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Grain size-dependent geochemical evidence reveals provenance and implications of aeolian sands,Dinggye region,southern Tibet 被引量:2
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作者 chen you-gui PAN Mei-hui +2 位作者 HAO Ze-wen QI Yu-han LI chen-lu 《Journal of Mountain Science》 SCIE CSCD 2022年第7期1998-2014,共17页
Identifying geochemical characteristics of aeolian sands on the Qinghai-Tibet Plateau(QTP)is essential for understanding the relationship between earth surface processes and paleoclimatic fluctuations in the region.He... Identifying geochemical characteristics of aeolian sands on the Qinghai-Tibet Plateau(QTP)is essential for understanding the relationship between earth surface processes and paleoclimatic fluctuations in the region.Here,we present new geochemical data which provides insight to the sedimentary environment of aeolian sands in the Dinggye region,southern Tibet.We sampled aeolian dune sands in a variety of settings,and determined grain size and concentration of major oxides and trace elements in the fine and coarse fractions.Results show that aeolian sediments are dominated by fine and medium sands,with a single-peaked frequency curve and a 3-stage probability cumulative curve.The fine and coarse fractions exhibit considerable heterogeneity in elemental concentrations and ratios and upper continental crust-normalized(UCC)distribution.The geochemical evidence indicates that wind dynamic sorting is responsible for the differentiation between fine and coarse fractions in different types of aeolian sand,rather than sediment provenance.Additionally,fine-fraction sediments are well dispersed and can be differentiated from the coarse fraction,suggesting that they contain more environmental information.Multidimensional scale(MDS)and principal component analysis(PCA)of commonly used tracer elements show that flood plain sediments are the sand source for mobile dunes and nebkhas,and lakeshore sediments are the sand source for climbing sand sheets. 展开更多
关键词 Aeolian sediments Geochemical characteristics Grain-size composition PROVENANCE Multidimensional scaling Principal component analysis
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