Multivariate techniques as alternative statistical tools applied to magnetic proxies for pollution: a case study from Argentina and Antarctica

Several statistical analyses—as alternative tools—were applied to magnetic monitoring studies. Magnetic and chemical data from two environments have been gathered from previous papers and studied separately. Univariate and multivariate analyses were first examined, revealing a link between magnetic and chemical variables. The latter analyses, in particular, canonical correlation analysis, showed very good canonical correlations: R = 0.950 (Antarctica) and R = 0.891 (Argentina). On the other hand, in order to classify the data according to the degree of contamination, principal coordinates and discriminant analyses, as well as the comparison of several multivariate means were performed. Three groups were distinguished in both case studies, which were well classified at a low margin of error and quite different from each other at a significant level: 0.01 (Antarctica) and 0.05 (Argentina). The joint use of these statistical analyses also showed, in agreement with previous studies, that the relevant variables in order to identify atmospheric pollution are: magnetic susceptibility, saturation of isothermal remanent magnetisation, anhysteric susceptibility/magnetic susceptibility, remanent coercivity, and copper, lead, zinc and chromium contents.     

An Approach for the Classification of Urban Building Structures Based on Discriminant Analysis Techniques

Recognition of urban structures is of interest in cartography and urban modelling. While a broad range of typologies of urban patterns have been published in the last century, relatively little research on the automated recognition of such structures exists. This work presents a sample‐based approach for the recognition of five types of urban structures: (1) inner city areas, (2) industrial and commercial areas, (3) urban areas, (4) suburban areas and (5) rural areas. The classification approach is based only on the characterisation of building geometries with morphological measures derived from perceptual principles of Gestalt psychology. Thereby, size, shape and density of buildings are evaluated. After defining the research questions we develop the classification methodology and evaluate the approach with respect to several aspects. The experiments focus on the impact of different classification algorithms, correlations and contributions of measures, parameterisation of buffer‐based indices, and mode filtering. In addition to that, we investigate the influence of scale and regional factors. The results show that the chosen approach is generally successful. It turns out that scale, algorithm parameterisation, and regional heterogeneity of building structures substantially influence the classification performance.     

Projection of climate change for daily precipitation: a case study in Shih‐Men reservoir catchment in Taiwan

A statistical downscaling model, based on the outputs of general circulation models (GCMs) as predictors, was proposed to simulate the daily precipitations in the Shih‐Men reservoir catchment in Taiwan. The structure of the proposed downscaling model is composed of two parts: classification and regression. Predictors of classification and regression models were selected from the large‐scale weather variables in the National Centers for Environmental Prediction (NECP) reanalysis data based on statistical tests. Discriminant analysis and support vector regression (SVR) were applied to build the classification and regression models. The outputs of five atmosphere‐ocean GCMs, which are reported to have properly considered tropical cyclone information and East Asian Monsoon modelling, were used for projecting future precipitations. Data from four grids covering Taiwan were used for developing the downscaling model. The potential of the downscaling models in simulating local precipitations was evaluated, and downscaling results reveal that the proposed downscaling model can reproduce local daily precipitations from large‐scale weather variables. Projected local precipitations under two emission scenarios show that the precipitations in the study area tend to decrease. Copyright © 2010 John Wiley & Sons, Ltd.     

Which catchment characteristics control the temporal dependence structure of daily river flows?

Hydrological classification systems seek to provide information about the dominant processes in the catchment to enable information to be transferred between catchments. Currently, there is no widely agreed‐upon system for classifying river catchments. This paper develops a novel approach to classifying catchments based on the temporal dependence structure of daily mean river flow time series, applied to 116 near‐natural ‘benchmark’ catchments in the UK. The classification system is validated using 49 independent catchments. Temporal dependence in river flow data is driven by the flow pathways, connectivity and storage within the catchment and can thus be used to assess the influence catchment characteristics have on moderating the precipitation‐to‐flow relationship. Semi‐variograms were computed for the 116 benchmark catchments to provide a robust and efficient way of characterising temporal dependence. Cluster analysis was performed on the semi‐variograms, resulting in four distinct clusters. The influence of a wide range of catchment characteristics on the semi‐variogram shape was investigated, including: elevation, land cover, physiographic characteristics, soil type and geology. Geology, depth to gleyed layer in soils, slope of the catchment and the percentage of arable land were significantly different between the clusters. These characteristics drive the temporal dependence structure by influencing the rate at which water moves through the catchment and/or the storage in the catchment. Quadratic discriminant analysis was used to show that a model with five catchment characteristics is able to predict the temporal dependence structure for un‐gauged catchments. This method could form the basis for future regionalisation strategies, as a way of transferring information on the precipitation‐to‐flow relationship between gauged and un‐gauged catchments. © 2014 The Authors. Hydrological Processes by published by John Wiley & Sons, Ltd.     

