基于LSTM神经网络的南加州中期地震预测

doi:10.7523/j.ucas.2023.068

中国科学院大学学报 ›› 2025, Vol. 42 ›› Issue (2): 199-208.DOI: 10.7523/j.ucas.2023.068

• 地质与地球科学 • 上一篇

基于LSTM神经网络的南加州中期地震预测

王艺璇¹, 张怀¹, 石耀霖¹, 程术²

1. 中国科学院大学地球与行星科学学院中国科学院计算地球动力学重点实验室, 北京 100049;
2. 复旦大学代谢与整合生物学研究院, 上海 200433

收稿日期:2023-02-10 修回日期:2023-06-02 发布日期:2023-06-02
通讯作者: 张怀,E-mail:hzhang@ucas.ac.cn
基金资助:
国家自然科学基金委员会地震科学联合基金重点项目(U2239205)和科技部国家重点研发计划重点项目(2020YFA0713400)资助

Medium-term prediction of earthquakes in Southern California using LSTM neural network

WANG Yixuan¹, ZHANG Huai¹, SHI Yaolin¹, CHENG Shu²

1. CAS Key Laboratory of Computational Geodynamics, College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;
2. Institute of Metabolism & Integrative Biology(IMIB), Fudan University, Shanghai 200433, China

Received:2023-02-10 Revised:2023-06-02 Published:2023-06-02

摘要/Abstract

摘要： 以神经网络预测地震为主题，采用长短时记忆(LSTM)神经网络构建地震预测模型。基于1932—2021年的南加州地震目录资料,数据按照0.8∶0.2的比例划分，训练集的时间窗口为1932年1月至2002年3月，测试集为2002年3月至2021年9月。模型以LSTM神经网络为核心，综合训练集地震时间序列数据中计算出的11个反映地震时空强度分布特征的地震活动性指标，以及与之对应的最大震级构建标签，对测试集进行回溯性预测检验。利用混淆矩阵中的准确率、精确度、R评分等指标评估模型预测效果。结果显示，该模型在地震预测方面有一定的成效，成功预测出2010年4月的7.2级大地震，并且部分模型的R评分高于我国目前的中期预测水平。然而仍有部分指标未达到理想状态，还需深入探讨。

关键词: LSTM神经网络, 南加州地区, 地震中期预测, 地震活动性指标, R评分

Abstract: This paper explores earthquake prediction using neural networks, focusing mainly on using long-short-time memory (LSTM) neural networks to construct an earthquake prediction model. Based on the Southern California earthquake catalog data from 1932 to 2021, the earthquake catalog from January 1932 to March 2002 was used as the training set(80% of the entire earthquake catalog), and the earthquake catalog from March 2002 to September 2021 was used as the test set (the remaining 20%). The LSTM neural network was selected, and 11 earthquake prediction factors reflecting the spatiotemporal intensity distribution characteristics of the earthquake time series data were calculated from the training set. The maximum magnitude label corresponding to these factors was used to construct the model. The test set was then used for retrospective prediction testing. The model’s prediction performance was evaluated using metrics such as accuracy, precision, and R-value, which were calculated based on the values in the confusion matrix. The results show that the prediction has achieved certain results, predicting the M7.2 earthquake in April 2010. The R-value of some models is significantly higher than China’s current medium-term prediction level. However, the value of the evaluation model is still not ideal, and further exploration is needed.

Key words: LSTM neural network, Southern California, medium-term earthquake prediction, seismic factor, R-value

中图分类号:

P315.7

王艺璇, 张怀, 石耀霖, 程术. 基于LSTM神经网络的南加州中期地震预测[J]. 中国科学院大学学报, 2025, 42(2): 199-208.

WANG Yixuan, ZHANG Huai, SHI Yaolin, CHENG Shu. Medium-term prediction of earthquakes in Southern California using LSTM neural network[J]. Journal of University of Chinese Academy of Sciences, 2025, 42(2): 199-208.

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基于LSTM神经网络的南加州中期地震预测

Medium-term prediction of earthquakes in Southern California using LSTM neural network

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