A comparison of PCA and ICA in geochemical pattern recognition of soil data: The case of Cyprus

IF 3.4 2区 地球科学 Q1 GEOCHEMISTRY & GEOPHYSICS
Shahed Shahrestani , David R. Cohen , Ahmad Reza Mokhtari
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Abstract

Multivariate analysis of soil geochemistry is a powerful tool for differentiating lithological units and detecting geochemical dispersion halos related to mineralization or contamination. While univariate analysis can effectively identify lithological units with pronounced variations, it may fail to differentiate between subtler variations in lithologies. Traditional multivariate techniques such as principal component analysis (PCA) have limitations, including difficulties in understanding the individual contributions of each variable and an inability to work with non-Gaussian data. Independent component analysis (ICA) has emerged as a potential alternative, as it can effectively identify independent components of non-Gaussian data. In this study, we compared the effectiveness of PCA and ICA in relating multivariate soil geochemistry to parent lithology using the Soil Geochemical Atlas of Cyprus and associated digital geological maps. Both PCA and ICA were able to differentiate between the ultramafic units within the Troodos Ophiolite (TO) and the Circum-Troodos Sedimentary Succession (CTSS). However, ICA was more effective than PCA in identifying pillow lavas, providing a clear separation in the scores for IC4 and IC5. Furthermore, both PCA and ICA were able to separate the sheeted dykes from the cumulate mafic units within the TO. The gabbro unit is closely defined by IC2 scores. In contrast, PCA failed to provide factors that effectively delineated the Mamonia Terrane from other units, especially the TO, while ICA was able to provide a distinct separation in IC4 and IC5 scores. Separation between the CTSS and Quaternary units was weakly observed in IC2 scores. These findings demonstrate that there is a difference in the effectiveness of PCA and ICA in identifying different lithological units and emphasize the need for a careful selection of multivariate methods to differentiate between subtle differences in soil geochemistry relating to variations in parent lithology.

比较 PCA 和 ICA 在土壤数据地球化学模式识别中的应用:塞浦路斯案例
土壤地球化学多变量分析是区分岩性单元和检测与矿化或污染有关的地球化学弥散晕的有力工具。虽然单变量分析可以有效识别变化明显的岩性单元,但可能无法区分岩性中更细微的变化。传统的多变量技术,如主成分分析(PCA)有其局限性,包括难以理解每个变量的单独贡献,以及无法处理非高斯数据。独立分量分析(ICA)可以有效识别非高斯数据的独立分量,因此成为一种潜在的替代方法。在这项研究中,我们利用《塞浦路斯土壤地球化学图集》和相关的数字地质图,比较了 PCA 和 ICA 在将多元土壤地球化学与母岩学联系起来方面的有效性。PCA 和 ICA 都能区分特罗多斯蛇绿岩 (TO) 和环特罗多斯沉积演替 (CTSS) 中的超基性岩单元。不过,在识别枕状熔岩方面,ICA 比 PCA 更有效,IC4 和 IC5 的得分有明显的区分。此外,PCA 和 ICA 都能够将片状岩堤与 TO 内的累积岩浆岩单元区分开来。辉长岩单元由 IC2 分数紧密界定。相比之下,PCA 未能提供有效划分 Mamonia Terrane 与其他单元(尤其是 TO)的因子,而 ICA 则能够通过 IC4 和 IC5 分数提供明显的分离。在 IC2 分数中,CTSS 和第四纪单元之间的分离较弱。这些研究结果表明,PCA 和 ICA 在识别不同岩性单元的有效性方面存在差异,并强调有必要谨慎选择多元方法,以区分与母岩变化有关的土壤地球化学的细微差别。
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来源期刊
Journal of Geochemical Exploration
Journal of Geochemical Exploration 地学-地球化学与地球物理
CiteScore
7.40
自引率
7.70%
发文量
148
审稿时长
8.1 months
期刊介绍: Journal of Geochemical Exploration is mostly dedicated to publication of original studies in exploration and environmental geochemistry and related topics. Contributions considered of prevalent interest for the journal include researches based on the application of innovative methods to: define the genesis and the evolution of mineral deposits including transfer of elements in large-scale mineralized areas. analyze complex systems at the boundaries between bio-geochemistry, metal transport and mineral accumulation. evaluate effects of historical mining activities on the surface environment. trace pollutant sources and define their fate and transport models in the near-surface and surface environments involving solid, fluid and aerial matrices. assess and quantify natural and technogenic radioactivity in the environment. determine geochemical anomalies and set baseline reference values using compositional data analysis, multivariate statistics and geo-spatial analysis. assess the impacts of anthropogenic contamination on ecosystems and human health at local and regional scale to prioritize and classify risks through deterministic and stochastic approaches. Papers dedicated to the presentation of newly developed methods in analytical geochemistry to be applied in the field or in laboratory are also within the topics of interest for the journal.
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