辽西凹陷欢喜岭地区沙河街组层序格架的建立

对辽西凹陷欢喜岭地区沙河街组进行了深入细致的层序划分以寻找岩性地层圈闭,由于地震反射特征在地震资料上表现不明显,层序界面识别困难,应用经典层序地层学的原理和方法,综合运用欢喜岭地区地震和测井资料,借助地震时频分析技术和测井连续小波变换来确定层序界面,建立了该区高精度层序地层格架。研究表明,地震时频分析技术和测井小波变换进行层序旋回划分是较为可靠和实用的。沙河街组共发育有5个具有等时意义的层序界面,可划分为6个三级层序。     

Testing for spurious and cointegrated regressions: A wavelet approach

This paper proposes a wavelet-based approach to analyze spurious and cointegrated regressions in time series. The approach is based on the properties of the wavelet covariance and correlation in Monte Carlo studies of spurious and cointegrated regression. In the case of the spurious regression, the null hypotheses of zero wavelet covariance and correlation for these series across the scales fail to be rejected. Conversely, these null hypotheses across the scales are rejected for the cointegrated bivariate time series. These nonresidual-based tests are then applied to analyze if any relationship exists between the extraterrestrial phenomenon of sunspots and the earthly economic time series of oil prices. Conventional residual-based tests appear sensitive to the specification in both the cointegrating regression and the lag order in the augmented Dickey–Fuller tests on the residuals. In contrast, the wavelet tests, with their bootstrap t-statistics and confidence intervals, detect the spuriousness of this relationship.     

Wavelet-based estimation for multivariate stable laws

In this article, an estimation problem for multivariate stable laws using wavelets has been studied. The method of applying wavelets, which has already been done, to estimate parameters in univariate stable laws, has been extended to multivariate stable laws. The proposed estimating method is based on a nonlinear regression model on wavelet coefficients of characteristic functions. In particular, two parametric sub-classes of stable laws are considered: the class of multivariate stable laws with discrete spectral measure, and sub-Gaussian laws. Using a simulation study, the proposed method has been compared with well-known estimation procedures.     

Smoothed detrended fluctuation analysis

ABSTRACT

The method of detrended fluctuation analysis (DFA) is useful in revealing the extent of long-range dependence, it has successfully been applied to different fields of interest. In this paper we proposed a smoothed detrended fluctuation analysis method based on the principle of wavelet shrinkage. The procedure is illustrated and compared with the DFA method by Monte Carlo simulations on fractional Gaussian noise models.     

基于双参数的管道泄漏检测系统的研究

泄漏检测在油料长输管道安全生产中占有重要的位置,介绍了一种基于压力流量双参数的管道泄漏检测系统的总体设计。其中压力检测利用了负压波法,负压波拐点的提取引入了小波变换理论,对时间及负压波波速的确定提出了解决的方法。提高了泄漏检测灵敏度和定位精度。     

小波变换中的数据变换方法

在图象压缩、重构等处理中,二进正交小波变换是一个非常有用的工具,其函数形式为{2~(k/2)W_n(2~kt-j):n∈N;k,j∈z}(t是实数);在计算过程中,由于数据量太大,为了减少计算量应选择运算较快的算法,在计算机中浮点数比整数运算速度慢,若把t浮点数表示为形如A/[2~(K+1)](A是整数)的形式时,则2~k(t-j)为整数的形式,可以大大提高计算速度;在进行上述变换时,在考虑计算机字长p的限制下,为使在加、减、乘运算时,运算后的数据误差满足小于给定的ε,k、A如何取值,并得到了ε与p的一个近似关系式。     

数字HDTV可分级信源编码的双尺度小波实现

根据全数字ＨＤＴＶ信源编码可分级性的要求，提出了一种基于小波分解的分级实现方案。同传统的小波分解有所不同，该方案在水平、垂直方向上进行低、高分辨率为３：１的双尺度分解，它利用了正交镜像滤波器优良的混叠抵消特性。理论分析及实验结果表明，该方案是一种优良的ＨＤＴＶ分级技术。     

Projection-based curve clustering

This paper focuses on unsupervised curve classification in the context of nuclear industry. At the Commissariat à l'Energie Atomique (CEA), Cadarache (France), the thermal-hydraulic computer code CATHARE is used to study the reliability of reactor vessels. The code inputs are physical parameters and the outputs are time evolution curves of a few other physical quantities. As the CATHARE code is quite complex and CPU time-consuming, it has to be approximated by a regression model. This regression process involves a clustering step. In the present paper, the CATHARE output curves are clustered using a k-means scheme, with a projection onto a lower dimensional space. We study the properties of the empirically optimal cluster centres found by the clustering method based on projections, compared with the ‘true’ ones. The choice of the projection basis is discussed, and an algorithm is implemented to select the best projection basis among a library of orthonormal bases. The approach is illustrated on a simulated example and then applied to the industrial problem.     

Forecasting energy futures volatility with threshold augmented heterogeneous autoregressive jump models

Abstract

This study forecasts the volatility of two energy futures markets (oil and gas), using high-frequency data. We, first, disentangle volatility into continuous volatility and jumps. Second, we apply wavelet analysis to study the relationship between volume and the volatility measures for different horizons. Third, we augment the heterogeneous autoregressive (HAR) model by nonlinearly including both jumps and volume. We then propose different empirical extensions of the HAR model. Our study shows that oil and gas volatilities nonlinearly depend on public information (jumps), private information (continuous volatility), and trading volume. Moreover, our threshold augmented HAR model with heterogeneous jumps and continuous volatility outperforms HAR model in forecasting volatility.     

Wavelet and short-time Fourier transform comparison-based analysis of myoelectric signals

In this investigation, extracted features ofsignals have been analyzed for the recognition of arm movements. Short-time Fourier transform and wavelet transform based on Euclidian distance were applied to reordered signals. Results show that wavelet is a more useful and powerful tool for analyzing signals, since it shows multiresolution property with a significant reduction in the computation time for eliminating resolution problems. Finally, a statistical technique of repeated factorial analysis of variance for experimental recorded data was implemented in a way to investigate the effect of class separability for multiple motions for establishing surface electromyogram–muscular force relationship.