基于主成分分析法优化广义回归神经网络的地震震级预测研究
DOI:
作者:
作者单位:

1.河北红山巨厚沉积与地震灾害国家野外科学观测研究站;2.河北省地震局邢台地震监测中心站;3.河北地质大学城市地质与工程学院;4.河北红山巨厚沉积与地震灾害国家野外科学观测研究站河北省地震局邢台地震监测中心站

作者简介:

通讯作者:

中图分类号:

P315

基金项目:

国家自然科学基金资助项目(41807231);河北省自然科学(D2019403182)资助;河北省地震科技星火项目(DZ2021110500001)。第一


Prediction Research of Earthquake Magnitude Based on Generalized Regression Neural Network Optimized by Principal Component Analysis
Author:
Affiliation:

National Field Scientific Observation and Research Station for Huge Thick Sediments and Seismic Disasters in Hongshan, Hebei Province

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 文章评论
    摘要:

    为科学有效预测地震震级,提出了基于广义回归神经网络(General Regression Neural Network,GRNN)的地震震级预测模型。选取地震累计频度、累计释放能量、b值、异常地震群数、地震条带个数、活动周期、相关区震级等7个指标作为地震震级影响因子,利用主成分分析法(Principal Component Analysis,PCA)对7个影响因子进行降维处理,以新生成的四个主成分作为模型输入变量,地震震级为输出变量,运用粒子群算法(Particle Swarm Optimization,PSO)寻优得到GRNN模型最优光滑因子,最终建立基于PCA-PSO-GRNN的地震震级预测模型,利用建好的模型对训练样本进行回判检验,并对测试样本进行预测,并同传统BP神经网络模型和单一GRNN模型预测结果进行对比,结果表明:PCA-PSO-GRNN模型预测结果的平均误差为5.17%,均方根误差为0.1000,决定系数为0.9868,均方相对误差为0.0073,平均绝对误差为0.1000,运行时间为5.2s,预测精度和运行效率均优于BP模型和单一GRNN模型。

    Abstract:

    In order to predict earthquake magnitude scientifically and effectively, earthquake magnitude prediction model based on general regression neural network was proposed. Seven indexes such as earthquake cumulative frequency, cumulative released energy, b value, number of abnormal earthquake clusters, number of seismic bands, activity cycle and magnitude of relevant areas were selected as the influence factors of earthquake magnitude. Principal component analysis was used to reduce the dimension of the seven influence factors, and the newly generated four principal components were used as the model input variables, the earthquake magnitude was taken as the output variable, the particle swarm optimization algorithm was used to find the optimal smoothing factor of GRNN model, and finally the earthquake magnitude prediction model based on PCA-PSO-GRNN was established. The PCA-PSO-GRNN model was used to test the study samples and predict the test samples, The prediction results were compared with results of the BP neural network model and the single GRNN model. The results show that the average error of the PCA-PSO-GRNN model was 5.17%, the root mean square error was 0.1000, the coefficient of determination was 0.9868, the mean square error was 0.0073, the mean absolute error was 0.1000, the running time was 5.2s, and the prediction accuracy and operation efficiency were better than BP model and single GRNN model.

    参考文献
    相似文献
    引证文献
引用本文

王晨晖,刘立申,袁颖,等. 基于主成分分析法优化广义回归神经网络的地震震级预测研究[J]. 科学技术与工程, , ():

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2022-02-07
  • 最后修改日期:2022-03-29
  • 录用日期:2022-04-18
  • 在线发布日期:
  • 出版日期:
×
关于近期《科学技术与工程》编辑部居家办公的说明
亟待确认的版面费信息