基于反向选择的地震预测方法

Method of earthquake prediction based on negative selection

针对大地震历史数据缺乏导致的大地震预测准确率低的问题,提出一种基于反向选择的地震预测方法。采用可变实值反向选择算法生成成熟检测器,用于预测地震是否发生。由于反向选择在训练过程中无须非我数据,可减小大地震数据缺乏对训练效果的影响。实验采用四川省历史地震数据,对一个月内是否发生5. 0级及以上地震进行预测。与传统机器学习算法进行对比,结果表明反向选择算法具有更好的预测效果。

For the low accuracy of earthquake prediction caused by the lack of large earthquake data,this paper proposed an earthquake prediction method based on negative selection.In this method,it used the variable real valued negative selection algorithm to generate mature detectors which were used to predict whether an earthquake occured.Due to the absence of non-self data sets in the negative selection training,it could be reduced the impact of lacking large earthquake data on the training effect.It used the historical earthquake data of Sichuan province to predict whether the magnitude 5.0 and above earthquakes occurred in Sichuan within one month.Compared with the traditional machine learning algorithm,the results show that the negative selection has better prediction effect.