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S. E. M. analysis of quartz sand grains from selected Hong Kong and Chinese littoral environments
作者姓名:A. T. Williams  M. C. Thomas
作者单位:Coastal Research Unit,Department of Science and Chemical Engineering.the Polytechnic of Wales,Pontypridd,Mid Glamorgan,CF37 IDL.UK,Coastal Research Unit,Department of Science and Chemical Engineering.the Polytechnic of Wales,Pontypridd,Mid Glamorgan,CF37 IDL.UK
摘    要:This pilot study attempts to demonstrate some underlying scanning electron microscopy themes of quartz grain surface textures. A variety of textural patterns and individual features are described for grains selected from various littoral environments. An attempt was made to differentiate samples on surface textures alone, but limitations of using this technique in sedimentological isolation were apparent. Statistical analysis of checklist data and photographic evidence revealed some of the more important feature combinations used in environmental diagnosis. The use of discriminant analysis provided quantitative sample separation.

收稿时间:1988/12/17 0:00:00
修稿时间:1988/12/20 0:00:00

S. E. M. analysis of quartz sand grains from selected Hong Kong and Chinese littoral environments
A. T. Williams,M. C. Thomas.S. E. M. analysis of quartz sand grains from selected Hong Kong and Chinese littoral environments[J].Acta Oceanologica Sinica,1989,8(3):409-422.
Authors:A T Williams and M C Thomas
Institution:Coastal Research Unit, Department of Science and Chemical Engineering, the Polytechnic of Wales, Pontypridd, Mid Glamorgan, CF37 1DL, UK
Abstract:This pilot study attempts to demonstrate some underlying scanning electron microscopy themes of quartz grain surface textures.A variety of textural patterns and individual features are described for grains selected from various littoral environments.An attempt was made to differentiate samples on surface textures alone,but limitations of using this technique in sedimentological isolation were apparent.Statistical analysis of checklist data and photographic evidence revealed some of the more important feature combinations used in environmental diagnosis.The use of discriminant analysis provided quantitative sample separation.
Keywords:
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