Application of multivariate statistical procedures to the hydrochemical study of a coastal aquifer: an example from Crete,Greece

A variety of multivariate statistical procedures were applied to three separate sets of quantitative analytical data from a coastal aquifer located in Malia, Crete (Greece), in order to identify the major hydrochemical processes affecting the groundwater quality and to investigate the evolution of groundwater composition in three different sampling periods. Two of them were carried out on October 2001 and September 2002 at the end of the dry season and the third on April 2002 at the end of the wet period. Two factors were found that explained major hydrochemical processes in the aquifer. These factors reveal the existence of an intensive intrusion of seawater and mechanisms of nitrate contamination of groundwater. Bivariate plots of the scores of the two main factors showed that the seawater intrusion and nitrate pollution processes are maintained through three surveys and that the process of nitrate pollution increases from the first to the second dry survey. Q‐mode factor analysis and discriminant analysis of the three sampling periods clearly showed a seasonal variation of the whole chemistry of groundwater samples. This seasonal variation can be attributed to the freshwater recharge and seawater intrusion that affect the groundwater quality of the Malia aquifer. The results of trend surface analysis are in agreement with those of factor analysis. Moreover, the fourth‐order trend surfaces of EC, Cl^? and NO₃^? showed that the salinization process is more intensive during the first dry period and the spatial variation of NO₃^? maxima plumes are strongly affected by the flow regime of the Malia aquifer. Copyright © 2007 John Wiley & Sons, Ltd.     

结合光谱角的最大似然法遥感影像分类

遥感影像含有丰富的信息,反映了地物特征。其中光谱角侧重描述了光谱的形状特征,具有对多光谱图像增益不敏感的特点。最大似然法是遥感影像分类最常用的方法之一,文中对该方法的后验概率判别函数进行修改,将光谱角以概率因子的形式加入到判别函数中构造一种新的判别函数,有机地将光谱角这一特征信息加入影像分类。通过实验,并与最大似然法和光谱角匹配法分类结果进行比较,结果表明,结合光谱角的最大似然分类法的分类精度得到提高。     

成兰铁路绵竹—茂县段茂县群浅变质千枚岩风化指数研究

本文以成兰铁路茂县群千枚岩为研究对象, 通过电子探针、电子显微镜和岩石地球化学分析等实验方法对其地球化学特征、微观成分结构特征以及原岩特征进行分析研究, 同时对该区风化千枚岩样及其新鲜岩的化学成分进行分析对比, 使用SPSS.22软件对各主要元素进行相关性分析, 得出其风化过程中的控制性元素后进行判别方程式拟合, 同时与前人已有的风化指标进行对比, 得出新生成的判别式更适用于本区域的风化千枚岩类判识。     

The role of debris supply conditions in predicting debris flow activity

Debris flow frequency and magnitude were determined for 33 basins in southwest British Columbia. Basins were first classified as either weathering-limited or transport-limited using a discriminant function based on debris-contributing area, an area-weighted terrain stability number, and drainage density. Multiple regression was used to predict magnitude, peak discharge, frequency and activity (frequency times magnitude) within each group of basins. Model performance was improved by stratifying the total sample of debris flow basins into weathering-and transport-limited groups. Explained variance increased by an average of 15 per cent in the transport-limited sample, indicating that sediment supply conditions in the more active basins are fundamental in predicting debris flow activity. An independent test of the regression models with 11 basins yielded generally good results for debris flow magnitude and peak discharge. Prediction of debris flow frequency proved problematical in weathering-limited basins. The methods developed here provide estimates of debris flow attributes in basins for which few data on past events are available. Copyright © 1999 John Wiley & Sons, Ltd.     

