首页> 外文期刊>Journal of Geochemical Exploration: Journal of the Association of Exploration Geochemists >Evaluation of elemental mineralization rank using fractal and multivariate techniques and improving the performance by log-ratio transformation
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Evaluation of elemental mineralization rank using fractal and multivariate techniques and improving the performance by log-ratio transformation

机译:使用分形和多变量技术评估元素矿化级别,并通过降低比率转换提高性能

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Background separation from anomalous populations in the polymetallic mineralization is a problematic issue in geochemical exploration. This study focuses on the applications of fractal modeling, correspondence analysis (CA), classical principal component analysis (PCA), and PCA method based on centred log-ratio (clr) transformation for the multi-elemental evaluation of geochemical data in Glojeh polymetallic mineralization, NW Iran. A Concentration-Number (C-N) multifractal modeling was applied to delineate individual self-similar patterns for elemental distributions simultaneously in terms of Au, Ag, Cu, Pb, and Zn. The differences between fractal dimensions and clockwise angle between fitted lines in the C-N Log-log plots could lead to evaluate mineralized rank corresponding to Au = Ag = Pb Cu approximate to Zn. A more comprehensive evaluation via CA and PCA of clr-transformed data could facilitate the vein structure identification in high-dimensional data. The PCA results of clr-transformed data revealed the potential intensification trend of (Pb, Ag, As, Te, Au) (Mo, Zn, Be, Cu) W (S, Cd), that is more consistent with fractal and CA approaches. The closure effect problem was overcome by the clr transformation, and the total variance explained by the first factor increased from 28.2% in classical PCA to 43.7% in clr transformation. Accordingly, As, Sb and Cd were considered as potential pathfinder elements for Au in Glojeh deposit. The ability to handle zeros in the data matrix and determining an elemental eccentricity as a criterion are the advantages of CA method, while loading factors spread in a full circle and providing subcompositional coherence are the competitive advantages of PCA (calculated according to log-ratio transformation). However, the CA and PCA based on Log-Ratio transformation techniques showed significant potential to draw an inference in such deposits.
机译:从多种矿化中的异常群体中的背景分离是地球化学勘探中的问题问题。本研究重点介绍了基于集中记录比(CLR)转换的分形模型,对应分析(CA),经典主成分分析(PCA)和PCA方法的应用,以进行Glojeh多种矿化中的地球化学数据的多元素评估,NW伊朗。浓度 - 数(C-N)多重分族建模用于在Au,Ag,Cu,Pb和Zn方面同时描绘各个自相似模式。 C-N对数图中配合线之间的分形尺寸和顺时针角度之间的差异可能导致对应于Au&gt的矿化等级。= Ag& = Pb& Cu近似到Zn。通过CA和CLR转换数据的PCA更全面的评估可以促进高维数据中的静脉结构识别。 CLR转换数据的PCA结果揭示了(PB,AG,AS,TE,AU)&GT的潜在强化趋势。 (Mo,Zn,Cu)& W> (S,CD),与分形和CA接近更一致。 CLR转化克服了闭合效应问题,第一因子解释的总方差从CLR转化中的经典PCA中的28.2%增加到43.7%。因此,如,Sb和Cd被认为是Glojeh沉积物中Au的潜在探测结果。在数据矩阵中处理​​零并确定元素偏心率作为标准的能力是Ca方法的优点,而在全圈中传播并提供子组件相干性的装载因子是PCA的竞争优势(根据逻辑比率转换计算) )。然而,基于记录比变换技术的CA和PCA显示出在这种沉积物中引起推理的显着潜力。

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