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Application And Comparision Of Two Classification Methods Of Surface Sediments In The Estuary

Posted on:2018-01-11Degree:MasterType:Thesis
Country:ChinaCandidate:X L WangFull Text:PDF
GTID:2322330518983985Subject:Hydraulics and river dynamics
Abstract/Summary:PDF Full Text Request
Based on the grain size information of surface sediment samples collected from Yangtze Estuary Deepwater Channel in August 2015 and March 2016,the grain size frequency curves and probability cumulative curve of samples were plotted,and the main grain size parameters(median particle size,average particle size,sorting coefficient,skewness and kurtosis)were calculated using Folk & word method,through the analysis of the temporal and spatial distribution characteristics of the above parameters,we achieved a preliminary understanding of these surface sediments;Then these surface sediments were classified by Folk's classification and Q-system clustering which combined with R-factor analysis.The final conclusions were obtained as follows:1.The surface sediments from Nangang Channel to North Passage were divided into four categories and five categories in August 2015 and March 2016,the sediment grain size shows a fining trend in March 2016,especially in North Passage.2.The Nangang Channel and North Passage were divided into four and five different depositional areas with different deposition factors,The formation mechanism of sediments is complex,except parts of Nangang Channel and Yuanyuansha Passage.3.Folk trigonometric classification requires less basic parameters and simpler,but will have some deviation,when used for classification of estuarine sediments;Multivariate statistical method requires more basic parameters and complex,but the classification results were more scientific and reasonable;Multivariate statistical method should be selected preferentially in the classification of estuarine sediments when have enough basic datas.
Keywords/Search Tags:South Channel & North passage, Surface Sediments, Folk's Classification, R-factor Analysis, Q-system Clustering
PDF Full Text Request
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