青岛港风暴潮经验统计预报

本文利用青岛港多年实测资料,分析了该港风暴潮概况。而后通过多元回归技术,求取了该港极值增减水的预报公式。经非独立和独立检验,结果令人满意。     

长牡蛎繁殖期糖原含量与类胰岛素基因表达的相关性分析

为探究长牡蛎在繁殖期间的糖原含量与类胰岛素基因(cgMIP123、cgMIP4、cgMILP7、cgILP)和相关转录调控因子(cgPdx)相对表达量的相关性,自2020年5月至2020年10月采集了山东胶南养殖海区的长牡蛎,测定了血糖含量、糖原含量、条件指数、类胰岛素基因相对表达量及环境因子(酸度、温度、盐度)等数据,采用多元统计方法对数据进行分析。logistic回归分析结果显示,长牡蛎配子排放前后血糖含量和内脏团糖原含量具有显著差异。构建的回归模型可以通过血糖含量和内脏团糖原含量准确判断配子是否已经排放,区分度C-index为0.903,Hosmer-Lemeshow拟合优度检验χ²值为9.06,P>0.05,验证结果显示该模型可靠。多元线性回归分析结果显示:条件指数与cgMIP123和cgMIP4基因的相对表达量具有相关性,R²为0.91,P值为0.0076,极显著相关;内脏团和唇瓣组织的糖原含量与ILPs和cgPdx相对表达量具有一定的相关性,其中cgMILP7的相对表达量与内脏团和唇瓣组织的糖原含量呈负相关,cgPdx相对表达量与唇瓣组织的糖原含量呈负相关,cgMIP4和cgILP的相对表达量与糖原含量呈正相关。     

Visual classification of very fine-grained sediments: Evaluation through univariate and multivariate statistics

Classifying very fine-grained rocks through fabric elements provides information about depositional environments, but is subject to the biases of visual taxonomy. To evaluate the statistical significance of an empirical classification of very fine-grained rocks, samples from Devonian shales in four cored wells in West Virginia and Virginia were measured for 15 variables: quartz, illite, pyrite and expandable clays determined by X-ray diffraction; total sulfur, organic content, inorganic carbon, matrix density, bulk density, porosity, silt, as well as density, sonic travel time, resistivity, and -ray response measured from well logs. The four lithologic types comprised: (1) sharply banded shale, (2) thinly laminated shale, (3) lenticularly laminated shale, and (4) nonbanded shale. Univariate and multivariate analyses of variance showed that the lithologic classification reflects significant differences for the variables measured, difference that can be detected independently of stratigraphic effects. Little-known statistical methods found useful in this work included: the multivariate analysis of variance with more than one effect, simultaneous plotting of samples and variables on canonical variates, and the use of parametric ANOVA and MANOVA on ranked data.     

Fitting the Linear Model of Coregionalization by Generalized Least Squares

In geostatistical studies, the fitting of the linear model of coregionalization (LMC) to direct and cross experimental semivariograms is usually performed with a weighted least-squares (WLS) procedure based on the number of pairs of observations at each lag. So far, no study has investigated the efficiency of other least-squares procedures, such as ordinary least squares (OLS), generalized least squares (GLS), and WLS with other weighing functions, in the context of the LMC. In this article, we compare the statistical properties of the sill estimators obtained with eight least-squares procedures for fitting the LMC: OLS, four WLS, and three GLS. The WLS procedures are based on approximations of the variance of semivariogram estimates at each distance lag. The GLS procedures use a variance–covariance matrix of semivariogram estimates that is (i) estimated using the fourth-order moments with sill estimates (GLS₁), (ii) calculated using the fourth-order moments with the theoretical sills (GLS₂), and (iii) based on an approximation using the correlation between semivariogram estimates in the case of spatial independence of the observations (GLS₃). The current algorithm for fitting the LMC by WLS while ensuring the positive semidefiniteness of sill matrix estimates is modified to include any least-squares procedure. A Monte Carlo study is performed for 16 scenarios corresponding to different combinations of the number of variables, number of spatial structures, values of ranges, and scale dependence of the correlations among variables. Simulation results show that the mean square error is accounted for mostly by the variance of the sill estimators instead of their squared bias. Overall, the estimated GLS₁ and theoretical GLS₂ are the most efficient, followed by the WLS procedure that is based on the number of pairs of observations and the average distance at each lag. On that basis, GLS₁ can be recommended for future studies using the LMC.     

