地震地质 ›› 2015, Vol. 37 ›› Issue (2): 649-660.DOI: 10.3969/j.issn.0253-4967.2015.02.025

• 问题讨论 • 上一篇    

基于热异常信息与BP神经网络的中强地震预测试验

宋冬梅1,2, 时洪涛1, 单新建2, 刘雪梅1, 崔建勇1, 沈晨3, 屈春燕2, 邵红梅3, 王一博1, 臧琳1, 陈伟民1, 孔建4   

  1. 1. 中国石油大学(华东), 地球科学与技术学院, 青岛 266580;
    2. 中国地震局地质研究所, 地震动力学国家重点实验室, 北京 100029;
    3. 中国石油大学(华东), 理学院, 青岛 266580;
    4. 武汉大学, 测绘学院, 武汉 430079
  • 收稿日期:2013-09-28 修回日期:2015-04-20 出版日期:2015-06-20 发布日期:2015-08-19
  • 作者简介:宋冬梅,女,1973年生,2003年于中国科学院沈阳应用生态研究所获理学博士学位,副教授,主要研究方向为地震热红外异常信息提取,电话:0532-86985091,E-mail:songdongmei1973@126.com。
  • 基金资助:

    地震动力学国家重点实验室(LED2012B02)资助

A TENTATIVE TEST ON MODERATELY STRONG EARTHQUAKE PREDICTION IN CHINA BASED ON THERMAL ANOMALY INFORMATION AND BP NEURAL NETWORK

SONG Dong-mei1,2, SHI Hong-tao1, SHAN Xin-jian2, LIU Xue-mei1, CUI Jian-yong1, SHEN Chen3, QU Chun-yan2, SHAO Hong-mei3, WANG Yi-bo1, ZANG Lin1, CHEN Wei-min1, KONG Jian4   

  1. 1. School of Geosciences, China University of Petroleum, Qingdao 266555, China;
    2. State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing 100029, China;
    3. College of Science, China University of Petroleum, Qingdao 266580, China;
    4. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
  • Received:2013-09-28 Revised:2015-04-20 Online:2015-06-20 Published:2015-08-19

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摘要：

地震预测是地震科学研究的主要领域之一。震前热异常现象(地表温度异常升高)普遍存在并且与地震三要素有复杂的非线性关系。文中结合神经网络的优点, 提出将热异常信息作为地震预测的信息源, 通过构建神经网络, 进行地震预测的思路, 并进行了试验。基于8d合成的1km分辨率的MODIS数据, 利用RST算法提取震前热异常信息, 在分析震前热异常信息时空变化的基础上, 确定出BP神经网络的结构, 利用该网络对中国及周边100个5级以上震例, 以及70个随机无震样本进行训练和仿真。试验结果表明, 通过RST算法提取的震前热异常指数值, 用于BP神经网络地震预测是可行的, 其预测的试验结果刻画出了地震要素与热异常值间的非线性相关性。未来预测区域范围的选取以及神经网络中隐层神经元的数量将对地震预测效果产生较大的影响。

关键词: MODIS, 震前热异常, BP神经网络, 地震预测

Abstract:

Earthquake prediction is one of the key areas of earthquake research. Thermal infrared abnormity, which is the abnormally increased land surface temperature, is universal before earthquake and has complex nonlinear relation with the three elements of earthquake. Combining the advantage of neural network, this paper provides a method for earthquake prediction by taking thermal anomaly as information source and constructing a neural network to carry out the test. Based on the MODIS data which has synthesis of eight days with 1km resolution, taking a 10°×10° rectangle, whose center is the epicenter, as research area, and a two-month time before earthquake as the time range, we used RST algorithm to extract thermal anomaly information before earthquake. Considering the time-space relationship between thermal anomaly information and the fault zone, thinking carefully about the information of the neural network input neurons, we constructed BP neural network and used 100 earthquake cases with magnitudes larger than 5, and 70 random samples without earthquake in the research region for training and simulating. According to the statistical analysis, the prediction accuracy is 80%, missing prediction rate is 20%, and false prediction rate is 13.3%. Prediction accuracy of magnitude with error within magnitudes of 2 is 69%, prediction accuracy of earthquake origin time with error within 30 days is 87.5%, and prediction accuracy of epicenter location with error within 3°is 81.2%. The result shows that BP neural network-based earthquake prediction is feasible by using thermal infrared abnormal precursor extracted by RST method. However, in this experiment, the determination of the start time of thermal abnormity, the origin point and range of research area are based on the known epicenter location and time. In fact, the result depicts a non-linear relationship between earthquake and thermal abnormity, and the accuracy of prediction reflects the correlation degree. Therefore, the prediction accuracy of future earthquake may be not as large as our result. For future earthquake prediction, accurate selection of research area and neuron number of hidden layer in neural network has great influence on prediction result.

Key words: MODIS, thermal anomaly before the earthquake, BP neural network, earthquake prediction test

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