河漫滩相沉积与风成沉积粒度判别函数的建立及在红土中应用

以长江南通段、婺江金华段的河漫滩沉积物和洛川、新疆昭苏的马兰黄土为训练样本,推导出河漫滩相沉积与风成沉积粒度的Fisher判别函数,并用启东河漫滩沉积物、南京下蜀黄土和伊犁马兰黄土对判别函数进行验证。第四纪红土的判别结果显示：新余、南平等地红土具有典型河漫滩沉积特征;长沙、金华、南昌、九江等地红土即具有风成又具有河漫滩沉积的特征,且风成沉积特征随着纬度减小及地形起伏增加而减小。结合现代降尘的粒度组成推测,中亚热带网纹红土的物质来源有近源也有远源。近源组分为来自附近河漫滩泛滥平原沉积物的风力二次搬运,在沉积后依旧保留了河漫滩沉积的大部分粒度特征;远源组分可能与黄土类似,由冬季风从北方物源区搬运而来。     

Discriminant analysis on land grading

This paper proposes the discriminant analysis on land grading after analyzing the common methods and discussing the Fisher‘s discriminant in detail. Actually this method deduces the dimension from multi to single, thus it makes the feature vectors in n-dimension change to a scalar, and use this scalar to classify samples. This paper illustrates the result by giving an example of the residential land grading by the discriminant analysis.     

加权距离判别法在泥石流危险度评价中的应用

将加权距离判别法引用到泥石流危险度分类中,建立了泥石流危险度分类模型。该模型选用了流域面积、主沟长度、流域最大相对高差、流域切割密度、主沟床弯曲系数、人口密度、泥沙补给长度比、植被覆盖率、一次泥石流（可能）最大冲出量和泥石流发生频率10项指标为建模参数,运用熵值法对这10个指标进行赋权,用已经分类的泥石流沟作为训练样本进行学习,建立了相应的判别准则。将待判泥石流沟样本代入判别准则进行判别分类,分类结果与传统分类结果100%吻合,验证了该模型的分类性能良好。该方法可以在实际工程中进行推广。     

砂土液化预测的Fisher判别模型及应用

基于Fisher判别理论建立了砂土液化可能性的Fisher判别分析（FDA）模型。在分析砂土液化影响因素的基础上,选取烈度、震中距、地下水位、砂层埋深、标贯击数、平均粒径、不均匀系数、剪应力比等8个实测特征指标作为FDA模型的预测指标。利用砂土液化的实测数据作为训练样本进行训练,建立FDA模型对砂土液化进行预测,并用其他未参加训练的实测数据进行了验证。研究结果表明,FDA模型简便可行、预测精度高,是解决砂土液化预测问题的有效方法之一。     

老挝北部黄色图章石的矿物成分及微量元素特征

老挝北部黄色图章石(又称老挝北部黄料)呈明亮的黄色至褐黄色,多见水波纹状内部纹理,与我国图章石中的田黄外观特征具有一定相似性。本文运用X射线粉晶衍射(XRD)、红外光谱(FTIR)、拉曼光谱(LRM)、扫描电子显微镜(SEM)、电子探针(EPMA)、激光剥蚀等离子质谱(LA-ICP-MS)、全铁化学分析等测试方法对其矿物组成、微量元素及产地特征等进行了研究。结果表明,老挝北部黄料的主要组成矿物单一,为无序地开石,杂质矿物包括重晶石、锐钛矿、硬水铝石和赤铁矿等,其中地开石颗粒表面平整,呈板状紧密排列;萝卜纹主要呈枝状、短脉状零星分布在基体中;老挝北部黄料的黄色由Fe~(3+)所致,且颜色深浅与Fe~(3+)含量呈正相关关系,其Cr、Rb、Sr含量低于田黄,Ga含量高于田黄。通过多元统计判别分析法可将老挝北部黄料和田黄进行有效区分,其判别函数对未知样品进行验证的正确率可达92. 9%,且模型的可靠性较高。通过稀土元素配分模式图可以直观区分老挝北部黄料和田黄。     

基于主成分分析法与Bayes判别法组合应用的火山岩岩性定量识别:以渤海海域中生界为例

渤海海域中生界火山岩岩石类型复杂多样,且同一岩性受岩石成分、结构差异的影响,因此岩石物理响应特征存在较大差异,为岩性识别增加了难度。本文通过对研究区大量取心资料、壁心资料以及薄片资料的岩电分析,优选出对岩性响应敏感的自然伽马(GR)、补偿中子(CNL)、密度(DEN)、声波时差(AC)以及原状地层电阻率(Rd)等5条曲线。基于主成分分析法,构建了F_1—F_5共5个综合变量,其中F_1和F_2方差贡献率占总贡献率的81.4%,可作为两个主成分有效地反映5个变量的信息。针对主成分信息,利用Bayes判别法,构建了不同岩性的定量解释模型,对研究区9种火山岩进行了岩性识别。对取心井段的回判结果显示,基于主成分分析与Bayes判别的联合识别方法较常规交会图法在岩石成分及结构的识别精度方面均有较大程度的提高。该方法的提出对研究区成像测井、元素测井资料缺少井段的火山岩岩性识别具有重要借鉴作用。