Abstract
Abstract To identify the provenance of fine-grained sediments in the northern East China Sea, various magnetic parameters indicating concentration (magnetic susceptibility, susceptibility of anhysteretic remanent magnetization (χ ARM ), and saturation of isothermal remanent magnetization (SIRM)), mineral composition (S 300 = − IRM − 300 mT / SIRM and HIRM (%) = 50 (SIRM − IRM 300 mT ) / SIRM), and grain size (frequency-dependent magnetic susceptibility (χ FD ), χ ARM / SIRM, and S 20 = IRM 20 mT / SIRM) were measured from surface sediments collected at 98 regularly spaced sites. As an unbiased analytic tool, fuzzy clustering analysis was performed on data from 97 sites except an outlier. Each dataset for each site comprised magnetic parameters representing ultrafine superparamagnetic (χ FD ), fine single-domain or pseudo-single-domain (χ ARM / SIRM), and coarse multidomain (S 20 ) fractions, and the relative dominance of low- and high-coercivity minerals (S 300 and HIRM (%), respectively). The statistical analysis yielded four clusters, indicating that the sediments in the study area can be differentiated magnetically. Of the magnetic properties of the cluster centers, the most distinctive feature of cluster 1 is a predominance of coarse-grained magnetic minerals (highest S 20 value), while the contribution of high-coercivity minerals is highest in cluster 2 (highest HIRM (%) value). Ultrafine- and fine-grained fractions are prominent in cluster 3 (highest χ FD and χ ARM / SIRM values), while cluster 4 has the highest ratio of low- to high-coercivity minerals (highest S 300 value). Related to sediment provenance, the sediments of cluster 1 are interpreted to have been derived from the Yangtze River in China. The old Yellow River deposits are the primary source of sediments in cluster 2. Sediments in cluster 3 were probably transported from Taiwan or the Northwest Pacific by the Taiwan Warm Current or the Kuroshio Current, respectively, while Korean river deposits are the most probable sources of sediments in cluster 4.
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