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EMD方法和Hilbert谱分析法的应用与探讨 总被引：56，自引：0，他引：56
利用EMD方法对海浪观测资料进行处理，通过在数据两端的“平衡位置”处分别附加平行直线段的方法进行端点抑制，分解出10个内在模函数和1个剩余趋势项，再对各内在模函数进行Hilbert变换，得到波浪的Hilbert谱，对所得结果的分析表明，各模态在Hilbert谱中的分布趋势和Fourier谱中谱线的变化趋势是一致的，第一模态的中心频率与Fourier谱的谱峰频率相对应；EMD方法是对非线性，非平稳过程数据进行距平化的好方法，距平化的过程和消除趋势项的处理是统一的。 相似文献
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采用径向基函数神经网络法延拓原始数据序列,有效抑制了EMD分解中出现的端点发散效应,从而实现准确的EMD分解。利用该方法对中国近海验潮站的月平均海平面资料进行处理,分解得到的内在模函数分量代表了海平面各种周期性变化。通过EMD分解得到的总体自适定趋势项为非线性变化,比以往趋势项提取方法更有优势,它反映了在资料长度内海平面的长期升降情况。数据序列越长,该方法所能分解出来的IMF成分越多,可分辨的频率越小。 相似文献
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根据2012年6月在海州湾湾顶浅海区利用ADCP定点测流以及CTD等观测资料,采用经验模态分解(EMD)和Hilbert变换方法,对该海区海流的垂向流速分量及其影响因素进行了分析,研究了垂向流速分量的频率结构及其随时间的变化。结果表明,海州湾湾顶浅海区垂向流速分量变化不规则,以团块状分布为主。经验模态分解显示垂向流速分量存在不同的模态,各模态对整个波动的贡献差异明显,其中心频率构成具有垂向变频特征。垂向流速分量与水平流速分量在低频模态和趋势项具有较好的相关性,表明垂向流速分量与水平流速分量具有一定的联系。 相似文献
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《中国海洋大学学报(自然科学版)》2016,(4)
针对经验模式分解(EMD)易受噪声影响的缺点,提出采用小波消噪结合Hilbert-Huang变换的结构损伤检测方法。首先对含噪声振动信号进行小波消噪预处理实现信噪分离,再进行EMD分解得到若干固有模态函数(IMF),利用希尔伯特(Hilbert)变换得到其瞬时频率。提出了一个基于低阶瞬时频率变化率的损伤判定指标,利用此指标可以判断损伤是否发生。数值研究结果表明小波消噪结合HHT的方法是进行损伤检测比较有效的方法。 相似文献
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经验模态分解(empirical mode decomposition,简称EMD)是反演海洋内波参数的有效方法之一,但由于EMD存在模态混叠等问题,对海洋内波进行参数反演时会产生一定误差。相比较于EMD,变分模态分解(variational mode decomposition,简称VMD)能够有效地抑制模态混叠现象。为了更好地对海洋内波进行参数反演,提出了一种基于VMD对合成孔径雷达(synthetic aperture radar,简称SAR)遥感图像中的内波参数进行自动反演的方法。该方法先对SAR图像进行Canny处理,获取图像中的内波条纹信息,再根据内波传播方向自动选取灰度剖面;然后利用集合经验模态分解(ensemble empirical mode decomposition,简称EEMD)信号特征自适应分解模态函数的特点,再将分解得到的有效模态数作为VMD中参数K的参考值;最后利用VMD分解后的数据进行内波参数反演。试验结果表明:通过对Canny预处理后的条纹信息进行灰度剖面自动选取,解决了人为选取剖面所可能导致的误差;通过对剖面信号进行VMD处理不仅解决了EMD模态混叠的问题,成功地反演出内波的前导波振幅,而且所反演的结果与EEMD反演参数以及实测资料数据吻合得很好。 相似文献
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为提高非线性和非平稳海水温度时间序列的预测能力,提出了一种基于经验模态分解(Empirical Mode Decomposition.简称EMD)的BP神经网络预测方法.该方法首先对原始序列进行经验模态分解,将其分解为多个平稳性得到很大改善的本征模态函数(Intrinsic Mode Function,简称IMF)之和,然后时每个本征模态函数进行预测,最后再根据EMD方法的完备性把预测结果相加得出原始序列的预测结果.预测试验结果表明.基于EMD的BP神经网络预测的精度比单纯用BP神经网络预测有很大提高. 相似文献
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针对海洋地震资料高分辨率宽频处理的要求,也为了克服常规谱白化、反Q滤波等方法无法同时增强时域和频域局部细节的不足,基于希尔伯特黄变换(HHT)的谱白化方法对海洋地震资料进行高分辨率处理应用研究。对地震记录进行固有模态分解(EMD),得到不同尺度的IMF分量;再利用常规谱白化方法对每个分量根据瞬时频率进行合理的振幅均衡;将均衡后的IMF分量进行反变换重构地震记录,从而得到高分辨地震数据。通过理论模型和实际地震资料实验,与常规谱白化方法的对比分析,表明该方法信号局部时频刻画能力以及相对振幅保真性优于常规方法,同时表明此方法能够有效增强地震信号时域和频域的分辨率,使地震剖面更为连续和清晰,并具有较高的信噪比。 相似文献
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基于拟层流风波生成机制的海浪谱模型 总被引：1，自引：0，他引：1
海浪谱的能量可以视为由具有不同相速度的谐波携带的能量所组成。基于对风波形成、发展过程的认识,认为各组成谐波的能量由谐波自平均风摄取而来,由此根据拟层流模型推导出谐波能量密度的计算公式,建立以等效风速和峰值频率等为基本参数的海浪谱模型——随机Fourier函数模型,并给出了确定谐波频率、波长、相速度、振幅以及等效风速等模型参数的原则和计算方法。在59个实测样本谱基础上,采用随机建模方法确定模型参数的取值及其概率分布。结果表明,海浪谱模型可以很好地预测谱能,所计算的物理谱与实测谱均值吻合良好。 相似文献
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A method based on empirical mode decomposition (EMD) and time-varying autoregressive (TVAR) model is proposed here to identify the modal parameters of time-varying systems, such as the Floating Production Storage and Offloading (FPSO) single point mooring system. For the EMD–TVAR method, the original signal is decomposed into a finite number of ‘intrinsic mode functions’ (IMFs) by the EMD. Each IMF can be represented as a TVAR model. Then, the time-varying modal parameters i.e., instantaneous frequency (IF) and modal dumping, can be obtained by the basis functions expansion method. The proposed EMD–TVAR method has good results in two experiments compared with the Huang–Hilbert transformation and Short Time Fourier Transform method, and it has been used to analysis the modal parameters of FPSO single point mooring system successfully. The system's time-varying characteristic and its frequency distribution can be known from the modal analysis results. 相似文献
