地震动时频参数预测模型研究

周万森; 钟菊芳; 张艳红; 胡晓

doi:10.3724/j.gyjzG22110105

地震动时频参数预测模型研究

doi: 10.3724/j.gyjzG22110105

周万森^1,2,
钟菊芳^2, ,,
张艳红¹,
胡晓¹

1. 中国水利水电科学研究院工程抗震研究中心, 北京 100048;
2. 南昌航空大学土木建筑学院, 南昌 330063

基金项目:

国家自然科学基金项目(51969019,51468045)。

2022年度水利部重大科技项目(SKS-2022100)

中国水利水电科学研究院基本科研业务费专项项目(EB110145B0012021)

详细信息

作者简介:
周万森,硕士研究生,主要从事地震动参数特征研究。

通讯作者:
钟菊芳,博士,教授,主要从事地震动研究,zhjf_814@163.com。

计量
- 文章访问数: 7
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- 被引次数: 0
出版历程
- 收稿日期: 2022-11-01
- 网络出版日期: 2025-01-04
- 刊出日期: 2024-12-20

Research on Time-Frequency Parameter Prediction Models of Ground Motion

1. Earthquake Engineering Research Centre, China Institute of Water Resources and Hydropower Research, Beijing 100048, China;
2. College of Civil Engineering, Nanchang Hangkong University, Nanchang 330063, China

摘要

摘要: 为了使地震危险性分析、地震区划等工程应用中的参数预测方程能同时体现地震动的时域和频域非平稳性,需要一种新的地震动时频参数预测模型。基于美国西部30余次地震记录,使用互补集合经验模态分解法计算时变功率谱,利用时变功率谱计算6个时频参数,建立时频参数随震级、距离和场地条件变化的预测方程和神经网络拓扑结构。运用非线性最小二乘法拟合得到预测方程的系数值,并训练得到神经网络预测模型。研究结果表明:总能量随震级的增大而增大,随距离增大而减小;频谱质心和标准差、时间质心和标准差以及时频相关系数随震级的增大而减小,随距离的增大表现则各不相同;建立的神经网络时频参数预测模型泛化能力较强,可以较好地预测地震动时频参数。
- 地震动时频参数 /
- 时变功率谱 /
- CEEMD法 /
- 人工神经网络 /
- 预测模型
Abstract: In order to make the parameter prediction equations in engineering applications such as seismic hazard analysis and seismic zoning reflect the time-frequency non-stationarity of ground motion, a new time-frequency parameter prediction model of ground motion is needed. Based on more than 30 earthquake records in the western United States, the complementary ensemble empirical mode decomposition method was used to calculate the time-varying power spectrum, the time-varying power spectrum was used to calculate six time-frequency parameters, and the prediction equation and neural network topology of time-frequency parameters with magnitude, distance and site conditions were established. The nonlinear least squares method was used to fit the coefficient values of the prediction equation, and the neural network prediction model was trained. The results showed that the total energy increased with the increase of magnitude and decreased with the increase of distance. The centroid and standard deviation of the spectrum, the centroid and standard deviation of the time, and the time-frequency correlation coefficients decreased with the increase of the magnitude, but varied with the increase of the distance. The established neural network time-frequency parameter prediction model showed strong generalization ability and could better predict the time-frequency parameters of ground motion.
- time-frequency parameters of ground motion /
- time-varying power spectrum /
- complementary ensemble empirical mode decomposition /
- artificial neural networks /
- prediction model

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参考文献(24)

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