

The support vector machine method for time sequence forecasting of strong earthquakes in China′s mainland

WANG Wei1, LIU Yue2, LI Guo-zheng2, WU Geng-feng2, LIN Ming-zhou1, MA Qin-zhong1, ZHAO Li-fei1   

  1. 1. Earthquake Administration of Shanghai Municipality, Shanghai 200062;
    2. School of Computer Engineering and Science, Shanghai University, Shanghai 200072, China
  • Received:2005-03-22 Revised:2005-05-25 Online:2005-10-31 Published:2021-11-10

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摘要： 统计学习理论(Statistical Learning Theory或SLT)是研究有限样本情况下机器学习规律的理论。 支持向量机(Support Vector Machines 或 SVM)是基于统计学习理论框架下的一种新的通用机器学习方法。 它不但较好地解决了以往困扰很多学习方法的小样本、 过学习、 高维数、 局部最小等实际难题, 而且具有很强的泛化(预测)能力。 文中使用支持向量机对中国大陆最大地震时间序列进行预测, 预测次年的我国大陆最大地震震级, 结果表明该方法具有较好的预报效果。 研究结果还表明我国大陆强震活动除了与强震时间序列本身有关外, 还与全球的强震活动、 太阳黑子活动等有密切的关系。 尽管这种关系还不清楚, 但是通过支持向量机可以很好地反应出这种非线性关系。

关键词: 统计学习理论, 支持向量机, 时间序列

Abstract: Statistical learning theory is a small-sample statistics theory. Support vector machine is a new machine learning method based on statistical learning theory. It is not only helpful to solve some problems, such as small-sample, devilishly learning, big-dimension and local minimum, but also is of strong generalization (forecasting) ability. The support vector machine was used to predict the time sequence of the strong earthquakes, and to forecast the maximum earthquake magnitude in China's mainland next year. The results show the method has good forecasting effect. The results also indicate that the activity of strong earthquakes in the world and the sunspot. Though the relation is still not clear, nonlinear relation is well shown by use of the support vector machine.

Key words: Statistical learning theory, Support vector machine, Time sequence

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