某水电站地下厂房岩体质量分级研究

根据某水电站地下厂房各勘探平硐现场统计的基础数据,采用RMR分类、Q系统法、水电系统分类法(HC法)、BQ法对地下厂区围岩岩体质量进行了分析评价,然后综合4种方法的评价结果,得到了各硐段的综合岩体质量类别,使这4种分类法相互补充、相互验证,达到综合确定围岩类别的目的,以此作为围岩稳定性分类的基础.此外,还对不同方法所得的评价结果进行了相关性分析.     

天山南麓山前平原土壤盐分空间异质性对植物群落组成及结构的影响

将轮台天山南麓山前平原中下部自北至南分为4个地貌带:洪水剥蚀带、溢出带、三角洲带及两河交汇区带。并以土壤电导作为积盐程度的指标,分析了天山南麓山前平原4个地貌带土壤盐的分布特征:溢出带和三角洲带土壤盐分含量高,两端洪水剥蚀带和两河交汇区带盐分含量低。物种多样性及物种组成分析结果表明,自北至南物种多样性及物种数量都在下降,洪水剥蚀带主要为柽柳群落、琵琶柴群落,溢出带主要为柽柳群落、盐节木群落、盐角草群落,三角洲带及两河交汇区均为柽柳群落。通过相关性分析,土壤盐分与群落物种多样性相关性不显著。但是,土壤盐渍化的变化明显影响到植物群落物种组成的变化、群落类型的空间分布和演替。     

Modelling bivariate rainfall distribution and generating bivariate correlated rainfall data in neighbouring meteorological subdivisions using copula

The rainfall patterns of neighbouring meteorological subdivisions of India are similar because of similar climatological and geographical characteristics. Analysing the rainfall pattern separately for these meteorological subdivisions may not always capture the correlation and tail dependence. Furthermore, generating the multivariate rainfall data separately may not preserve the correlation. In this study, copula method is used to derive the bivariate distribution of monsoon rainfall in neighbouring meteorological subdivisions. Different Archimedean copulas are used for this purpose and the best copula is selected based on nonparametric test and tail dependence coefficient. The fitted copula is then applied to derive the bivariate distribution, joint return period and conditional distribution. Bivariate rainfall data is generated with the fitted copula and it is observed with the increase of sample size, the generated data is able to capture the correlation as well as tail dependence. The methodology is demonstrated with the case study of two neighbouring meteorological subdivisions of North‐East India: Assam and Meghalaya meteorological subdivision and Nagaland, Manipur, Mizoram and Tripura meteorological subdivision. Copyright © 2010 John Wiley & Sons, Ltd.     

Analysis of the contributions of topographic,soil, and vegetation features on the spatial distributions of surface soil moisture in a steep natural forested headwater catchment

Surface soil moisture has been extensively studied for various land uses and landforms. Although many studies have reported potential factors that control surface soil moisture over space or time, the findings have not always been consistent, indicating a need for identification of the main factors. This study focused on the static controls of topographic, soil, and vegetation features on surface soil moisture in a steep natural forested headwater catchment consisting of three hillslope units of a gully area, side slope, and valley‐head slope. Using a simple correlation analysis to investigate the effects of the static factors on surface soil moisture at depths of 0–20 cm at 470 points in 13 surveys, we addressed the characteristics of surface soil moisture and its main controlling factors. The results indicated that the mean of surface soil moisture was in the decreasing order of gully area > valley‐head slope > side slope. The relationship between the mean and standard deviation of surface soil moisture showed a convex‐upward shape in the headwater catchment, a negative curvilinear shape in the gully area, and positive curvilinear shapes at the side and valley‐head slopes. At the headwater catchment and valley‐head slope, positive contributions of soil porosity and negative contributions of slope gradient and saturated hydraulic conductivity were the main controlling factors of surface soil moisture under wetter conditions, whereas positive contributions of topographic wetness index and negative contributions of vegetation density were the main controlling factors of surface soil moisture under drier conditions. At the side slope underlain by fractured bedrocks, only saturated hydraulic conductivity and vegetation density were observed to be the controlling factors. Surface soil moisture in the gully area was mainly affected by runoff rather than were static features. Thus, using hillslope units is effective for approximately estimating the hydrological behaviours of surface moisture on a larger scale, whereas dependency between the main static factors and moisture conditions is helpful for estimating the spatial distributions of surface moisture on a smaller scale.     

Identifying effects of local and nonlocal factors of soil water storage using cyclical correlation analysis

There are various factors governing the spatial and temporal variability of soil water storage including soil properties, topography and vegetation. Some factors act locally, whereas others act nonlocally, which means that a factor measured at one location has effect on soil water storage at another location. The objective of this study was to examine the effects of local and nonlocal controls of soil water storage in a hummocky landscape using cyclical correlation analysis. Soil water storage, soil properties and terrain indices were measured along a 128‐point transect of 576 m long from the semiarid, hummocky, prairie pothole region of North America. There are large coefficients of determination (r²) between soil water storage and sand content (r² = 0.32–0.53), organic carbon content (r² = 0.22–0.56), depth to carbonate layer (r² = 0.13–0.63), wetness index (r² = 0.25–0.45) and other variables at the measurement scale at different times, indicating strong local effects from these variables. The correlation coefficients were also calculated by physically shifting the spatial series of soil water storage with respect to that of controlling factors. The shifting improves the correlation between the spatial series, and the length of shifting indicated the difference in the response of soil water to its controlling factors. For example, the value of r² increased more than eightfold (r² = 0.47–0.64) after shifting the spatial series of soil water storage by 54 m, almost equal to the average length of existing slope, compared with the very weak correlation (r² = 0.02–0.08) at the measurement scale. This indicated the nonlocal effect from the relative elevation. The identification of nonlocal effects from factors improves the prediction of soil water storage. Copyright © 2011 John Wiley & Sons, Ltd.     

