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Classifying the sedimentary environments of the Xincun Lagoon, Hainan Island, by system cluster and principal component analyses

机译:通过系统聚类和主成分分析对海南岛新村泻湖的沉积环境进行分类

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摘要

An understanding of the sedimentary environment in relation to its controlling factors is of great importance in coastal geomorphology, ecology, tourism and aquaculture studies. We attempt to deal with this issue, using a case study from the Xincun Lagoon, Hainan Island in southern China. For the study, surficial sediment samples were collected, together with hydrodynamic and bathymetric surveys, during August 2013. Numerical simulation was carried out to obtain high-spatial resolution tidal current data. The sediment samples were analyzed to derive mean grain size, sorting coefficient, skewness and kurtosis, together with the sand, silt and clay contents. The modern sedimentary environments were classified using system cluster and principal component analyses. Grain size analysis reveals that the sediments are characterized by extremely slightly sandy silty mud (ESSSM) and slightly silty sand (SSS), which are distributed in the central lagoon and near-shore shallow water areas, respectively. Mean grain size varies from 0 to 8.0Ф, with an average of 4.6Ф. The silt content is the highest, i.e., 52% on average, with the average contents of sand and clay being 43% and 5%, respectively. There exists a significant correlation between mean size and water depth, suggesting that the surficial sediments become finer with increasing water depth. Cluster analyses reveals two groups of samples. The first group is characterized by mean grain size of more than 5.5Ф, whilst the second group has mean grain size of below 3.5Ф. Further, these groups also have different correlations between mean grain size and the other grain size parameters. In terms of the tidal current, the average values of the root mean square velocity (RMSV) are 7.5 cm/s and 6.9 cm/s on springs and neaps, respectively. For the RMSVs that are higher than 4 cm/s, a significant positive correlation is found between the content of the 63–125 μm fraction and the RMSV, suggesting that the RMSV determines the variability of the very fine sand fraction. Based on system cluster and principal component analyses (PCA), the modern sedimentary environments are classified into three types according to the grain size parameters, RMSVs and water depth data. The results suggest the importance of grain size parameters and high-spatial resolution hydrodynamic data in differentiating the coastal sedimentary environments.
机译:对沉积环境及其控制因素的理解在沿海地貌,生态学,旅游业和水产养殖研究中具有重要意义。我们尝试使用来自中国南部海南岛新村泻湖的案例研究来解决这个问题。为了进行研究,2013年8月期间收集了表面沉积物样本以及水动力和水深测量。进行了数值模拟,以获得高空间分辨率的潮流数据。对沉积物样品进行分析,得出平均粒度,分选系数,偏度和峰度以及沙子,粉砂和粘土含量。利用系统聚类和主成分分析对现代沉积环境进行了分类。粒度分析表明,沉积物的特征是极细沙质的粉质淤泥(ESSSM)和微粉质沙(SSS),它们分别分布在中央泻湖和近岸浅水区。平均晶粒度从0到8.0Ф不等,平均为4.6Ф。泥沙含量最高,平均为52%,沙子和粘土的平均含量分别为43%和5%。平均大小与水深之间存在显着的相关性,表明表层沉积物随着水深的增加而变得更细。聚类分析揭示了两组样本。第一组的特征是平均晶粒度大于5.5Ф,而第二组的平均晶粒度小于3.5Ф。此外,这些组在平均晶粒尺寸和其他晶粒尺寸参数之间也具有不同的相关性。就潮流而言,弹簧和棉结的均方根速度(RMSV)的平均值分别为7.5 cm / s和6.9 cm / s。对于高于4 cm / s的RMSV,在63–125μm馏分的含量与RMSV之间发现显着的正相关关系,这表明RMSV决定了非常细的沙粒的变异性。基于系统聚类和主成分分析(PCA),现代沉积环境根据粒度参数,RMSVs和水深数据分为三类。结果表明粒度参数和高空间分辨率水动力数据在区分沿海沉积环境中的重要性。

著录项

  • 来源
    《海洋学报(英文版)》 |2017年第4期|64-71|共8页
  • 作者单位

    State Key Laboratory for Estuarine and Coastal Research, East China Normal University, Shanghai 200062, China;

    Ministry of Education Key Laboratory for Coast and Island Development, Nanjing University, Nanjing 210093, China;

    State Key Laboratory for Estuarine and Coastal Research, East China Normal University, Shanghai 200062, China;

    Ministry of Education Key Laboratory for Coast and Island Development, Nanjing University, Nanjing 210093, China;

    State Key Laboratory for Estuarine and Coastal Research, East China Normal University, Shanghai 200062, China;

    Ministry of Education Key Laboratory for Coast and Island Development, Nanjing University, Nanjing 210093, China;

    College of Harbour, Coastal and Offshore Engineering, Hohai University, Nanjing 210098, China;

    Ministry of Education Key Laboratory for Coast and Island Development, Nanjing University, Nanjing 210093, China;

    Ministry of Education Key Laboratory for Coast and Island Development, Nanjing University, Nanjing 210093, China;

    Ministry of Education Key Laboratory for Coast and Island Development, Nanjing University, Nanjing 210093, China;

    Ministry of Education Key Laboratory for Coast and Island Development, Nanjing University, Nanjing 210093, China;

  • 收录信息 中国科学引文数据库(CSCD);中国科技论文与引文数据库(CSTPCD);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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  • 入库时间 2022-08-19 03:57:50
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