Otolith shape and temporal stability of spawning groups of Icelandic cod (Gadus morhua L.)     

Jonsdottir  Ingibjorg G.; Campana  Steven E.; Marteinsdottir  Gudrun 《ICES Journal of Marine Science》2006,63(8):1501-1512

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距离判别法在公路隧道围岩分类中的应用   总被引：4，自引：1，他引：3  

王吉亮  陈剑平  杨静 《吉林大学学报(地球科学版)》2008,38(6)

将距离判别分析方法应用于公路隧道围岩类别判定问题中,结合前人研究经验,选用隧道洞室围岩岩石单轴饱和抗压强度σc、节理平均间距d、岩石质量指标RQD、地下水情况作为围岩类别判定的距离判别分析模型判别因子,以前人实际工程中公路隧道围岩实测数据资料作为训练样本进行分析计算,建立围岩类别判定的距离判别分析模型。采用回判估计方法对判别结果进行验证,误判率为零,将对样本数据的回判结果与前人应用神经网络方法判别结果对比,判别结果一致。运用距离判别分析模型对浙江省诸永高速公路枫树岭隧道围岩类别情况进行判别,将所选洞段围岩情况判定为Ⅳ类,与规范方法判定结果一致。  相似文献   

空间点所属空间体的距离判别法分析     

相诗尧  邢会敏  徐东晶 《测绘科学》2016,41(6):40-43,112

针对空间直角坐标系下空间点所属空间体的判定问题,该文提出一种采用距离判别法进行分析研究的方法。基于空间点的特点以及距离判别法的基本原理,通过建立一种以空间点的x、y、z坐标为判别因子,以马氏距离为判别距离的空间点所属性距离判别分析模型来完成对该问题的判定,并采用训练样本回代法和总体均值向量假设检验来验正该模型。通过40个训练样本的回代结果和两总体均值向量假设检验结果,证明了该方法的合理性和有效性。运用该模型,对4个待判样本进行判别,判别结果与真实情况一致,同时也证明了该方法的可行性。  相似文献   

含油气盆地深部高过成熟烃源岩古TOC恢复方法及其应用          下载免费PDF全文

庞雄奇  李倩文  陈践发  黎茂稳  庞宏 《古地理学报》2014,16(6):769-789

根据烃源岩演化过程中残留生烃母质变化原理,建立了TOC恢复的数学地质模型,提出了TOC演化与各种主控因素之间的定量关系模式,通过对模式中各主控因素在自然条件下变化的特征取值,计算得到了TOC恢复系数,做出了泥质岩烃源岩和碳酸盐岩烃源岩在含不同类型干酪根情况下的TOC演化图版。这一方法的最大特色是避免了人为主观因素和高温压物理模拟实验条件可能带来的结果偏差。研究表明:随着热演化程度的增加,烃源岩内残留的TOC含量逐渐减少,在大量生排烃阶段(R_O=0.5％～2.0%)变化幅度最大;处于高过成熟阶段(R_O>1.2%)的烃源岩,其原始TOC恢复系数随R_O增大而逐渐增加,含Ⅰ型、Ⅱ型、Ⅲ型母质的泥质岩烃源岩TOC恢复系数分别可达3.0、2.0、1.4;含Ⅰ型、Ⅱ型、Ⅲ型母质的碳酸盐岩烃源岩TOC恢复系数分别可达3.2、2.2、1.5。论文例举了源岩TOC恢复在实际工程中的应用。  相似文献   

判别分析在金矿核物探异常定性中的应用     

唐桢  周四春  李娇龙  魏威  万志雄 《物探与化探》2011,35(4):488-492

以马脑壳金矿为例,运用判别分析方法对核物探探测异常进行定性判别研究。通过SPSS软件运用费歇判别和逐步判别两种分析方法,定性非矿异常准确率分别达到83.3%和66.7%,定性有矿异常准确率分别达到89.7%和91.4%。  相似文献   

Discriminant models for uncertainty characterization in area class change categorization     

