Classification of natural mineral and spring bottled waters of Portugal using Principal Component Analysis

Considering its area, Portugal is one of the world's richest countries in mineral and spring waters. There are 33 different types of bottled water, 18 of which are classified as natural mineral water and the remaining as spring water. The majority of these waters are of low mineralisation in comparison to most European bottled waters.     

主成分分析在地质样品分类与浓度预测中的应用研究

用主成分分析方法研究地质样品的Ｘ荧光光谱强度与浓度的关系,对未知样分类并预测样品浓度。对标准化后的数据计算各样品的主成分得分,根据得分分布图可快速分类样品。对训练样品作主成分回归分析,建立降维的主成分回归模型,用主元回归预测各组分浓度,效果好于多元回归分析方法。     

基于主成分分析和因子分析的宁夏水资源承载力研究

为了探求宁夏水资源承载力状况,利用主成分分析法和因子分析法,在分析宁夏2004~2014年水资源承载力状况的基础上,对宁夏5市2013年水资源承载力分水资源支持力、社会经济技术水平和社会生活水平3个方面进行综合考虑,得到宁夏5市的水资源利用状况指数和分级标准。具体得到以下几个结论:(1)2004~2014年间,宁夏水资源承载力仅在2005~2006年有所波动,其余年份两种方法结果一致,11年间保持稳定上升;(2)对于宁夏5市2013年水资源承载力状况,主成分分析结果由大到小排序为吴忠市、银川市、石嘴山市、固原市和中卫市,因子分析结果由大到小排序为吴忠市、银川市、固原市、石嘴山市和中卫市;(3)建立宁夏5市水资源承载力状况分级阈值,主成分分析下吴忠市和银川市的水资源利用状况高,石嘴山市的水资源利用状况中等,固原市和中卫市的水资源利用状况低;因子分析下吴忠市和银川市的水资源利用状况高,其余3市水资源利用状况都较低。     

Lithofacies Clustering Using Principal Component Analysis and Neural Network: Applications to Wireline Logs

Both statistical methods and artificial neural network (ANN) have been used for lithology or facies clustering. ANN, in particular, has increasingly gained popularity for clustering of categorical variables as well as for predictions of continuous variables. In this article, we discuss several counter examples that show deficiencies of these techniques when used for automatic lithofacies clustering. Our examples show that the lithofacies clustered by ANN alone or ANN in combination with principal component analysis (PCA), as commonly used, are highly inconsistent with the benchmark charts based on laboratory results. We propose several techniques to overcome these problems and improve the clustering of lithofacies, including (1) classification of lithofacies using the minor or intermediate principal component(s), (2) rotation of a principal component before using ANN for clustering, (3) cascading two or more PCAs and ANNs for clustering lithofacies or electrofacies, and (4) classifying lithofacies with demarcated stratigraphic reference classes.     

井控主成分分析技术在储层预测中的应用

南堡凹陷中浅层火成岩类型多、非均质性强,利用单一技术方法难以有效刻画火成岩和砂岩储层的空间分布,制约了中浅层砂岩油气藏的勘探开发。在岩石物理交会分析基础上,利用对岩性响应敏感的纵波阻抗、伽马、泊松比和甜点4类地震属性和PCA主组分降维技术,对玄武岩、蚀变火成岩、砂岩、泥岩四类岩性进行了有效的区分,利用创新的井控综合解释技术获得了最终岩性体数据,达到在复杂岩性背景下准确识别砂岩储层的目的。结果表明,地震预测与实钻情况吻合程度较高,达到87.1%,预测的火成岩与砂岩分布特征与区域地质认识相匹配,为研究区滚动开发和开发调整提供了有力技术支撑。     

Inversion of magnetic anomalies due to isolated thin dike-like sources using artificial neural networks

A new approach is proposed to interpret magnetic anomalies caused by isolated thin dike-like causative targets. The approach is essentially based on utilizing artificial neural network (ANN) inversion for estimating the problem parameters. Particularly, the modular neural network (MNN) is used for the inversion process in order to quantitatively interpret the magnetic anomalies. The MNN inversion has been first tested on a synthetic data with and without random white Gaussian noise. The effect of random noise has been clearly investigated where it showed that the approach provided satisfactory results. Furthermore, three field examples have been inverted in order to investigate the applicability of the proposed approach. The results showed good agreement with the techniques that have been stated in the literatures.     