岩石大地构造学说的兴起、没落与新生

在板块构造环境中形成的岩石组合(rock assemblages)被称为岩石大地构造组合(petrotectonic assemblages)。岩石大地构造学说创立于20世纪70~80年代,经历了兴起、没落与新生的曲折过程。研究表明,由Pearce等学者创立的岩石大地构造学说的理论基本正确,概括来说,洋中脊、洋岛和岛弧三大构造背景的源区是不同的,这是玄武岩判别图的理论基础。目前的情况是,三大构造背景源区不均一性可能比早先认为的更复杂。由于不同构造环境源区的复杂性,早先的玄武岩判别图采用的是抽样数据、典型地区、精确数据,显然不适合全球数据海量积累的情况,遂使玄武岩判别理论遇到了瓶颈。我们的研究发现,不同构造背景的所有岩石、矿物(包括玄武岩、苦橄岩、辉长岩、堆晶岩、橄榄石、单斜辉石、尖晶石等)几乎都保留了不同构造背景的“基因”信息,并且大多可采用大数据方法予以识别,遂使岩石大地构造学说焕发了青春。虽然本文提出了不同构造环境存在不同“基因”的假说,按照大数据方法判别的效果显著增加了。但是,这个不同构造背景的“基因”究竟是什么仍然不清楚。此外,上述“基因”假说的理论解释还非常不足,需要今后进一步探讨。     

逐步判别分析法在筛选水质评价因子中的应用

应用逐步判别分析法对水质进行评价因子筛选,通过对水质实际监测因子的假设检验分析,引入判别能力好的因子,建立判别方程。对实例评价结果表明：通过筛选引入判别方程变量的后验概率均达到90%以上,对判别分类有显著影响,从而提高了所建立的判别函数的稳定性和评价结果的可靠性。进行因子筛选的评价结果显示石头口门水库2001-2004年为Ⅱ类水,2005年为Ⅲ类水,污染呈现出逐年加重之势,与实际情况相符;而未进行因子筛选的评价结果显示5年水质没有变化,均为Ⅱ类水,且2005年后验概率仅达到52％,结果判误率高。     

岩爆等级判定的距离判别分析方法及应用

将距离判别分析方法应用于岩爆等级判定问题。选用洞室围岩最大的切向应力σθ、岩石单轴抗压强度σc、抗拉强度σt、岩石弹性能量指数Wet作为岩爆等级判定的距离判别分析模型判别因子,以工程中实际岩爆情况及数据作为训练样本,进行分析计算,建立岩爆等级判定的距离判别分析模型。运用该分析模型对国内外工程实际岩爆情况进行判定,判别结果与工程实际完全相符。将该模型应用到诸（暨）永（嘉）高速公路括苍山隧道工程的岩爆情况预测中,判别结果与实际情况相符。研究表明,岩爆等级判定的距离判别分析方法,判别能力强,误判率低,是解决岩爆等级判定的一条有效途径。     

中国5 个海区不同群体毛蚶形态差异分析

运用多变量形态度量学分析方法,采用10个形态性状,对中国5个海区5个野生毛蚶群体间的形态差异进行了比较研究.聚类分析和主成分分析结果表明:天津塘沽群体和山东青岛群体形态最为接近,广西北海群体的趋异程度最大.主成分分析建立了3个主成分--主成分1、主成分2和主成分3,其贡献率分别为:34.70%、19.80%、15.00%,积累贡献率为69.50%. 判别分析结果显示,5个群体间的形态差异显著(P<0.01).建立了5个群体毛蚶的判别函数,其判别准确率P1为45.45%～95.45%、P2为36.36%～95.45%,综合判别率为74.50%.     

基于主成分分析方法的海量地震数据属性降维优化

针对传统的地震数据属性降维优化方法所选取的地震数据属性特征贡献率低导致降维过程计算量大、CPU占用率高等问题,提出一种基于主成分分析的海量地震数据属性降维优化方法。首先根据地震样本特征建立地震数据特征矩阵,把矩阵中的特征进行聚类,运用降序法排列聚类结果,选取前几项数据作为地震数据属性特征选取结果,对其结果评估分类信息量;通过特征积分准则(FSC)修正分类信息量,获取海量地震数据属性特征节点;运用主成分分析方法对地震数据属性特征节点主成分添加标签,确定Fisher判别分析与PCA可变动选择不确定关系,建立半监督降维的全局最优化形式,运用特征值分解计算降维结果,克服海量地震数据属性降维过程中的过拟合问题,融合主成分分析算法与Fisher判别分析算法实现海量地震数据属性降维优化。实验结果证明,所提方法选取的属性特征精度及贡献率较高,降维过程中CPU占用率较低。