在地脉动数据上应用分类算法的地震预测实验  

Earthquake Forecast Experiment with Classification Algorithm Based on Microtremor Data

作　　者：马士振[1] 刘宏志[2] 牟磊育[3] MA Shi-zhen;LIU Hong-zhi;MU Lei-yu(Beijing Earthquake Agency,Beijing 100080,China;School of Software&Microelectronics,Peking University,Beijing 102600,China;Institute of Geophysics,China Earthquake Administration,Beijing 100081,China)

机构地区：[1]北京市地震局,北京100080 [2]北京大学软件与微电子学院,北京102600 [3]中国地震局地球物理研究所,北京100081

出　　处：《地震》2020年第1期159-171,共13页Earthquake

摘　　要：以"红肿"假说为基础,在由地脉动数据统计量和过往震例构成的样本集上应用数据挖掘中的分类算法开展地震预测实验。筛选符合震级、震中距、发震时间间隔以及未受台风影响等要求的地震对,并以其尾地震作为预测对象。计算地震对时间范围内各时间窗中地脉动数据的标准差,并采用z-score标准化方法对标准差数据进行标准化处理。然后,选取距震中最近三个台的最后一组标准化数据的中位数作为正样本数据,选取各台站平静期数据的中位数作为负样本数据,最后将上述正负样本数据构成样本集。使用CART算法、GBDT算法和SVM算法在此样本集上分别构建预测模型,采用5折交叉验证方法对预测模型进行评估。实验结果表明:①地震与地脉动变化存在一定的关系,且地脉动异常现象更多地出现在6.0级以上地震发生前;②6.0级以上地震构成的正样本对预测模型的构建影响较大;③SVM算法更适用于小样本数据环境。Based on the"redness and swelling"hypothesis,the classification algorithm of data mining is applied to carry out earthquake prediction experiments on the sample set which is composed of microtremor data statistics and past earthquake cases.We chose seismic pairs that meet the requirements of magnitude,epicenter distance,occurrence time interval,and unaffected by typhoons conditions,and use the tail earthquakes as prediction targets.The standard deviation of microtremor data in time windows is calculated,and the standard deviation data is standardized by Z-score standardization method.Then,the median of the last group of standardized data from the nearest three stations to the epicenter is selected as the positive sample data,and the median of the seismic quiet period data of each station is selected as the negative sample data.Finally,the positive and negative sample data are constructed into the sample set.CART,GBDT and SVM methods are used to construct the prediction model on this sample set,and 5 fold cross validation method is used to evaluate the prediction model.The results show that:①there is a relationship between earthquakes and microtremor changes,and there are microtremor anomalies before earthquakes(M≥6.0)occurred.②The positive samples that magnitude over 6 have a greater influence on the model construct.③The SVM algorithm is more suitable for the small sample data environment of this paper.

关 键 词：地脉动 分类 地震预测 

分 类 号：P315.7[天文地球—地震学]