M变换在NOAA/AVHRR数据变化检测中的应用

将基于典型相关分析的正交变换的基础理论用于NOAA/AVHRR数据的多元变化检测 ,对具体实施步骤进行了深入的探讨 ,并对部分中间结果进行了分析。实验表明 ,该方法应用于NOAA/AVHRR数据的多元变化检测具有明显的优势 ,克服了传统方法存在的缺陷 ,具有良好的应用前景。     

Estimator Stability in Geological Applications

Classic mathematical statistics recommends maximum likelihood estimators of parameters of a model because they have minimal variance in the model. The theory of robustness showed that these estimators were unstable to small deviations of probability density. The estimator stability is necessary for applications, where reality is always more complex than any model, especially in geology, where objects are unique. Methods of calculus of variations give a measure of the estimator stability, and the maximum likelihood estimators have little stability. Simultaneous maximization of efficiency and stability gives new estimators more suitable for applications. The estimator instability is especially harmful in the estimation of the multivariate normal distribution. To avoid instability, multivariate problems are reduced to sequences of bivariate problems. An example of the solution of a geological problem shows that methods of classic statistics are not good and the reductive method is much better.     

基于GIS和证据权重法的那仁宝力格地区铜多金属矿成矿预测

根据那仁宝力格地区主要铜多金属矿床成矿规律,利用GIS平台提取地、物、化、遥等多元地质找矿信息,在MRAS系统中开展了基于18个成、控矿要素的铜多金属矿成矿预测与评价。根据后验概率值大小编制了成矿后验概率等值线图,划分了4个等级的找矿条件和9个找矿远景区。结果表明,预测模型有效,近95%的已知矿床（点）位于D级及以上找矿有利地区。在众多成、控矿要素中,增隆昌组与阿古鲁沟组、构造信息、遥感组合异常、铁族元素地球化学组合异常等具有较大权重。新圈定的那仁宝力格找矿远景区和霍各乞南部找矿远景区是下一步研究区寻找新的铜多金属矿床的重点地区。     

多元地球化学异常识别的核马氏距离方法

地球化学数据满足多元正态分布时,马氏距离是一种有效识别多元地球化学异常的综合指标。然而,由于地质系统的复杂性、成矿作用的多期多阶段性以及控矿因素的多重性常常导致多元地球化异常临界面是非线性的和模糊的,用马氏距离定义的平滑超椭球面不能准确表示这种复杂曲面。核函数能够将地球化学样品集非线性变换至特征空间,背景样品的映像集合在特征空间中构成一种流型,异常样品的映像则零散分布于流型的边缘及外围。计算和比较样品映像到样品映像总体的核马氏距离,可以识别异常样品。把该方法应用于白山地区多元地球化学异常识别,用核马氏距离、马氏距离和主成分得分识别金-银、金-银-砷-铋-汞、金-银-铜-铅-锌-锑-钴、金-银-铜-铅-锌-砷-锑-铋-汞-钴4种组合模式的多元地球化学异常。研究结果表明：复合核函数马氏距离的多元地球化学异常识别效果优于其他方法。     

Vibration‐based damage assessment of steel structure using global and local response measurements

Damage assessment of a structure involves acquiring and identifying dynamic characteristics of the structure and using these characteristics to evaluate behavior and performance. In this study, an unsymmetrical three‐story steel structure (fabricated with one weak column in the first floor) was tested on shaking table and subjected to a series of earthquake excitations with increasing level of excitation back to back. Besides, white noise excitation was also applied in between the earthquake excitation to serve as the reference state. Both the traditional sensing system (accelerometer and linear variable differential transformer) and the local optical tracker system were implemented in the structure to collect the vibration‐based responses. For operational modal analysis, structural response from white noise excitation will be used in this study. First, the traditional system identification using global response data is used (multivariate autoregressive (AR)‐model) to extract system natural frequencies and mode shapes from all different set of white noise responses after earthquake excitation. The migration of AR‐coefficient ellipse error from each sensor response was used to identify the damage location. Second, blind source separation technique was used to identify the modal contribution of the structure from each test, which provide information to detect the damage severity. Finally, from the local optical tracker array data, the principal component analysis was applied to quantify the earthquake‐induce local stress of the structural member. Combine the result from damage detection using global measurement and the identified local element stress, one can locate and quantify the damage. Copyright © 2015 John Wiley & Sons, Ltd.     

Compositional Data Analysis of Some Alkaline Glasses

The approach to the analysis of compositional data involving log-ratio transformation of the data has not been generally adopted by researchers wishing to analyse such data. In the context of exploratory methods of multivariate analysis, such as principal components analysis, where the hope is to identify (cluster) structure in the data, this may be because traditional methods can produce more interpretable results than the log-ratio approach. After illustrating this with an example, circumstances under which the log-ratio approach performs poorly when traditional approaches work well are identified. Log-ratio analysis can be dominated by variables having low absolute presence and high relative variation that do not contribute to, and can obscure, structure in the data. Traditional methods can detect certain kinds of structure in the data that correspond to structure on a ratio scale, after a suitable redefinition of the composition. Since traditional methods often detect such structure more directly than log-ratio analysis it can be concluded that claims that the traditional analysis is inappropriate or meaningless are exaggerated. This conclusion is based on empirical experience rather than theoretical concerns. The arguments are illustrated using compositional data for alkaline glasses, but have more general application.     