Jingxiong Zhang  Jiong You 《地球空间信息科学学报》2011,14(4):255-261

Discriminant space defining area classes is an important conceptual construct for uncertainty characterization in area-class maps.Discriminant models were promoted as they can enhance consistency in area-class mapping and replicability in error modeling.As area classes are rarely completely separable in empirically realized discriminant space,where class inseparabil-ity becomes more complicated for change categorization,we seek to quantify uncertainty in area classes(and change classes)due to measurement errors and semantic discrepancy separately and hence assess their relative margins objectively.Experiments using real datasets were carried out,and a Bayesian method was used to obtain change maps.We found that there are large differences be-tween uncertainty statistics referring to data classes and information classes.Therefore,uncertainty characterization in change categorization should be based on discriminant modeling of measurement errors and semantic mismatch analysis,enabling quanti-fication of uncertainty due to partially random measurement errors,and systematic categorical discrepancies,respectively.  相似文献   

采动影响下矿井突水水源Fisher判别与地下水补给关系反演          下载免费PDF全文

刘鑫  陈陆望  林曼利  李思达 《水文地质工程地质》2013,40(4):36-43

为了揭示采动影响下矿井突水水源误判原因,进而推测地下水补给关系,收集了朱仙庄煤矿历年水化学资料,建立了突水水源的Fisher判别模型,并根据水样误判结果以及矿井地质与水文地质条件,反演分析了地下水补给关系.研究结果表明朱仙庄煤矿煤系砂岩裂隙含水层全部水样回判正确,含水层较为封闭,与其他含水层无水力联系;新生界第四含水层、侏罗系砾岩含水层与石炭系或奥陶系灰岩含水层部分水样误判,矿井采动影响下不同时期含水层间的地下水存在不同程度的水力联系,矿井新生界第四含水层水源对侏罗系砾岩含水层存在补给,石炭系或奥陶系灰岩水则通过矿井南部露水补给新生界第四含水层.地下水动力条件的变化验证了地下水补给关系.建立突水水源判别模型,分析回代误判原因,反演地下水补给关系,为正确认识矿井采动影响下地下水循环条件提出了新的研究手段.  相似文献   

主成分判别分析分类方法在地理国情普查样本解译中的应用     

张伟  窦春和  侯加林 《东北测绘》2014,(6):49-50

提出了一种主成分判别分析分类方法。通过在地理国情普查样本解译中的应用实验证明,主成分判别分析分类方法能够利用少量的样本实现更优的分类精度。  相似文献   

U.K.-U.S. WORKSHOP ON INTERFACING MICRO- AND MACRO-MODELS OF SPATIAL BEHAVIOR     

《The Professional geographer》2013,65(4):486-487

Five semiarid Montana ghost towns abandoned for more than 45 years were studied to understand better the nature of soil and vegetation recovery following severe human impacts. Discriminant analysis was used to interpret and classify variation among land-use intensity groups. Recovery at the five towns was strongly linked to the degree of the initial soil disturbance, vegetation type, and precipitation. Recovery of the vegetation to ambient conditions was far from complete in all but one town.  相似文献   

Using geovisual analytics to compare the performance of geographically weighted discriminant analysis versus its global counterpart,linear discriminant analysis     

Peter Foley 《International journal of geographical information science》2013,27(4):633-661

Geographically weighted spatial statistical methods are a family of spatial statistical methods developed to address the presence of non-stationarity in geographical processes, the so-called spatial heterogeneity. While these methods have recently become popular for analysis of spatial data, one of their characteristics is that they produce outputs that in themselves form complex multi-dimensional spatial data sets. Interpretation of these outputs is therefore not easy, but is of high importance, since spatial and non-spatial patterns in the results of these methods contain clues to causes of underlying non-stationarity. In this article, we focus on one of the geographically weighted methods, the geographically weighted discriminant analysis (GWDA), which is a method for prediction and analysis of categorical spatial data. It is an extension of linear discriminant analysis (LDA) that allows the relationship between the predictor variables and the categories to vary spatially. This produces a very complex data set of GWDA results, which include on top of the already complex discriminant analysis outputs (e.g. classifications and posterior probabilities) also spatially varying outputs (e.g. classification function parameters). In this article, we suggest using geovisual analytics to visualise results from LDA and GWDA to facilitate comparison between the global and local method results. For this, we develop a bespoke visual methodology that allows us to examine the performance of global and local classification method in terms of quality of classification. Furthermore, we are also interested in identifying the presence (or absence) of non-stationarity through comparison of the outputs of both methods. We do this in two ways. First, we visually explore spatial autocorrelation in both LDA and GWDA misclassifications. Second, we focus on relationships between the classification result and the independent variables and how they vary over space. We describe our visual analytic system for exploration of LDA and GWDA outputs and demonstrate our approach on a case study using a data set linking election results with a selection of socio-economic variables.  相似文献