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Identification of Geochemical Anomaly by Multifractal Analysis

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【作者】 谢淑云成秋明柯贤忠鲍征宇王长明全浩理

【Author】 Xie Shuyun State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China; Faculty of Earth Sciences, China University of Geosciences, Wuhan 430074, China; Department of Earth and Space Science and Engineering, York University, Toronto ON M3J1P3, Canada Cheng Qiuming State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China; Department of Earth and Space Science and Engineering, York University, Toronto ON M3J1P3, Canada Ke Xianzhong , Bao Zhengyu State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China; Faculty of Earth Sciences, China University of Geosciences, Wuhan 430074, China Wang Changming State Key Laboratory of Geological Processes and Mineral Resources, Key Laboratory of Lithosphere Tectonics and Lithoprobing Technology of Ministry of Education, China University of Geosciences, Beijing 100083, China; Department of Earth and Space Science and Engineering, York University, Toronto ON M3J1P3, Canada Quan Haoli Faculty of Earth Sciences, China University of Geosciences, Wuhan 430074, China

【机构】 State Key Laboratory of Geological Processes and Mineral Resources, China University of GeosciencesFaculty of Earth Sciences, China University of GeosciencesDepartment of Earth and Space Science and Engineering, York UniversityState Key Laboratory of Geological Processes and Mineral Resources, Key Laboratory of Lithosphere Tectonics and Lithoprobing Technology of Ministry of Education, China University of Geosciences

【摘要】 The separation of anomalies from geochemical background is an important part of data analysis because lack of such identifications might have profound influence on or even distort the final analysis results. In this article, 1 672 geochemical analytical data of 11 elements, including Cu, Mo, Ag, Sn, and others, from a region within Tibet, South China, are used as one example. Together with the traditional anomaly recognition method of using the iterative mean ±2σ, local multifractality theory has been utilized to delineate the ranges of geochemical anomalies of the elements. To different degrees, on the basis of original data mapping, C-A fractal analysis and singularity exponents, Sn differs from the other 10 elements. Moreover, geochemical mapping results based on values of the multifractal asymmetry index for all elements delineate the highly anomalous area. Similar to other 10 elements, the anomalous areas of Sn delineated by the asymmetry index distribute along the main structure orientations. According to the asymmetry indexes, the 11 elements could be classified into 3 groups: (1)Ag and Au, (2) As-Sb-Cu-Pb-Zn-Mo, and (3) Sn-Bi-W. This paragenetic association of elements can be used to interpret possible origins of mineralization, which is in agreement with petrological analysis and field survey results.

【Abstract】 The separation of anomalies from geochemical background is an important part of data analysis because lack of such identifications might have profound influence on or even distort the final analysis results. In this article, 1 672 geochemical analytical data of 11 elements, including Cu, Mo, Ag, Sn, and others, from a region within Tibet, South China, are used as one example. Together with the traditional anomaly recognition method of using the iterative mean ±2σ, local multifractality theory has been utilized to delineate the ranges of geochemical anomalies of the elements. To different degrees, on the basis of original data mapping, C-A fractal analysis and singularity exponents, Sn differs from the other 10 elements. Moreover, geochemical mapping results based on values of the multifractal asymmetry index for all elements delineate the highly anomalous area. Similar to other 10 elements, the anomalous areas of Sn delineated by the asymmetry index distribute along the main structure orientations. According to the asymmetry indexes, the 11 elements could be classified into 3 groups: (1)Ag and Au, (2) As-Sb-Cu-Pb-Zn-Mo, and (3) Sn-Bi-W. This paragenetic association of elements can be used to interpret possible origins of mineralization, which is in agreement with petrological analysis and field survey results.

【基金】 jointly supported by the National Natural Science Foundation of China (Nos. 40525009, 40638041, 40502029, and 40373003)
  • 【文献出处】 Journal of China University of Geosciences ,中国地质大学学报(英文版) , 编辑部邮箱 ,2008年04期
  • 【分类号】P595
  • 【被引频次】17
  • 【下载频次】262
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