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Unmixing grain-size distributions in lake sediments: a new method of endmember modeling using hierarchical clustering

  • Xiaonan Zhang (a1), Aifeng Zhou (a1), Xin Wang (a1), Mu Song (a2), Yongtao Zhao (a1), Haichao Xie (a1), James M. Russell (a3) and Fahu Chen (a1) (a4)...


The grain-size distribution (GSD) of sediments provides information on sediment provenance, transport processes, and the sedimentary environment. Although a wide range of statistical parameters have been applied to summarize GSDs, most are directed at only parts of the distribution, which limits the amount of environmental information that can be retrieved. Endmember modeling provides a flexible method for unmixing GSDs; however, the calculation of the exact number of endmembers and geologically meaningful endmember spectra remain unresolved using existing modeling methods. Here we present the methodology hierarchical clustering endmember modeling analysis (CEMMA) for unmixing the GSDs of sediments. Within the CEMMA framework, the number of endmembers can be inferred from agglomeration coefficients, and the grain-size spectra of endmembers are defined on the basis of the average distance between the samples in the clusters. After objectively defining grain-size endmembers, we use a least squares algorithm to calculate the fractions of each GSD endmember that contributes to individual samples. To test the CEMMA method, we use a grain-size data set from a sediment core from Wulungu Lake in the Junggar Basin in China, and find that application of the CEMMA methodology yields geologically and mathematically meaningful results. We conclude that CEMMA is a rapid and flexible approach for analyzing the GSDs of sediments.


Corresponding author

*Corresponding authors at: Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China. E-mail addresses: (F. Chen); (X. Zhang).


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Unmixing grain-size distributions in lake sediments: a new method of endmember modeling using hierarchical clustering

  • Xiaonan Zhang (a1), Aifeng Zhou (a1), Xin Wang (a1), Mu Song (a2), Yongtao Zhao (a1), Haichao Xie (a1), James M. Russell (a3) and Fahu Chen (a1) (a4)...


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