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Application of fractal-wavelet analysis for separation of geochemical anomalies

机译:分形小波分析在地球化学异常分离中的应用

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The purpose of this paper is separation and detection of different geochemical populations and anomalies from background utilizing fractal-wavelet analysis. Daubechies2 and Morlet wavelets were used for transformation of the Cu estimated data to spatial frequency based on lithogeochemical data in Bardaskan area (SE Iran) by a MATLAB code. Wavelet is a significant tool for transformation of exploratory data because the noise data are removed from results and also, accuracy for determination of thresholds can be higher than other conventional methods. The Cu threshold values for extremely, highly and moderately anomalies are 1.4%, 0.66% and 0.4%, respectively, according to the fractal-wavelet analysis based on the Daubichies2 transformation. Moreover, the fractal-wavelet analysis by the Morlet wavelet shows that the Cu threshold values are 2%, 0.75% and 0.46% for extremely, highly and moderately anomalies and populations, respectively. The results obtained by the both WT methods indicate that the main Cu enriched anomalies and populations were situated in the central parts of the Bardaskan district which are associated with surface mineralization and ancient mining digs. Furthermore, results derived via the Morlet WT is better than Daubichies2 WT according to the correlation with geological characteristics by logratio matrix. The results obtained by the fractal-wavelet method have a good correlation with geological particulars including alteration zones and surface Cu mineralization which reveals the proposed technique is an applicable approach for identification of various geochemical anomalies and zones from background. However, the main targets for detailed exploration is located in the central part of the studied area. (C) 2016 Published by Elsevier Ltd.
机译:本文的目的是利用分形小波分析法从背景中分离和检测不同的地球化学种群和异常。使用Daubechies2和Morlet小波通过MATLAB代码,基于Bardaskan地区(东南伊朗)的岩化化学数据,将Cu估计数据转换为空间频率。小波是用于探索性数据转换的重要工具,因为从结果中删除了噪声数据,并且阈值确定的准确性可能比其他常规方法更高。根据基于Daubichies2变换的分形小波分析,极端,高度和中等异常的Cu阈值分别为1.4%,0.66%和0.4%。此外,通过Morlet小波进行的分形小波分析表明,对于极度,高度和中度异常和总体,Cu阈值分别为2%,0.75%和0.46%。两种WT方法获得的结果均表明,富Cu的主要异常和种群位于Bardaskan区的中部,与地表矿化和古代采矿有关。此外,根据对数矩阵与地质特征的相关性,通过Morlet WT得出的结果优于Daubichies2 WT。分形小波方法获得的结果与地质特征(包括蚀变带和表层铜矿化)具有良好的相关性,这表明所提出的技术是从背景识别各种地球化学异常和区域的适用方法。但是,详细勘探的主要目标位于研究区域的中部。 (C)2016由Elsevier Ltd.出版

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