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本文采用经验模式分解 (EMD)提取信号的内在模函数 (IMF) ,并利用希尔伯特变换对所得IMF进行包络分析 ,提取机械故障特征。与直接对原信号进行包络分析相比较 ,该方法提取的机械故障特征更明显。数值模拟和对故障轴承振动信号分析表明了该方法的有效性。 相似文献
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异常事件对EMD方法的影响及其解决方法研究 总被引：4，自引：1，他引：4
赵进平 《中国海洋大学学报(自然科学版)》2001,31(6):805-814
作者指出异常事件在数据中形成局部的高频信号 ,运用经验模态分解 (EMD)方法分析这种存在异常事件干扰的数据 ,就会产生本征模函数 (IMF)的频率混叠现象 ,而造成物理过程的重叠 ,使得难以用时间过程曲线表现特定的物理过程。这一问题是 EMD方法中尚未妥善解决的问题。为解决这一问题 ,作者利用干扰信号极值及其两边的极大与极小值位置与原始数据有明显对应关系的特征 ,将相关 IMF中的异常信息直接滤除 ,再用 Spline插值方法弥补滤除时段的数据 ,得到重新拟合的该 IMF数据。采用这种方法可以提取出异常信号 ,提取的精度与异常信号的时段长度有关。而且 ,拟合结果消除了异常干扰 ,可以将该 IMF与其余 IMF一起叠加成没有异常干扰的数据。将滤除了异常干扰的数据再次进行 EMD分解 ,可以得到新的 IMF系列 ,而它与不加校正的分解结果有相当大的差别 ,可靠地反映了真实物理过程。结果表明 ,只有在有效滤除异常干扰的情况下才能获得可靠的 IMF系列 ,并准确地描述各种尺度的现象 ;消除了异常干扰的 IMF可以任意单独或组合使用 ,表现各种时间尺度的变化与过程 ;所讨论的方法只适合异常时段较小的情形。对于异常时段接近或大于正常变化周期的干扰还需要探讨其他方法 相似文献
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We use the Hilbert–Huang transform (HHT) for the spectral analysis of a North Sea storm that took place in 1997. We look at the contribution of the different Intrinsic Mode Functions (IMF) obtained using the Empirical Mode Decomposition algorithm, and also compare the Hilbert Marginal Spectra and the classical Fourier Spectra for the data set and the corresponding IMFs. We find that the number of IMFs needed to decompose the data and the energy associated to them is different from previous studies for different sea conditions by other authors. A tentative reason for this may lie in the difference in the sampling rate used. 相似文献
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应用经验模式分解（EMD）将恒电量瞬态响应信号分解为不同时间尺度的内在模函数（IMF）分量。去除其中的小时间尺度的干扰噪声分量。然后经过拉普拉斯变换获得恒电量频谱以研究电化学腐蚀过程。 相似文献
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The frequency attenuation gradient method can provide important information for hydrocarbon detection. In this paper, a method using Complete Ensemble Empirical Mode Decomposition (CEEMD), Hilbert transform and the least-squares curve-fitting is proposed for seismic attenuation estimation as an effective frequency attenuation gradient estimation approach. We first use CEEMD to obtain the different Intrinsic Mode Functions (IMFs), which have a narrow band and can enhance the physical meaning of instantaneous attributes trace by trace. The time-frequency spectrum, which is computed using a Hilbert transform of each IMF, is represented as a spectrum with a single-peak that has narrow side lobes, which is conducive to frequency attenuation gradient estimation. Second, for each time sample, the frequency-amplitude spectrum of each IMF trace is extracted from the time-frequency spectrum to conduct the attenuation gradient computation. Then, the logarithm operation is performed for each IMF trace. Due to the very narrow bands of some IMFs in some seismic traces, a variable frequency window is adopted along the IMF trace according to the local data characteristics. Finally, the attenuation gradient for each IMF in a seismic trace can be computed using least-squares fitting. A different IMF reflects a seismic trace with a different spatiotemporal scale and can highlight different geologic and stratigraphic information. The correlation weighted average operation is used to highlight some useful details in seismic trace and obtains the attenuation gradient for each seismic trace. Field data examples demonstrate our method and its effectiveness. The proposed method can stably estimate the frequency attenuation gradient. 相似文献