Correlation Between Total Lightning Activity and Precipitation Particle Characteristics Observed from 34 Thunderstorms

A total of 34 thunderstorms around Shanghai and Wuhan of China are analyzed in order to determine the relationship between total lightning activity and precipitation particle characteristics.Precipitation particle concentration data are obtained from the 2A12 product of TRMM/TMI(Tropical Rainfall Measuring Mission/TRMM Microwave Image) and lightning activity data are from the TRMM/LIS(Lightning Imaging Sensor) and SAFIR3000(Surveillance et Alerte Founder par Interferometric Radioelectirque).On a spatial ...     

Spatial and multivariate analysis of geochemical data from metavolcanic rocks in the Ben Nevis area, Ontario

A study of the lithogeochemistry of metavolcanics in the Ben Nevis area of Ontario, Canada has shown that factor analysis methods can distinguish lithogeochemical trends related to different geological processes, most notably, the principal compositional variation related to the volcanic stratigraphy and zones of carbonate alteration associated with the presence of sulphides and gold. Auto- and cross-correlation functions have been estimated for the two-dimensional distribution of various elements in the area. These functions allow computation of spatial factors in which patterns of multivariate relationships are dependent upon the spatial auto- and cross-correlation of the components. Because of the anisotropy of primary compositions of the volcanics, some spatial factor patterns are difficult to interpret. Isotropically distributed variables such as CO ₂ are delineated clearly in spatial factor maps. For anisotropically distributed variables (SiO ₂ ), as the neighborhood becomes smaller, the spacial factor maps becomes better. Interpretation of spatial factors requires computation of the corresponding amplitude vectors from the eigenvalue solution. This vector reflects relative amplitudes by which the variables follow the spatial factors. Instability of some eigenvalue solutions requires that caution be used in interpreting the resulting factor patterns. A measure of the predictive power of the spatial factors can be determined from autocorrelation coefficients and squared multiple correlation coefficients that indicate which variables are significant in any given factor. The spatial factor approach utilizes spatial relationships of variables in conjunction with systematic variation of variables representing geological processes. This approach can yield potential exploration targets based on the spatial continuity of alteration haloes that reflect mineralization.List of symbols c _i Scalar factor that minimizes the discrepancy between andU _i - D Radius of circular neighborhood used for estimating auto- and cross-correlation coefficients - d Distance for which transition matrixU is estimated - d _ij Distance between observed valuesi andj - E Expected value - E _i Row vector of residuals in the standardized model - F(d _ij) Quadratic function of distanced _ij F(d _ij)=a+bd _ij+cd _ij ² - L Diagonal matrix of the eigenvalues ofU - _i Eigenvalue of the matrixU;ith diagonal element ofL - N Number of observations - p Number of variables - Q Total predictive power ofU - R Correlation matrix of the variables - R _0j Variance-covariance signal matrix of the standardized variables at origin;j is the index related tod andD (e.g.,j=1 ford=500 m,D=1000 m) - R _1j Matrix of auto- and cross-correlation coefficients evaluated at a given distance within the neighborhood - R _m ² Multiple correlation coefficient squared for themth variable - S _i Column vectori of the signal values - s _k ² Residual variance for variablek - T _i Amplitude vector corresponding toV _i;ith row ofT=V ^–1 - T Total variation in the system - U Nonsymmetric transition matrix formed by post-multiplyingR ₀₁ ^–1 byR _ij - U _i Componenti of the matrixU, corresponding to theith eigenvectorV _i;U _i= _iV_iT_i - U* _i ComponentU _i multiplied byc _i - U _ij Sum of componentsU _i+U _j - V _i Eigenvector of the matrixU;ith column ofV withUV=VL - w Weighting factor; equal to the ratio of two eigenvalues - X _i Random variable at pointi - x _i Value of random variable at pointi - y _i Residual ofx _i - Z _i Row vectori for the standardized variables - z _i Standardized value of variable     

Generation of correlated properties in heterogeneous porous media

The spatial distribution of rock properties in porous media, such as permeability and porosity, often is strongly variable. Therefore, these properties usefully may be considered as a random field. However, this variability is correlated frequently on length scales comparable to geological lengths (for example, scales of sand bodies or facies). To solve various engineering problems (for example, in the oil recovery process) numerical models of a porous medium often are used. A need exists then to understand correlated random fields and to generate them over discretized numerical grids. The paper describes the general mathematical methods required to do this, with one particular method (the nearest neighbor model) described in detail. How parameters of the mathematical model may be related to rock property statistics for the nearest neighbor model is shown. The method is described in detail in one, two, and three dimensions. Examples are given of how model parameters may be determined from real data.     

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.     

桃山、诸广复式岩体花岗岩类岩石铀、钍地球化学特征

通过对桃山、诸广复式岩体的深入研究,得出铀、钍元素在本地区花岗岩中具有以下地球化学特征及地球化学行为:1.岩体的铀、钍丰度与岩体外接触带的变质岩铀,钍丰度具同步增长特点;2.铀与硅、钾、钠关系密切,并有随钾增长,及随铀增长二种趋势,而钍则与TiO_2、FeO、MnO关系密切;3.钍在花岗岩中分布型式为正态分布,变异系数较铀小,而铀在花岗岩中分布型式为正态分布及对数正态分布,变异系数略高,显示了铀有某种富集可能;4.铀在花岗岩中地球化学行为,表现有二个富集高峰,既有在结晶早期富集在各类副矿物中的早富集特征,也有随花岗岩形成和演化富集在晚期的晚富集特征。二个富集途径,即随Si、K增高和随Si、Na增高途径。