形态相关系数及其在地球化学数据分析中的应用（英文）
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摘要
地球化学数据元素间的相关分析具有重要意义。地球化学数据是一种不遵循正态分布的成分数据,其封闭特征存在挖掘和分析的困难和障碍,因此许多传统的统计方法不适合使用。主要通过元素值的趋势分析了地球化学元素之间的相关关系,并提出了形态相关系数的概念。该方法不需要数据服从正态分布,可以忽略闭包特征的影响。实验表明,该方法简单、稳定、准确,可以显示数据元素之间的关系。此外,方法计算过程消除了回溯,因此适用于大数据的实时和动态分析。
Correlation analyses are important between geochemical data elements. Geochemical data is a type of compositional data which does not obey the normal distribution and its closure feature present mining and analysis difficulties and obstacles; therefore, many traditional statistical methods are not suitable for use. This paper mainly analyzed the correlation relationship between the geochemical elements through the elemental values trend and proposed the Morphological Correlation Coefficient concept. This method didn't need the data obey the normal distribution and can ignore the influence of closure feature. Experiments show that the method is simple, stable and accurate to show the relation between data elements. In addition, the method calculation process eliminates backtracking, so it is appropriate in real-time and dynamic analyses of large data.
Correlation analyses are important between geochemical data elements. Geochemical data is a type of compositional data which does not obey the normal distribution and its closure feature present mining and analysis difficulties and obstacles; therefore, many traditional statistical methods are not suitable for use. This paper mainly analyzed the correlation relationship between the geochemical elements through the elemental values trend and proposed the Morphological Correlation Coefficient concept. This method didn't need the data obey the normal distribution and can ignore the influence of closure feature. Experiments show that the method is simple, stable and accurate to show the relation between data elements. In addition, the method calculation process eliminates backtracking, so it is appropriate in real-time and dynamic analyses of large data.
引文
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Buccianti A, 2013. Is compositional data analysis a way to see beyond the illusion? [J]. Computers & Geosciences, 50: 165-173.
Egozcue J J, Pawlowsky-Glahn V, Mateu-Figueras G,et al., 2003. Isometric logratio transformations for compositional data analysis [J]. Mathematical Geology, 35(3): 279-300.
Edjabou M E, Martin-Fernandez J A, Scheutz C, et al., 2017. Statistical analysis of solid waste composition data: Arithmetic mean, standard deviation and correlation coefficients [J]. Waste Management, 69: 13-23.
Fieller E C, Hartley H O, Pearson E S, 1957. Test for rank correlation coefficients. I [J]. Biometrika, 44(3/4): 470-481.
Filzmoser P, Hron K, 2009. Correlation analysis for compositional data [J]. Mathematical Geosciences, 41(8): 905-919.
Pearson K, 1897. Mathematical contributions to the theory of evolution: On a form of spurious correlation which may arise when indices are used in the measurement of organs [J]. Proceedings of the Royal Society of London, 60: 489-498.
Pawlowsky-Glahn V, Egozcue J J, Lovell D, 2015. Tools for compositional data with a total [J]. Statistical Modelling, 15(2): 175-190.
Reimann C, Filzmoser P, 2000. Normal and lognormal data distribution in geochemistry: death of a myth. Consequences for the statistical treatment of geochemical and environmental data [J]. Environmental Geology, 39(9): 1001-1014.
Wang Haicheng, Cheng Qiuming, Zuo Renguang, 2015. Spatial characteristics of geochemical patterns related to Fe mineralization in the southwestern Fujian province (China) [J]. Journal of Geochemical Exploration, 148: 259-269.
Wang Kun, Xiao Keyan, Li Nan, et al., 2017. Application of partial least squares regression for identifying multivariate geochemical anomalies in stream sediment data from Northwestern Hunan, China [J]. Geochemistry: Exploration, Environment, Analysis, 17(3): 217-230.
Zhou Di, 1998. Geological compositional data analysis: difficulties and solutions [J]. Earth Science, 23(2): 147-152.
Buccianti A, 2013. Is compositional data analysis a way to see beyond the illusion? [J]. Computers & Geosciences, 50: 165-173.
Egozcue J J, Pawlowsky-Glahn V, Mateu-Figueras G,et al., 2003. Isometric logratio transformations for compositional data analysis [J]. Mathematical Geology, 35(3): 279-300.
Edjabou M E, Martin-Fernandez J A, Scheutz C, et al., 2017. Statistical analysis of solid waste composition data: Arithmetic mean, standard deviation and correlation coefficients [J]. Waste Management, 69: 13-23.
Fieller E C, Hartley H O, Pearson E S, 1957. Test for rank correlation coefficients. I [J]. Biometrika, 44(3/4): 470-481.
Filzmoser P, Hron K, 2009. Correlation analysis for compositional data [J]. Mathematical Geosciences, 41(8): 905-919.
Pearson K, 1897. Mathematical contributions to the theory of evolution: On a form of spurious correlation which may arise when indices are used in the measurement of organs [J]. Proceedings of the Royal Society of London, 60: 489-498.
Pawlowsky-Glahn V, Egozcue J J, Lovell D, 2015. Tools for compositional data with a total [J]. Statistical Modelling, 15(2): 175-190.
Reimann C, Filzmoser P, 2000. Normal and lognormal data distribution in geochemistry: death of a myth. Consequences for the statistical treatment of geochemical and environmental data [J]. Environmental Geology, 39(9): 1001-1014.
Wang Haicheng, Cheng Qiuming, Zuo Renguang, 2015. Spatial characteristics of geochemical patterns related to Fe mineralization in the southwestern Fujian province (China) [J]. Journal of Geochemical Exploration, 148: 259-269.
Wang Kun, Xiao Keyan, Li Nan, et al., 2017. Application of partial least squares regression for identifying multivariate geochemical anomalies in stream sediment data from Northwestern Hunan, China [J]. Geochemistry: Exploration, Environment, Analysis, 17(3): 217-230.
Zhou Di, 1998. Geological compositional data analysis: difficulties and solutions [J]. Earth Science, 23(2): 147-152.