基于危险理论的地震预测方法

doi:10.19678/j.issn.1000-3428.0049375

计算机工程 ›› 2019, Vol. 45 ›› Issue (1): 278-283. doi: 10.19678/j.issn.1000-3428.0049375

基于危险理论的地震预测方法

甘颖,梁意文,谭成予,周雯,吴晶晶

武汉大学计算机学院,武汉 430072

收稿日期:2017-11-21 出版日期:2019-01-15 发布日期:2019-01-15
作者简介:甘颖(1992—),女,硕士研究生,主研方向为计算机免疫学;梁意文,教授、博士;谭成予,副教授、博士;周雯,博士研究生;吴晶晶,硕士研究生。
基金资助:
国家自然科学基金“计算机免疫学的危险感知方法及其应用研究”(61170306)

Earthquake Prediction Method Based on Danger Theory

GAN Ying,LIANG Yiwen,TAN Chengyu,ZHOU Wen,WU Jingjing

School of Computer,Wuhan University,Wuhan 430072,China

Received:2017-11-21 Online:2019-01-15 Published:2019-01-15

摘要/Abstract

摘要：

采用机器学习算法进行地震预测存在过拟合且需要大量训练集的问题。为此,将危险理论引入地震预测的应用中,在分析大量地震历史源数据和结合领域专家经验知识的基础上,提出一种利用地震学获取特征指标的地震预测方法。通过Gutenberg-Ricthter规则、特征地震震级分布和近期地震预测研究的结论提取9个地震特征指标,采用具有动态性的危险理论预测未来一个月内发生大地震事件的概率。同时,通过分析四川省地震历史数据,应用危险理论对地震特征指标进行分析和预测,并与现有的地震预测方法BP神经网络进行比较。实验结果表明,该方法的检查概率、准确率及R得分均高于BP神经网络,表明在采用较少的样本集时其可靠度更高。

关键词: 危险理论, 地震预测, Gutenberg-Richter规则, 特征指标, BP神经网络

Abstract:

Aiming at the existing problem that the machine learning algorithm used for earthquake prediction is over-fitting and needs a large number of training sets,this paper introduces the danger theory into the application of earthquake prediction.Based on the analysis of a large number of seismic history source data and the combination of of field experts’ experiences,a seismic prediction method using seismology to acquire feature indexes is proposed.Based on the Gutenberg-Ricthter(G-R) law,the distribution of the characteristic earthquake magnitude and the recent earthquake prediction,this method extracts nine seismic characteristic indexes and uses a dynamic danger theory to predict the probability of a major earthquake event in the next month.At the same time,by analyzing the seismic history data of Sichuan province,the danger theory is used to analyze and forecast the seismic characteristic index,and compared with the existing neural network of earthquake prediction method.Experimental results show that the detection probability,accuracy and R score of the proposed method are higher than BP neural network,which indicates that the reliability of it is higher when using the less sample set.

Key words: danger theory, earthquake prediction, Gutenberg-Ricthter(G-R) law, characteristic index, BP neural network

中图分类号:

TP18

甘颖,梁意文,谭成予,周雯,吴晶晶. 基于危险理论的地震预测方法[J]. 计算机工程, 2019, 45(1): 278-283.

GAN Ying,LIANG Yiwen,TAN Chengyu,ZHOU Wen,WU Jingjing. Earthquake Prediction Method Based on Danger Theory[J]. Computer Engineering, 2019, 45(1): 278-283.

https://www.ecice06.com/CN/Y2019/V45/I1/278

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