Significance tests for multivariate normality of clusters from branching patterns in dendrograms

A significance test is presented for whether, based on levels of branches in a dendrogram, a cluster is from a multivariate normal distribution. The method compares the observed cumulative graph of number of branches with a graph derived from a simple logistic function. Provided the number of objects or variables is not small, the difference between graphs can be tested by the Kolmogorov-Smirnov, Cramér-von Mises, and Lilliefors statistics.Logistic functions were obtained by simulation and are available for three similarity measures: (1) Euclidean distances, (2) squared Euclidean distances, and (3) simple matching coefficients, and for five cluster methods: (1) WPGMA, (2) UPGMA, (3) single linkage (or minimum spanning trees), (4) complete linkage, and (5) Ward's increase in sums of squares. For simple matching coefficient, the mean intracluster similarity also is required.The method allows a test of whether the dendrogram could be from a cluster of smaller dimensionality due to character correlations. Good fit of the data to abnormally large or small dimensionality provides an important warning to interpretation of the dendrogram. Quantiles of test statistics were found by simulation to be well-approximated by logistic functions. The Lilliefors test is recommended for general use; if a conservative test is required, the two-tailed Kolmogorov-Smirnov test is most suitable. The method is suitable for use with a hand calculator, and a computer program for it is available from the author.     

An Application of Multivariate Simulation in the Cement Industry

The profitability of a cement plant depends largely on its capacity to produce homogeneous cement with chemical composition close to specified targets for the cement type produced. One crucial step is the mixing of limestone with other raw materials in proportions calculated to meet these targets. Major design and operation decisions depend on the efficiency of this homogenizing step. The adequate modeling of the mixing process requires simulation of representative cross-correlated time series of chemical compositions of the raw materials involved. The chemical composition signals are obtained by multivariate geostatistical simulation using an LU (Cholesky) decomposition of the covariance matrix. Modifications to the usual LU method are presented. First, the effect on the raw covariance matrix of the closure property of chemical analysis is imposed. Second, the problem of memory space limitations in the LU method is tackled by using overlapping sliding neighbourhoods. The simulation algorithm is applied to the Joppa cement plant owned by Lafarge North America. The simulated raw material input streams are fed into the quality mix control (QMC), a proprietary software that models and controls the mixing operation to produce an output stream with cement characteristics as close as possible to desired targets. Two signal series are studied, one autocorrelated with a moderate temporal range and one with no autocorrelation. The QMC produces C₃S output signals having comparable short scale periodic variograms except that the variance of the uncorrelated signal is four times greater than those of the autocorrelated signal and the real Joppa data. The raw material feeder variograms have the same sill for both the white noise and the autocorrelated signals. However, the autocorrelated signal feeder variogram presents lower short term dispersion variance, a characteristic feature of Joppa operations. Our results show the importance of simulating the right temporal structure of the raw materials to realistically forecast the behavior of the output signals. We also discuss some practical implications of these findings for the design and operation of a cement plant.     

嵊泗列岛海域三种贻贝贝体框架特征的差异

以壳长SL、壳宽SW、壳高SH(BD)、OA(壳顶至韧带末端的直线距离)、OB(壳顶至壳背面最高点的直线距离)、OC(壳顶至壳后端最远点的直线距离)、OD(壳顶至壳高性状在腹缘的落点的直线距离)、AB(韧带末端至壳背缘最高点的直线距离)、BC(壳背缘最高点至壳后端最远点的直线距离)、CD(壳后端最远点至壳高性状在腹缘的落点的直线距离)为贝体框架变量,采用多元分析方法系统比较了嵊泗列岛海域厚壳贻贝、紫贻贝和"杂交贻贝"贝体框架特征的差异,结果表明:(1)在所涉9项贝体框架特征指标中,紫贻贝与厚壳贻贝间无显著差异的指标仅为L5(OC/SL)和L7(AB/SL)(P0.05),而"杂交贻贝"各项指标则均与厚壳贻贝和紫贻贝具显著差异(P0.05),厚壳贻贝和紫贻贝变异系数大于10%的指标均仅为L7(AB/SL),而"杂交贻贝"则仅为L3(OA/SL);(2)厚壳贻贝与紫贻贝间的欧氏距离最短(P0.05),仅为0.160;厚壳贻贝与"杂交贻贝"间和紫贻贝与"杂交贻贝"间的欧氏距离相近(P0.05),分别为0.452和0.418;(3)经主成分分析,提取到的3个特征值均大于1的主成分,累计贡献率达82.928%,其中第一主成分、第二主成分、第三主成分可依次归为与滤食功能区水平剖面占比相关的贝体框架因子,与消化功能区水平剖面占比相关的贝体框架因子,和与消化功能区垂直剖面占比相关的贝体框架因子,通过第一主成分仅能较清晰地区分厚壳贻贝和"杂交贻贝";(4)采用逐步判别法,以判别贡献率较大的L1(SW/SL)、L3(OA/SL)、L4(OB/SL)、L5(OC/SL)、L6(OD/SL)和L7(AB/SL)为自变量,所建Fisher分类函数方程组可较清晰区分厚壳贻贝、紫贻贝和"杂交贻贝",三者的判别准确率依次为94.6%、94.6%和100%,综合判别准确率为96.4%。     

二维复合极值模型在暴雨多变量联合分布中的应用

为了更好地描述暴雨多变量特征,构建以GPD为边缘分布的泊松-二维Copula复合极值模型,并将其应用于成都温江站暴雨雨量和暴雨雨峰的联合概率分布计算,研究表明该模型:理论联合频率与经验联合频率吻合较好,能用于暴雨联合分布的计算;考虑了暴雨过程出现的频次,增加了概率模型的物理内涵,选取独立的暴雨过程极大值作为样本,克服了GPD阈值选取的随意性;可以推广应用于连续分布为其他的Copula函数的情形,具有广泛的应用前景。