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首页> 外文期刊>Stochastic environmental research and risk assessment >Application of singular value decomposition (SVD) and semi-discrete decomposition (SDD) techniques in clustering of geochemical data: an environmental study in central Iran
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Application of singular value decomposition (SVD) and semi-discrete decomposition (SDD) techniques in clustering of geochemical data: an environmental study in central Iran

机译：奇异值分解（SVD）和半离散分解（SDD）技术在地球化学数据聚类中的应用：伊朗中部的一项环境研究

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摘要

Common multivariate clustering techniques are ineffective in identifying subtle patterns of correlation, and clustering of variables or samples within complex geochemical datasets. This study compares the combination of singular value decomposition (SVD) and semi discrete decomposition (SDD), with that of hierarchical cluster analysis (HCA), to examine patterns within a multielement soil geochemical dataset from an agricultural area in the vicinity of Pb-Zn mining operations in central Iran. SVD was used to both identify patterns of correlation between variables and samples and to "denoise" the data, and SDD to simultaneously cluster the samples and variables. The results reveal various spatial associations of mining waste-associated metals As, Ba, Pb and Zn, and within the remaining elements whose distribution is largely controlled by the major oxides. SVD-SDD was found to be superior to HCA, in its ability to detect subtle clusters in soil geochemistry indicative of mine-related contamination in the study area.
机译：常见的多变量聚类技术无法有效地识别相关的细微模式，也无法在复杂的地球化学数据集中聚类变量或样本。本研究比较了奇异值分解（SVD）和半离散分解（SDD）与层次聚类分析（HCA）的组合，以检查Pb-Zn附近农业区的多元素土壤地球化学数据集中的模式伊朗中部的采矿业务。 SVD用于识别变量和样本之间的相关模式并“消噪”数据，而SDD用于同时对样本和变量进行聚类。结果揭示了采矿相关废物金属As，Ba，Pb和Zn的各种空间关联，以及在其余元素中其分布主要受主要氧化物控制的各种空间关联。 SVD-SDD被发现优于HCA，因为它能够检测土壤地球化学中的细微簇，从而表明研究区域内与矿山有关的污染。

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