共查询到18条相似文献,搜索用时 390 毫秒
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阵列声波信号是典型的非线性、非平稳信号,其动力特性的量化提取对于进行地层结构构造分析提供了必要的基础资料.而Hilbert-Huang变换(HHT)是一种处理非线性、非平稳信号的新方法.它通过经验模态分解(EMD)将信号分解为有限个固有模态函数(IMF),并对每个固有模态函数进行Hilbert变换得到Hilbert谱.本文将这种方法应用于阵列声波信号动力特性的提取,有效地获得了信号能量的时频分布,瞬时能量、Hilbert能量、最大振幅对应的时频分布等动力特性,显示了HHT的优势以及对于进一步实现地层结构构造分析的重要意义. 相似文献
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《地震工程与工程振动》2017,(1)
为了能够从地震勘探记录中提纯出瑞雷波信号,基于信号经验模态分解的自适应特征,将经验模态分解应用到瑞雷波信号提纯研究中。介绍了瑞雷波勘探实测信号的经验模态分解计算过程,通过仿真实验及工程实例,说明经验模态分解能够有效应用于瑞雷波的提纯,分析比较了经验模态分解提纯方法和其他方法的优缺点,认为经验模态分解方法具有可靠性和稳定性。提出下一步的研究方向是在经验模态分解后直接利用Hilbert变换求得信号的瞬时频率,从而求解出频散曲线。 相似文献
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周挚(2005)的重力固体潮研究表明,EMD分解后的重力固体潮IMF(本征模态函数)的信号特征与AM-FM(调频调幅)信号非常接近.AM-FM信号是典犁的非平稳信号.基于AM-FM研究的前沿性,本文借助于函数构造理论和非线性逼近方法,求取重力固体潮IMF信号的数学显式,为进一步的解析工作做好铺垫.首先建立AM-FM数学模型:AM-FM信号y(t)=A(f)*F(t).依据拉格朗日定理、傅立叶级数基本性质和三角函数基本性质,分别用若干正弦函数之和的级数形式来描述AM信号A(t)和FM信号F(t). 相似文献
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基于Hilbert-Huang变换和随机子空间识别技术提出了两种土木工程结构的模态参数识别方法。方法一是基于Hilbert-Huang变换和自然激励技术,通过经验模态分解和Hilbert变换提取信号的瞬时特性,进而利用自然激励技术和模态分析的基本理论识别结构的模态参数;方法二是基于经验模态分解和随机子空间识别技术,通过经验模态分解对信号进行预处理,进而运用随机子空间识别方法处理得到的结构单阶模态响应以识别结构的模态参数。利用这两种方法,通过对一12层钢筋混凝土框架模型振动台试验测点加速度记录的处理,识别了该模型结构的模态参数。识别结果与传统的基于傅里叶变换的识别结果及有限元分析结果的对比验证了这两种方法的可行性和实用性。 相似文献
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HHT的滤波特性及在声波测井波列信号处理中的应用(英文) 总被引:2,自引:2,他引:0
阵列声波信号是典型的非线性、非平稳信号,Hilbert~Huang变换(HHT)是处理非平稳信号的一种比较新的时频分析方法。通过对信号进行经验模态分解(EMD)和对瞬时频率的求解,可以获得声波信号的时一频谱。其关键技术就是进行经验模态分解,任何非平稳的信号都可以分解为有限数目并且具有一定物理意义的固有模态函数。EMD方法可以理解为以声波信号极值特征尺度为度量的时频滤波过程。滤波器充分保留了声波信号本身的非线性和非平稳特征,在声波信号的滤波和去噪中具有很大的优势。文中介绍了HHT时频滤波的实现过程,并列举了一些声波测井波列实例,说明了该方法的有效性。 相似文献
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重力固体潮信号是一种多谐波的混合信号,为了提取其中所包含的地震前兆异常信息.本文结合重力固体潮的产生机制,建立了一种重力固体潮正交分解模型.在此基础上,利用独立分量分析算法实现重力固体潮信号的加性分解,然后针对独立分量中的调制关系,利用谱相关方法对其进行乘性解调.从而,完整地提取出了重力固体潮信号中丰富的潮汐谐波信息.进一步,引入理论计算值作为实际测量值的参考背景,在独立分量中凸显出原重力固体潮信号中的异常变化特征.通过对云南地区的实际震例研究表明,重力固体潮独立分量的异常特征与地震事件的时序存在密切的相关性.在地震发生前的1~5个月内,对应于重力固体潮信号长周期谐波系的独立分量在时域波形和循环相关谱方面均有明显的异常变化,而且普遍存在,充分反映了这一异常变化与地震前地壳内部能量的变化有关,很有可能就是重力固体潮信号中隐含的地震前兆信息. 相似文献
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Hilbert-Huang Transformation(HHT)是一种新的非线性信号处理方法(Huang,1998).通过HHT对信号进行经验模态分解(empirical mode decomposition,简称EMD),能有效地把各种频率成分以本征模态函数(intrinsic mode function,简称IMF)形式从中分离出来.再对IMF序列进行Hilbert变换,可得到包含时间、频率、振幅的三维离散时频谱.它提供了非常清晰的局部细节时频特征,适合于描述具非线性非平稳性变化特征的信号. 相似文献
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地球固体潮观测对验证各种地球模型、研究地球内部构造具有重要意义,可为地震预报提供重要的参考依据.BBVS-120甚宽带地震计具有较低的自噪声水平,在低频端输出信号的频率很低,涵盖固体潮信息频带范围.本文提出:BBVS-120地震计输出信号中存在周日波、半日波和1/3日波的固体潮信号,利用小波分析,提取垂直分量中的重力固体潮信号,并作调和分析. 相似文献
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在简要介绍时程信号的小波分析和Hilbert-Huang变换(HHT)理论的基础上,通过地震波和其它时程信号实例,对比分析了小波变换和HHT变换结果. 比较显示:HHT变换和小波变换均能用于对非平稳的信号进行分析,并能捕捉到信号变化的主要特征;与受所选母波影响较大的小波分析不同,HHT变换得到的固有模态函数是直接从原始时程数据中分离出来的,它更能反映原始数据的固有特性;小波分析得到的谱的能量在频率范围内分布较广,而HHT变换的Hilbert谱的大部分能量都集中在一定的时间和频率范围内,能清晰地刻画信号能量随时间、频率的分布. 因此,Hilbert-Huang变换不仅是对非平稳信号进行分析的有效方法,而且也是检测时程信号局部特征的有用工具. 相似文献