基于主成分分析的干旱综合指标构建及其应用

探讨了干旱综合指标的构建,提出采用主成分分析法将降雨、径流及土壤含水量等水文气象要素融合为一个干旱综合指标,并将该指标应用于干旱过程识别和干旱频率分析,且以南盘江上游西桥水文站以上区域为例,将基于干旱综合指标计算的干旱重现期同基于降雨、径流和土壤含水量等单指标计算的干旱重现期进行对比分析。结果表明:采用主成分分析法构建的干旱综合指标是合理可行的。     

雅鲁藏布江流域水文分区研究

青藏高原水系众多,水文站点稀疏,观测资料匮乏,通过水文分区可以将相似流域归为一类,实现其有限测站水文资料向无资料区移用以及达到站网优化设置的目标。以雅鲁藏布江流域(以下简称雅江)为研究对象,利用气象资料和反映下垫面条件的土地利用和地形等物理因子,采用主成分分析、K-Means聚类分析等方法对雅江进行水文分区研究。研究中,根据气候和地形的垂直特征,将雅江划分为5个水文分区,分别为高寒荒漠区、高原温带河谷区、高原峡谷稀林区、高原冰川雨雪区和亚热带山地多雨区,并阐述了各分区的特征。最后,采用斯米尔诺夫检验的方法,选取具有长序列水文资料的站点进行检验,论证了分区结果的合理性。此项成果可为雅江无资料流域水文预测提供依据。     

Instability in Principal Component Analysis and the Quantification of Polyphenism in Palaeontological Data

The occurrence of cryptic polyphenism (variation in morphological properties within a single species) in ammonites is used to exemplify the application of the multivariate set of techniques known in analytical chemistry as cross-validation to quantify and isolate deviating specimens (ecomorphs) in a genetically homogeneous sample. A byproduct of the analysis bears on a method of identifying redundant variables. A species of Nigerian Turonian (Cretaceous) ammonites of the genus Thomasites is used in the exemplification.     

Kernel Principal Component Analysis for Efficient,Differentiable Parameterization of Multipoint Geostatistics

This paper describes a novel approach for creating an efficient, general, and differentiable parameterization of large-scale non-Gaussian, non-stationary random fields (represented by multipoint geostatistics) that is capable of reproducing complex geological structures such as channels. Such parameterizations are appropriate for use with gradient-based algorithms applied to, for example, history-matching or uncertainty propagation. It is known that the standard Karhunen–Loeve (K–L) expansion, also called linear principal component analysis or PCA, can be used as a differentiable parameterization of input random fields defining the geological model. The standard K–L model is, however, limited in two respects. It requires an eigen-decomposition of the covariance matrix of the random field, which is prohibitively expensive for large models. In addition, it preserves only the two-point statistics of a random field, which is insufficient for reproducing complex structures. In this work, kernel PCA is applied to address the limitations associated with the standard K–L expansion. Although widely used in machine learning applications, it does not appear to have found any application for geological model parameterization. With kernel PCA, an eigen-decomposition of a small matrix called the kernel matrix is performed instead of the full covariance matrix. The method is much more efficient than the standard K–L procedure. Through use of higher order polynomial kernels, which implicitly define a high-dimensionality feature space, kernel PCA further enables the preservation of high-order statistics of the random field, instead of just two-point statistics as in the K–L method. The kernel PCA eigen-decomposition proceeds using a set of realizations created by geostatistical simulation (honoring two-point or multipoint statistics) rather than the analytical covariance function. We demonstrate that kernel PCA is capable of generating differentiable parameterizations that reproduce the essential features of complex geological structures represented by multipoint geostatistics. The kernel PCA representation is then applied to history match a water flooding problem. This example demonstrates that kernel PCA can be used with gradient-based history matching to provide models that match production history while maintaining multipoint geostatistics consistent with the underlying training image.     

基于主成分分析的致密砂砾岩孔隙度测井评价方法

岩性识别是致密砂砾岩测井评价的重要工作。砂砾岩岩性多样、成分复杂,导致测井识别岩性准确率低、测井解释孔隙度不准确。以东营凹陷北部陡坡带沙四下亚段致密砂砾岩为例,在对其岩石学特征分析的基础上,按照岩石类型和骨架矿物差异给砂砾岩分类,利用铸体薄片资料对测井曲线进行岩性标定,提取各种岩性的测井响应特征,在此基础上建立了基于主成分分析的测井岩性识别方法,并分岩性建立了孔隙度测井评价模型,提高了砂砾岩测井岩性识别和测井孔隙度计算的准确率。     

基于层次聚类法和主成分分析法的铜陵市大气降尘污染元素来源解析研究

本文采用层次聚类法和主成分分析法对有色金属矿山城市-铜陵市的大气降尘中污染元素,主要是重金属元素的来源进行了识别,并分析了各来源所占的比例。结果显示,铜陵市大气降尘中污染物主要来源于冶金和采矿,其次为燃煤、交通和土壤扬尘等,其贡献率分别为冶金源+采矿源43.29%,燃煤源32.23%,交通源和土壤源10.53%,其他源13.94%。因此,优先控制冶金尘、采矿尘和燃煤尘,可以有效降低铜陵市大气降尘中污染元素的含量。     

稳健主成分分析在地震资料异常值噪声压制中的应用

提高地震数据的信噪比是地震资料处理的重要目标之一。传统的地震去噪方法虽然可以有效压制随机噪声,但对非高斯分布的异常值噪声压制效果欠佳。本研究展示了一种基于稳健主成分分析的地震数据异常值噪声压制方法。该方法在频率-空间域通过对地震数据实施稳健低秩近似来求取理想无噪声数据。在目标函数构建方面,采用核范数最小化模型求取理想的低秩近似数据,并使用l₁范数最小化模型来估计异常值噪声。此外,运用增广拉格朗日乘子法求解该反演问题。最后,模型数据和实际资料的去噪结果验证了本研究方法的有效性,与传统F-XY域预测滤波法去噪结果进行对比,也显示本研究方法在有效压制异常值噪声的同时能更好地保护有效波能量。     