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Sonar generated acoustic signals transmitted in underwater channel for distant communications are affected by numerous factors like ambient noise, making them nonlinear and non-stationary in nature. In recent years, the application of Empirical Mode Decomposition （EMD） technique to analyze nonlinear and non-stationary signals has gained much attention. It is an empirical approach to decompose a signal into a set of oscillatory modes known as intrinsic mode functions （IMFs）. In general, Hilbert transform is used in EMD for the identification of oscillatory signals. In this paper anew EMD algorithm is proposed using FFT to identify and extract the acoustic signals available in the underwater channel that are corrupted due to various ambient noises over a range of 100 Hz to 10 kHz in a shallow water region.Data for analysis are collected at a depth of 5 m and 10 m offshore Chennai at the Bay of Bengal. The algorithm is validated for different sets of known and unknown reference signals. It is observed that the proposed EMD algorithmidentifies and extracts the reference signals against various ambient noises. Significant SNR improvement is alsoachieved for underwater acoustic signals. 相似文献
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《Ocean Engineering》2004,31(14-15):1783-1834
This paper relates to the newly developed Hilbert–Huang transformation (HHT). An overview of this time-frequency analysis technique and its applications are given. Key elements of the numerical procedure and principles of the Hilbert transformation (HT) are established. A simple parameter study with trigonometric functions to get an idea about the numerical performance of the empirical mode decomposition (EMD) is performed. The main results of estimating relative standardized errors made between analytically exact defined sine waves and disintegrated intrinsic functions as well as their specific influence on each other are determined. Practical applications are carried out next to evaluate computed nonlinear irregular water waves based on Stokes perturbation expansion approach and measurements on fully nonlinear irregular water waves recorded in a laboratory wave flume. Correspondence between simulated and recorded wave trains is given for narrow-banded fundamental components. Deviations are unveiled when carrier and riding waves get broad banded. Time-dependent spectral representation shows signs of an interesting phenomenon as instantaneous frequencies and amplitudes exhibit strong correlations with water surface elevations of both numerical and measured data series. 相似文献
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针对全波形三维激光测绘雷达(LiDAR)在数字地形测量中如何降低背景噪声问题,提出了一种基于经验模态分解(EMD)和小波阈值的自适应降噪方法。在扫测的地形信号经EMD分解后,计算内蕴模式函数(IMF)与经过2/3阶重构的扫测信号之间的互相关函数,从而改善小波阈值自适应地对IMF中的高频噪声成分进行滤除。实验结果表明,与EMD重构降噪法、小波阈值降噪法和传统的EMD-小波联合降噪法比较,这种方法在对全波形LiDAR回波信号的噪声剔除和地物信号保留方面具有明显的优势,降噪后信号的误差能缩小10%~20%,波形相关性能提升5%~20%,信噪比能提升20%~40%。 相似文献
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