Probability density evolution method based stochastic simulation of near-fault pulse-like ground motions基于概率密度演化方法的近断层脉冲地震动随机模拟
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Luo CR, Peng YB, 2024. Probability density evolution method based stochastic simulation of near-fault pulse-like ground motions. Probabilistic Engineering Mechanics, 76: 103629.DOI: 10.1016/j.probengmech.2024.103629
摘要 | Abstract
量化近断层效应并建立合理的近断层脉冲地震动模型对于近断层区域结构的抗震设计尤为重要。鉴于地震具有显著的随机性，本研究首先提出了一种新的近断层脉冲地震动随机模型，该模型结合了修正有限断层模型 (improved finite-fault model, IFFM) 和多变量连接函数速度脉冲模型 (copula-based velocity-pulse model, CVPM)。此外，本文提出了一种基于概率密度演化方法 (probability density evolution method, PDEM) 的随机模拟方法，通过该方法可以在统一的概率空间中确定模型参数，从而确保两个独立模型的一致性。为进行说明，采用了 1999 年集集地震收集的观测记录生成了一组新的随机地震动集，并提供了基于经典随机模拟方法的两组地震动集作为比较。数值结果表明，所提近断层脉冲地震动随机模拟方法是有效的；模拟样本的地震峰值加速度和频谱特性统计量与观测站记录一致。此外，所提方法能够容纳与近断层脉冲型地震动相关的显著随机性和分量比例一致性，使其适用于近断层区域抗震结构的随机响应和可靠性分析。这种优势对于缺乏对模型参数相关性和随机性合理考虑的经典随机模拟方法来说是一个挑战。关键词: 近断层地震动, 随机模拟, 有限断层模型, 多变量连接函数, 速度脉冲模型, 概率密度演化方法Quantifying the near-fault effect and establishing a reasonable model of near-fault pulse-like ground motions are particularly important for seismic design of structures in near-fault regions. Given the pronounced randomness associated with earthquakes, this study first proposes a novel stochastic model of near-fault pulse-like ground motions by combining the improved finite-fault model (IFFM) and the multivariate copula-based velocity-pulse model (CVPM). Further, a probability density evolution method (PDEM) based stochastic simulation method is developed, by which the model parameters can be determined in a unified probability space so as to ensure the consistency of two independent models. For illustrative purposes, the observed records collected from the 1999 Chi-Chi earthquake are used to generate new stochastic ground motions set. Two ground motions sets based on classical stochastic simulation methods are also presented for comparison. Numerical results show that the proposed method for stochastic simulation of near-fault pulse-like ground motions is reliable; the statistics of peak ground accelerations and spectral characteristics of simulated samples are consistent with station records. Besides, the proposed method accommodates the noteworthy randomness and proportion consistency of components associated with near-fault pulse-like ground motions, making it suitable for the stochastic response and reliability analysis of seismic structures in near-fault regions. This superiority is challenging to classical stochastic simulation methods that lack reasonable consideration of randomness and correlation associated with model parameters.Keywords: Near-fault ground motions; Stochastic simulation; Finite-fault model; Multivariate copula; Velocity-pulse model; Probability density evolution method创新点 | Highlights
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Fig. 1. Physical meaning of parameters in Gabor wavelet
Fig. 2. Schematic of conditional sampling based on copulas
Fig. 3. Procedure of stochastic simulation of near-fault pulse-like ground motions
图 4: 1999 年集集地震 CHY 和 TCU 监测站的位置Fig. 4. Positions of CHY and TCU stations for monitoring 1999 Chi-Chi earthquake
Fig. 5. Inversion rupture information
Fig. 6. Relative positions of sub-faults and stations
Fig. 7. Amplitude spectrum parameters of IFFM
Fig. 8. Phase spectrum parameters of IFFM
图 9: 修正有限断层模型的当地站点参数 (I 类站点)Fig. 9. Local site parameters of IFFM (Site Class I)
图 10: 修正有限断层模型的当地站点参数 (II 类站点)Fig. 10. Local site parameters of IFFM (Site Class II)
图 11: 修正有限断层模型的当地站点参数 (III 类站点)Fig. 11. Local site parameters of IFFM (Site Class III)
Fig. 12. Phenomenological parameters of CVPM
Fig. 13. Pulse period T_pp of CVPM
Fig. 14. Copulas and their parameters in velocity-pulse model
Fig. 15. Comparison between sampling and fitting distributions of model parameters
Fig. 16. Theoretical probability density curves of copula trees
Fig. 17. Simulated probability density curves of the first copula tree (the 5th line of Fig. 16)
图 18: 子断层、台站 (II 类站点) 与模拟空间点的相对位置Fig. 18. Relative positions of sub-faults, stations (Site Class II), and simulated spatial points
Fig. 19. Example diagram of a sample of time history
Fig. 20. Example diagram of a sample without detected pulse effect
Fig. 21. Comparison of acceleration response spectra
Fig. 22. Comparison of peak value scatter of simulated samples by three stochastic models
Fig. 23. Comparison of acceleration response spectra by three stochastic models
作者信息 | Authors
同济大学 (Tongji Universtiy) 土木工程学院
彭勇波 Yong-Bo Peng, 通讯作者 (Corresp.) 同济大学 (Tongji Universtiy) 上海防灾救灾研究所Email: pengyongbo@tongji.edu.cn
律梦泽 M.Z. Lyu | 编辑 (Ed)
P.D. Spanos | 审校 (Rev)
陈建兵 J.B. Chen | 审校 (Rev)
彭勇波 Y.B. Peng | 审校 (Rev)
