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地震 ›› 2025, Vol. 45 ›› Issue (1): 161-180.doi: 10.12196/j.issn.1000-3274.2025.01.011

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基于VMD-BP神经网络模型的地震三要素预测算法

张家琪, 贺茜君, 王启阅   

  1. 北京工商大学数学与统计学院, 北京 100048
  • 收稿日期:2024-02-21 接受日期:2024-08-27 发布日期:2025-04-15
  • 通讯作者: 贺茜君, 教授。 E-mail: hexijun111@sina.com
  • 作者简介:张家琪(2000-), 女, 湖南衡阳人, 硕士研究生, 主要从事地震预测研究。 E-mail: 1204915037@qq.com
  • 基金资助:
    国家自然科学基金面上项目(42374143)

Three Elements Earthquake Prediction Method Using VMD-BP Neural Network Model

ZHANG Jia-qi, HE Xi-jun, WANG Qi-yue   

  1. School of Mathematics and Statistics, Beijing Technology and Business University, Beijing 100048, China
  • Received:2024-02-21 Accepted:2024-08-27 Published:2025-04-15

摘要: 地震灾害的发生具有突发性和瞬时性, 可能对人类社会产生巨大的影响, 造成严重的灾难。 因此, 发展地震预测的相关理论和方法具有重要意义。 本研究利用经典的反向传播(Back Propagation, BP)神经网络, 结合变分模态分解(Variational Mode Decomposition, VMD)技术对地震数据集进行预处理, 建立了一种新的VMD-BP地震预测模型。 本研究对于地震目录中震级1.5级以上的每个条目, 都考虑了除震源深度以外的四个特征: 时间差、 震级、 纬度和经度, 将过去5个时间相邻地震事件的特征作为VMD-BP模型的输入, 并将下一个地震事件的特征作为输出, 通过构建4个VMD-BP模型分别对地震发震时间、 震级、 震中位置(纬度、 经度)进行预测。 对该模型在四川和西藏地区进行了实证分析。 通过比较模型的性能参数, 我们发现VMD-BP模型比BP模型的预测精度和拟合优度更好。 这表明基于VMD-BP的模型能够更为准确地进行地震预测, 为未来地震预测的深入研究提供了有益参考。

关键词: 地震预测, BP神经网络, 变分模态分解, 深度学习

Abstract: The occurrence of an earthquake is instantaneous and destructive, which may cause serious disaster to human society. Therefore, the development of relevant theories and methods for earthquake prediction is of great significance. In this study, classical back propagation (BP) neural network and variational mode decomposition (VMD) technology are combined to develop a new VMD-BP earthquake prediction model. Four VMD-BP models were constructed to predict the time, magnitude, and location (latitude and longitude) of earthquakes. For each entry in the earthquake catalog, four features other than the depth of the earthquake source are considered: time difference, magnitude, latitude, and longitude. The past five adjacent seismic events was used as the input to the VMD-BP model, and the next seismic event was used as the output. The model was empirically analyzed in the Sichuan and Xizang regions. By comparing the prediction performance of the models, it is concluded that the VMD-BP model has better prediction accuracy and goodness of fit than the BP model. It illustrates that the VMD-based model can make more accurate earthquake prediction, which will provide useful references for future in-depth research on earthquake prediction.

Key words: Earthquake prediction, BP neural network, Variational mode decomposition, Deep learning

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