Multivariate statistical “unmixing” of Indian and Pacific Ocean sediment provenance

IF 2.1 3区 地球科学 Q2 LIMNOLOGY
Ann G. Dunlea, Kazutaka Yasukawa, Erika Tanaka, Ingrid L. Hendy
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Abstract

The geochemistry of marine sediment is a massive archive of (paleo)oceanographic information. Accessing that information requires “unmixing” the various influences on marine sediment geochemistry to understand individual sources and marine geochemical processes. Q‐mode factor analysis (QFA) and independent component analysis (ICA) are multivariate statistical techniques that have successfully been applied to large datasets of marine sediment element concentrations to identify the number and composition of marine sediment sources or end‐members. In this study, we apply both techniques to two datasets of marine sediment geochemistry, compare the output, and discuss the advantages of each approach. In both datasets, ICA identified a mixing trend between carbonates and dust, whereas QFA represented the end‐members as two separate factors. In the Pacific and Indian Oceans dataset, both techniques produced three factors or independent components involving rare earth elements, but two of the QFA factors explained a small, almost negligible, amount of the variability of the dataset. Also, QFA identified more aluminosilicate end‐members (dust or volcanic ash) than ICA. In the Indian Ocean Sites 738 and 752 dataset, ICA identified two processes affecting Sr and Ba concentrations as separate independent components, while QFA created a factor representing the covariation of Sr and Ba over intervals of the site's paleoceanographic history. The results of this study exemplify that QFA identifies covariances and finds discrete end‐members contributing to the bulk mass of sediment. ICA works best with non‐Gaussian element distributions and finds geochemical signals and mixing trends that constitute the characteristic structure of the multielemental data.
印度洋和太平洋沉积物来源的多元统计 "非混合
海洋沉积物地球化学是一个庞大的(古)海洋学信息档案库。要获取这些信息,需要 "去除 "海洋沉积物地球化学的各种影响因素,以了解各个来源和海洋地球化学过程。Q模式因子分析(QFA)和独立成分分析(ICA)是一种多元统计技术,已成功应用于海洋沉积物元素浓度的大型数据集,以确定海洋沉积物来源或终端成分的数量和组成。在本研究中,我们将这两种技术应用于两个海洋沉积物地球化学数据集,比较其输出结果,并讨论每种方法的优势。在这两个数据集中,ICA 确定了碳酸盐和尘埃之间的混合趋势,而 QFA 则将最终成员表示为两个独立的因子。在太平洋和印度洋数据集中,两种技术都产生了三个涉及稀土元素的因子或独立成分,但 QFA 的两个因子只能解释数据集的少量变化,几乎可以忽略不计。此外,QFA 比 ICA 识别出更多的铝硅酸盐终端成员(尘埃或火山灰)。在印度洋 738 号和 752 号站点数据集中,ICA 将影响 Sr 和 Ba 浓度的两个过程识别为单独的独立成分,而 QFA 则创建了一个因子,代表站点古海洋学历史上 Sr 和 Ba 的共变。这项研究的结果说明,QFA 能够识别共变,并找到对沉积物总量有贡献的离散终值。ICA 在处理非高斯元素分布时效果最佳,并能发现构成多元素数据特征结构的地球化学信号和混合趋势。
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来源期刊
CiteScore
4.80
自引率
3.70%
发文量
56
审稿时长
3 months
期刊介绍: Limnology and Oceanography: Methods (ISSN 1541-5856) is a companion to ASLO''s top-rated journal Limnology and Oceanography, and articles are held to the same high standards. In order to provide the most rapid publication consistent with high standards, Limnology and Oceanography: Methods appears in electronic format only, and the entire submission and review system is online. Articles are posted as soon as they are accepted and formatted for publication. Limnology and Oceanography: Methods will consider manuscripts whose primary focus is methodological, and that deal with problems in the aquatic sciences. Manuscripts may present new measurement equipment, techniques for analyzing observations or samples, methods for understanding and interpreting information, analyses of metadata to examine the effectiveness of approaches, invited and contributed reviews and syntheses, and techniques for communicating and teaching in the aquatic sciences.
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