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The evaluation of macroscopic and microscopic textures of sand grains using elliptic Fourier and principal component analysis: Implications for the discrimination of sedimentary environments

机译:椭圆傅里叶和主成分分析法评价砂粒的宏观和微观织构:对沉积环境判别的意义

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摘要

A method that integrates elliptic Fourier and principal component analysis is a new development in the analysis of the shapes of sand grains. However, conventional elliptic Fourier and principal component analysis based on the variance-covariance matrix of the elliptic Fourier results can determine only the form of sand grains, and fails to quantify fine-scale boundary smoothness of grains. In this study, sand grains from glacial, fluvial, foreshore and aeolian environments were analysed using both elliptic Fourier and principal component analysis and an extension of elliptic Fourier and principal component analysis based on the correlation matrix to extract information on grain form (macroscopic) and grain boundary smoothness (microscopic) separately. Conventional elliptic Fourier and principal component analysis based on the variance-covariance matrix produces macroscopic particle shape descriptors, such as the elongation index and bump indices. These indices indicate that sand grains exposed to subaqueous transportation (fluvial and foreshore) have forms that are more elongated than those exposed to subaerial transportation (aeolian dunes). However, elliptic Fourier and principal component analysis based on the correlation matrix is, in addition, able to extract microscopic particle features, which can be interpreted in terms of a boundary smoothness index. The boundary smoothness index indicates that the surfaces of glacial grains are the most rugged, whereas the surfaces of aeolian grains are the smoothest. On bivariate plots of the boundary smoothness and elongation indices, samples from fluvial, foreshore, aeolian and glacial environments cluster in discrete regions. In addition, the analysis reveals that glacial grains are exposed to different morphological maturation pathways than those from fluvial, foreshore and aeolian environments.
机译:结合椭圆傅立叶和主成分分析的方法是砂粒形状分析的新进展。然而,基于椭圆傅里叶结果的方差-协方差矩阵的常规椭圆傅里叶和主成分分析只能确定砂粒的形式,而无法量化晶粒的细尺度边界光滑度。在这项研究中,使用椭圆傅立叶和主成分分析方法对冰川,河流,前滨和风沙环境中的沙粒进行了分析,并基于相关矩阵对椭圆傅立叶和主成分分析进行了扩展,以提取颗粒形态信息(宏观)和晶界光滑度(微观)分开。基于方差-协方差矩阵的常规椭圆傅立叶和主成分分析会产生宏观粒子形状描述符,例如伸长指数和凹凸指数。这些指数表明,暴露于水下运输(河流和前滨)的沙粒的形状比暴露于地下运输(风沙丘)的沙粒的形状更长。然而,此外,基于相关矩阵的椭圆傅立叶和主成分分析还能够提取微观粒子特征,这可以用边界平滑度指数来解释。边界光滑度指数表明,冰川颗粒的表面最粗糙,而风沙颗粒的表面最光滑。在边界平滑度和伸长率指数的二元图上,河流,前滨,风沙和冰川环境的样本聚集在离散区域。此外,分析表明,与来自河流,前滨和风沙环境的冰川相比,冰川谷所经历的形态成熟途径不同。

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