【摘要】 在2008年5月12日汶川MS8.0地震中,四川数字强震台网共获取了133组三分向加速度记录.本文选取了一些不同断层距的台站所获取的强震动记录进行了处理和分析.在数据处理中,采用基于聚类经验模态分解(EEMD)提取信号时频特性的方法,有效获得了信号能量的时频分布,提取了中心频率、Hilbert能量、最大振幅对应的时频等特性,并与傅里叶变换、小波变换进行了对比研究.研究结果表明,对非线性的强震记录采用聚类经验模态分解(EEMD)能抑制经验模态分解(EMD)中存在的模态混叠问题;与傅里叶变换和小波变换相比发现,HHT边际谱在低频处幅值高于傅里叶谱;与小波变换受到所选取的母波强烈影响不同,HHT直接从强震记录中分离出固有模态函数(IMF),更能反映出原始数据的固有特性,Hilbert谱反映出大部分能量都集中在一定的时间和频率范围内,而小波谱的能量却在频率范围内分布较为广泛.因此,基于EEMD的HHT在客观性和分辨率方面都具有明显的优越性,能提取到更多强震加速度记录的时频特性.更多还原

【Abstract】 During 12 May 2008 Wenchuan MS8.0 earthquake,Sichuan strong motion network obtained 133 sets of 3-componet acceleration records.This paper processed and analyzed some of the records with different station distance from the fault.Using a clustering algorithm based on the ensemble empirical mode decomposition(EEMD),this paper effectively extracted the time-frequency distribution of the signal energy,its central frequency,Hilbert energy and the time-frequency characteristics corresponding to the maximum amplitudes,and made a comparative study of EEMD method with Fourier transform and wavelet transform(WT) analysis.This study obtained the following results:For non-linear strong motion records,EEMD can be used to suppress the mode mixing effect existing in empirical mode decomposition(EMD) decomposition;in comparison with Fourier transform and wavelet transform the Hilbert-Huang transform(HHT) marginal spectrum amplitude is larger than the low frequency Fourier spectrum amplitude;different from strong influence of the selected parent wave on WT,HHT can directly isolate inherent mode function(IMF) from the strong motion record,representing inherent characteristics of the original data;Hilbert amplitude spectrum exhibits the concentration of most energy in a certain time and frequency range,while wavelet spectral energy distribute in a wide frequency range.Therefore,the HHT based on EEMD has obvious advantages in terms of its objectivity and high resolution,being able to extract more time-frequency characteristics of seismic acceleration records.更多还原