System identification of linear structures based on Hilbert–Huang spectral analysis. Part 2: Complex modes

A method, based on the Hilbert–Huang spectral analysis, has been proposed by the authors to identify linear structures in which normal modes exist (i.e., real eigenvalues and eigenvectors). Frequently, all the eigenvalues and eigenvectors of linear structures are complex. In this paper, the method is extended further to identify general linear structures with complex modes using the free vibration response data polluted by noise. Measured response signals are first decomposed into modal responses using the method of Empirical Mode Decomposition with intermittency criteria. Each modal response contains the contribution of a complex conjugate pair of modes with a unique frequency and a damping ratio. Then, each modal response is decomposed in the frequency–time domain to yield instantaneous phase angle and amplitude using the Hilbert transform. Based on a single measurement of the impulse response time history at one appropriate location, the complex eigenvalues of the linear structure can be identified using a simple analysis procedure. When the response time histories are measured at all locations, the proposed methodology is capable of identifying the complex mode shapes as well as the mass, damping and stiffness matrices of the structure. The effectiveness and accuracy of the method presented are illustrated through numerical simulations. It is demonstrated that dynamic characteristics of linear structures with complex modes can be identified effectively using the proposed method. Copyright © 2003 John Wiley & Sons, Ltd.     

利用MATLAB实现图像处理与分析

本文通过对图像的均衡操作、二值图像的处理、小波去噪等实例，介绍了MATLAB进行图像处理和分析的实用方法与技巧，它对各种软钉包、进行图像处理和分析等，提供了高效便捷的方法。     

Hilbert-Huang变换在密频结构阻尼识别中的应用

Hilbert—Huang变换是一种新的数据处理方法，由经验模分解(Empirical Mode Decomposition)技术及Hilbert变换两部分组成。本文研究此方法对于密频结构阻尼识别的应用。首先对于两自由度系统模型，说明该方法用于阻尼识别的步骤。进而研究存在频率密集现象的高层建筑的阻尼识别问题。上述结果与理论值及由半功率带宽法的识别值进行了比较，对比显示Hilbert．Huang方法较传统方法具有良好的识别密频结构阻尼的性能，适用于大型结构的系统识别。     

小波变换在识别储层流体性质中的应用

根据小波变换的特点提出一种识别储层流体的新方法. 本文假设测井所得的信号为地层微观孔隙结构、地层流体性质及岩性和岩相等各部分的综合贡献之和，不同的储层中小波分析的能量谱特征不同，因而通过对已知储层模型进行小波能量谱分析，可得到各种储层的特征能量谱，根据这些储层特征能量谱就可以对待划分的储层段进行储层流体性质判别.     

地震资料小波剖面制作系统

对不同的小波变换算法和小波基函数进行了研究，得到了适用于地震资料处理的最佳小波基函数，并用Visual C 语言开发了基于Windows操作平台下的地震资料小波剖面制作系统。利用小波剖面制作系统和最佳小波基函数对煤田实际地震资料进行了处理，取得了令人满意的地质效果。     

中国重力异常的小波变换与多尺度分析

阐述了小波变换及多尺度分析方法的原理，利用二维小波多尺度分析方法对中国布格重力异常进行了分解，并从其中划分出有意义的剩余异常．模型结果证明方法有效．由于本文方法可以将重力异常分解成各种几何尺度意义下的不同成分，这为重力解释和研究地壳的结构提供更多更新的依据．     

SHOULD BESTOW MORE PHYSICAL CONNOTATION ON GRAIN SIZE PARAMETER

1.INTRODUCnONSAn~sizeplaysanimpo~roleinmanyfields,SUChasinndmeDt-laden~ordebrisflow.TheStateofsedimentmovementwhateVertheyaresugunded.jUInpingorbed-loading,isoftencloselyrelatedtOtheirSizes.Thebodyor~forcesbornacaglainareOftendiredproponionedtotheabcorSq~sizeofthisgrain.Thereforesedimentsizeisanimpohalltindextoesedimentcharacteristics.Besides,depositsizeanditsdistributionimplysome~infonnationdepositingfonnationenvironmentandcauseing~hyandgeqlogy.Ina~on.thesizeanditsdistributionareeac…     

井间地震串联滤波波场分离方法

本文根据井间地震波场响应以及多种波场分离方法的性能，采用基于Ｋ－Ｌ变换的特征向量滤波与中值滤波相结合的串联滤波方法分离井间地震观测到的上、下行反射波。数值计算结果表明：在波场分离过程中，由于同时综合考虑了地震波能量主分量分布以及反射波同相轴时差两个参数，使得分离效果明显、处理更为简便，具有滤波特征矢量的选取条件更为宽松、无需精确确定拉平轴的时间等特点。     

Flexible spectral methods for the generation of random fields with power-law semivariograms

Random field generators serve as a tool to model heterogeneous media for applications in hydrocarbon recovery and groundwater flow. Random fields with a power-law variogram structure, also termed fractional Brownian motion (fBm) fields, are of interest to study scale dependent heterogeneity effects on one-phase and two-phase flow. We show that such fields generated by the spectral method and the Inverse Fast Fourier Transform (IFFT) have an incorrect variogram structure and variance. To illustrate this we derive the prefactor of the fBm spectral density function, which is required to generate the fBm fields. We propose a new method to generate fBm fields that introduces weighting functions into the spectral method. It leads to a flexible and efficient algorithm. The flexibility permits an optimal choice of summation points (that is points in frequency space at which the weighting function is calculated) specific for the autocovariance structure of the field. As an illustration of the method, comparisons between estimated and expected statistics of fields with an exponential variogram and of fBm fields are presented. For power-law semivariograms, the proposed spectral method with a cylindrical distribution of the summation points gives optimal results.