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在重力固体潮地震前兆分析中引入HHT时频分析新方法.结合HHT的优越性、固体潮的特点和地震的非平稳过程特性,设计重力固体潮地震前兆分析的瞬时频率特征参数;以相应理论计算值作为参照背景,研究固体潮的震前变化特征.昆明、下关的震例分析表明, 的确存在瞬时频率特征参数的震前变化,且具短期、同步正异常特征;瞬时频率特征参数具有明确的物理意义,其震前变化反映了地震非平稳过程对理论重力固体潮的影响. 相似文献
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The brief theories of wavelet analysis and Hilbert-Huang transform (HHT) are introduced firstly in the present paper. Then several signal data were analyzed by using wavelet and HHT methods, respectively. The comparison shows that HHT is not only an effective method for analyzing non-stationary data, but also is a useful tool for examining detailed characters of time history signal. 相似文献
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Chih-Sung ChenYih Jeng 《Journal of Applied Geophysics》2011,75(1):113-123
An alternative data processing procedure is proposed in this paper for the purpose of enhancing the signal/noise (S/N) ratio of ground penetrating radar (GPR) data. The processing methodology is achieved by performing the logarithmic transform in conjunction with the ensemble empirical mode decomposition (EEMD), a new nonlinear data analysis method in signal processing. The synthetic model study and field example indicate that the logarithmic transform is effective in alleviating the attenuation problem. Additionally, the spectrogram obtained from Hilbert-Huang transform (HHT) shows that the decomposition sensitivity of the EEMD method is greatly improved with the aid of the logarithmic transform. This new method allows us to extract the signal components from noisy GPR data efficiently. The success of this study suggests a possible nonlinear analysis application in future GPR investigation, particularly in the filter design and gain correction. 相似文献
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自然过程大都具有非线性非平稳性,一种自适应数据分析方法对于分析这些过程来说是极其必要的。Huang于1998年创立的H ilbert-Huang变换(HHT)就是这样一种自适应性非线性非平稳数据分析方法,该方法由经验模态分解(EMD)和H ilbert变换两部分组成。文中首先详细解释了HHT的思想和基本理论;然后介绍了该方法近期的重要研究进展,列举了其在科研和工程领域的应用;最后,对方法中仍然存在的问题进行了讨论。 相似文献
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Dong Yinfeng Li Yingmin Xiao Mingkui Lai Ming 《Soil Dynamics and Earthquake Engineering》2008,28(1):7-19
Some limitations of the Hilbert–Huang transform (HHT) for nonlinear and nonstationary signal processing are remarked. As an enhancement to the HHT, a time varying vector autoregressive moving average (VARMA) model based method is proposed to calculate the instantaneous frequencies of the intrinsic mode functions (IMFs) obtained from the empirical mode decomposition (EMD) of a signal. By representing the IMFs as time varying VARMA model and using the Kalman filter