主成分分析法在地下水质量评价中的应用

在水质评价应用中,采用主成分分析法(PCA),将多因子纳入同一系统进行定量化研究,基于MATLAB软件编程[1],对德阳市平原区地下水进行综合质量评价,并与模糊数学评价结果对比分析,结果表明,前6个主成分携带的信息达到85.73%,再分析这6个新的综合指标,确定研究区水质问题主要为总硬度及总溶解固体(TDS)超标,地下水质量最好为绵竹市水样点SY14,水质最差为旌阳区水样点SY58,该结果符合实际监测情况;选用主成分分析方法,结合模糊数学的计算而划分的地下水分级标准,能较好的体现区内地下水质量。     

基于特征导向主成分分析遥感蚀变异常提取方法

为利用多光谱遥感数据提取蚀变异常信息, 在分析蚀变矿物的先进星载热发射和反射辐射仪(advanced spaceborne thermal emission and reflection radiometer, ASTER)和影像短波近红外(visible and near IR-short wave-length IR, VNIR-SWIR)谱带的特征光谱曲线的基础上, 对传统的主成分分析法进行了改进, 利用特征导向主成分分析法对辽宁兴城地区进行矿物蚀变信息提取, 成功的对该地区内的褐铁矿(Fe3+)、绿泥石(Mg-OH基团矿物)和高岭石(Al-OH基团矿物)进行了蚀变异常信息提取.通过实践验证和研究区地质资料表明, 特征导向主成分分析法能够有效地提取蚀变信息并识别研究区内主要矿物, 可以为该区的成矿预测工作提供一定的依据.      

Textural identification of basaltic rock mass using image processing and neural network

A new approach to identify the texture based on image processing of thin sections of different basalt rock samples is proposed here. This methodology uses RGB or grayscale image of thin section of rock sample as an input and extracts 27 numerical parameters. A multilayer perceptron neural network takes as input these parameters and provides, as output, the estimated class of texture of rock. For this purpose, we have use 300 different thin sections and extract 27 parameters from each one to train the neural network, which identifies the texture of input image according to previously defined classification. To test the methodology, 90 images (30 in each section) from different thin sections of different areas are used. This methodology has shown 92.22% accuracy to automatically identify the textures of basaltic rock using digitized image of thin sections of 140 rock samples. Therefore, present technique is further promising in geosciences and can be used to identify the texture of rock fast and accurate.     

水足迹与主成分分析法耦合的新疆水资源承载能力评价

通过耦合水足迹理论与主成分分析方法,构建基于水足迹的新疆地区水资源承载力评价指标体系,分析研究区2010～2015年间水资源承载能力变化情况.结果表明:2010～2015年间新疆地区的水资源承载力呈先增大后减小的现象,2014年水资源承载力最差,供水压力最大.其中,研究区的承载力主要受水资源和社会因素以及进出口贸易中虚...     

基于主成分分析和BP神经网络的五道沟水面蒸发计算研究

针为探讨五道沟水面蒸发量与气象因子间的关系,准确估算该地区水面蒸发量,选取五道沟1991—2019年水面蒸发量以及气象因子实测资料,基于主成分法分析水面蒸发量的影响因素,并结合BP人工神经网络算法建立了水面蒸发计算模型.结果表明:主成分分析提取了三个主成分,第一主成分为地表温度、饱和差、绝对湿度、平均气温以及水汽压力差...     

数字图像技术在岩石细观力学研究中的应用

岩石内部的细观组成和结构决定了其在外力作用下的应力-应变状态,进而控制了其宏观力学响应和破坏机制。数字图像处理技术作为一种材料细观尺度上的空间结构精确测量和数字表述手段,已广泛应用于土、岩石及混凝土的细观结构定量分析中。应用数字图像处理进行的岩石细观力学研究是对岩石力学研究方法的革新。现阶段主要研究内容包括：岩石裂隙隙宽的非接触测量,数字表述岩石结构的非均匀性,进行岩石细观力学行为分析,将提取的岩石数字特征值与相应的岩石物性结合以实现岩石流-固耦合研究,建立基于数字图像技术的岩石细观力学数值模拟方法。在研究相关文献的基础上,对数字图像技术在岩石细观力学定量研究中的成果进行客观评述,探讨各种方法的优缺点,分析展望数字图像技术在岩石细观研究领域中的应用前景。     

基于主成分分析和贝叶斯正则化方法的神经网络年最大洪峰流量预测模型探讨

针时水文预测建模中输入因子过多而导致神经网络结构规模过大,泛化能力差的问题,利用主成分分析和贝叶斯正则化方法对神经网络进行改进,优化网络结构,从而提高泛化能力。以洮儿河流域镇西站年最大洪峰流量预测为例,研究结果表明,改进的神经网络预测方法与传统的神经网络方法相比,泛化能力有显著提高,而且网络的收敛也比较稳定,实际预测中效果良好。