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Distribution of clay minerals in surface sediments off Yangtze River estuary. OceanDocs
Lan, Xianhong; Zhang, Zhixun; Li, Rihui; Wang, Zhongbo; Chen, Xiaohui; Tian, Zhenxing.
Based on the clay minerals, content analysis from 187 stations in the surface sediments, this paper dealt with the assemblages and the distributions of clay minerals in the surface sediments off the Yangtze River estuary. The research results showed that the illite dominates the surface sediments off the Yangtze River estuary with the smectite or kaolinite the second rank and chlorite the third rank; the illite-smectite- kaolinite- chlorite-assemblage is the main type of the clay minerals assemblage in the area, with the illite-kaolinite-chlorite-smectite -type as the second rank; the source of clay mineral is mainly from the Yellow River and the Yangtze River. The Q-type cluster analysis show that sediments from both recent and ancient Yellow River were...
Tipo: Journal Contribution Palavras-chave: Clay minerals; Distribution.
Ano: 2012 URL: http://hdl.handle.net/1834/5849
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Prediction of Petroleum Prospect in the South Yellow Sea Basin Using Satellite Remote Sensing. OceanDocs
Gong, Jianming; Chen, Jianwen; Liang, Jie; Zhang, Zhixun; Liu, Chuang; Lu, Zhenquan; Huang, Fulin.
Using satellite remote sensing to measure the sea surface temperature of the East China Sea Shelf Basin can serve the purpose of predicting the petroleum prospect in the South Yellow Sea Basin. The satellite thermal infrared temperature anomaly area always repeats in the same position as the proved oil prospect area in the East China Sea Shelf Basin, and coincides well with both the CH4 content curve at the water-atmosphere interface and the seafloor geochemical anomaly. The sea surface temperature anomaly areas of the South Yellow Sea Basin in the satellite remote sensing image go banded along 123?30? in the S-N direction, and naturally follow the aeromagnetically interpreted eastern nose-type uplift that shows a S-N strike. The north and south ends of...
Tipo: Journal Contribution Palavras-chave: Remote satellite sensing; Petroleum; Prediction.
Ano: 2007 URL: http://hdl.handle.net/1834/5865
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