to estimate the time varying model parameters, the instantaneous frequencies are calculated according to the time varying parameters, then the instantaneous frequencies and the envelopes derived from the cubic spline interpolation of the maxima of IMFs are used to yield the Hilbert spectrum. The analysis of the length of day dataset and the ground motion record El Centro (1940, N–S) shows that the proposed method offers advantages in frequency resolution, and produces more physically meaningful and readable Hilbert spectrum than the original HHT method, short-time Fourier transform (STFT) and wavelet transform (WT). The analysis of the seismic response of a building during the 1994 Northridge earthquake shows that the proposed method is a powerful tool for structural damage detection, which is expected as the promising area for future research. 相似文献
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Due to strong heterogeneity of marine carbonate reservoir, seismic signals become more complex, thus, it is very difficult for hydrocarbon detection. In hydrocarbon reservoir, there usually exist some changes in seismic wave energy and frequency. In their instantaneous spectrums there often exist such phenomena that show the characteristics of attenuation of high frequency energy and enhancement of low-frequency energy. The three EMD-based time-frequency analysis methods' instantaneous spectra all have certain oil and gas detection capability. In this paper, we introduced the Normalized Hilbert Transform (NHT) and a new method named the HU method for hydrocarbon detection. The model results in the Jingbian Gas Field which is located in the eastern Ordos Basin, China, show that NHT and HU methods can be adopted. They also detect the gas-bearing reservoir efficiently as the HHT method does. The three EMD-based methods, that is, the Hilbert–Huang transformation (HHT) and NHT and HU methods, were respectively applied to analyze the seismic data from the Jingbian Gas Field. Firstly, the seismic signals were decomposed into a finite number of intrinsic mode functions (IMFs) by empirical mode decomposition (EMD) method. The second IMF signal (IMF2) of the original seismic section better indicates the distribution of the reservoir. Information on hydrocarbon-bearing reservoir is mainly in IMF2. Secondly, the HHT, NHT and HU methods were respectively used to obtain different frequency division sections from IMF2. Hydrocarbon detection was realized from the energy distribution of the different frequency division sections with these three EMD-based methods. The practical application results show that the three EMD-based methods can all be employed to hydrocarbon detection. Frequency division section of IMF2 using NHT method was better for the seismic data from the Jingbian Gas Field than when using the HHT method and HU method. 相似文献