致密砂岩孔隙度计算方法

鄂尔多斯盆地北部下二叠系下石盒子组为纵横向变化大,岩性、孔隙结构等复杂的河流相地层,很难用统一的计算公式来完成对孔隙度的计算。采用多元统计分析、散点图和孔隙度对比图来确定其孔隙度。运用上述方法,比较精确地确定鄂尔多斯盆地北部下二叠系下石盒子组的孔隙度,对鄂尔多斯盆地油气储量的估计,提供一个很好的基础资料。     

Extensions to Spatial Factor Methods with an Illustration in Geochemistry

Many applications involving spatial data require several layers of information to be simultaneously analyzed in relation to underlying geography and topographic detail. This in turn generates a need for forms of multivariate analysis particularly oriented to spatial problems and designed to handle spatial structure and dependency both within and between spatially indexed multivariate responses. In this paper we focus on one group of such methods sometimes referred to as spatial factor analysis. Use of these techniques has so far been mostly restricted to applications in the geosciences and in some forms of image processing, but the methods have potential for wider use outside these fields. They are concerned with identifying components of a multivariate data set with a spatial covariance structure that predominantly acts over a particular spatial range or zone of influence. We review the various forms of spatial factor analysis that have been proposed and emphasize links between them and with the linear model of coregionalization employed in geostatistics. We then introduce extensions to such methods that may prove useful in exploratory spatial analysis, both generally and more specifically in the context of multivariate spatial prediction. Application of our proposed exploratory techniques is demonstrated on a small but illustrative geochemical data set involving multielement measurements from stream sediments.     

基于GIS的证据权重法在冀东地区多元信息成矿预测中的应用

本文通过详细分析和系统总结前人的研究成果,充分利用目前新的成矿预测理论和矿产资源勘查与评价理论和方法技术,结合GIS技术分别对冀东地区主要的沉积变质型铁矿床成矿规律进行分析总结,并对与该区成矿相关的地质信息、物探信息、化探信息等多元化信息归纳,总结出该区找矿概念模型。继而在证据权重法的基础上采用网格单元法,圈定了找矿远景区,实现了对冀东地区沉积变质型铁矿资源的综合预测研究。     

Stepwise Conditional Transformation for Simulation of Multiple Variables

Most geostatistical studies consider multiple-related variables. These relationships often show complex features such as nonlinearity, heteroscedasticity, and mineralogical or other constraints. These features are not handled by the well-established Gaussian simulation techniques. Earth science variables are rarely Gaussian. Transformation or anamorphosis techniques make each variable univariate Gaussian, but do not enforce bivariate or higher order Gaussianity. The stepwise conditional transformation technique is proposed to transform multiple variables to be univariate Gaussian and multivariate Gaussian with no cross correlation. This makes it remarkably easy to simulate multiple variables with arbitrarily complex relationships: (1) transform the multiple variables, (2) perform independent Gaussian simulation on the transformed variables, and (3) back transform to the original variables. The back transformation enforces reproduction of the original complex features. The methodology and underlying assumptions are explained. Several petroleum and mining examples are used to show features of the transformation and implementation details.     

The control of maturity and kerogen type on quantitative analytical pyrolysis data

Quantitative pyrolysis-gas chromatography has been performed on 96 kerogen samples isolated from 17 wells on the Norwegian Continental shelf. Petrographic and bulk geochemical measurements were also performed on the samples, and a combined data set of 117 variables for each sample was analysed using principal components analysis (PCA). This approach provides an objective and reproducible means of kerogen characterisation, which can be easily automated. In addition to objective kerogen characterisation and facile visualisation of facies and maturity related chemical trends, the method has the potential to allow objective prediction of key geochemical parameters such as maturity level from pyrogram data.     

Estimation of a multidimensional covariance function in case of anisotropy

A model of a multivariate covariance function with an ellipsoidal directional correlation scale has been developed. The axes of the ellipsoidal scale are related to the eigenvalues and eigenvectors of a matrix B which characterizes the ellipsoid of the range of influence. The matrix B is found to be related to a matrix T which can be estimated directly from sparse sampling data and can be used to determine estimates of the matrix B. The method has been applied to both two-dimensional and three-dimensional cases. The numerical results show that the satisfactory accuracy is obtained with sparse sampling data from an anisotropic random function.