华南海岸现代沙丘沙与海滩沙粒度的定量识别  

Quantitative Discrimination of Grain Size of Modern Coastal Aeolian Sands and Beach Sands of South China

作　　者：唐丽[1,2] 董玉祥[1] 汪旭东 

机构地区：[1]中山大学地理科学与规划学院∥广东省城市化与地理环境空间模拟重点实验室,广东广州510275 [2]广州大学教师培训学院,广东广州510405 [3]中海石油(中国)有限公司深圳分公司研究院,广东广州510240

出　　处：《中山大学学报（自然科学版）》2015年第6期130-136,共7页Acta Scientiarum Naturalium Universitatis Sunyatseni

基　　金：国家自然科学基金资助项目(41371030;40971007)

摘　　要：海岸沙丘沙和海滩沙的粒度识别与区分是海岸风沙研究中争论较大的问题之一。以我国华南海岸为研究区域,基于在海岸风沙地貌广泛发育的闽南、粤东、粤西、海南岛沿岸4个区域的141个现代海岸沙丘沙和70个海滩沙样品的粒度数据,尝试运用多元线性判别分析和人工神经网络分析两种方法进行现代沙丘沙和海滩沙粒度的定量识别。结果表明,比较而言非线性的人工神经网络分析法较传统线性判别分析方法的识别率为高,人工神经网络分析可以通过粒度数据区分部分海岸的沙丘沙和海滩沙,但整体而言2种定量识别方法均难以完全对华南海岸的现代沙丘沙和海滩沙进行粒度的有效区分。The discrimination of grain size of modern aeolian sands and beach sands is one of the heated argument problems in coastal aeolian research. Based on the data of grain size of 141 modern Aeolian sand samples and 70 beach sand samples from the southern Fujian coast,eastern Guangdong coast,western Guangdong coast and Hainan island coast which are the typical coastal dune distribution places in south China coast,the modern aeolian sands and beach sands are quantitatively discriminated by use of the multivariate linear discriminant analysis method and B-P artificial neural network analysis method. The results show that the B-P artificial neural network analysis method has higher discrimination precision than the multivariate linear discriminant analysis method. Modern aeolian sands and beach sands in Hainan island coast could be discriminated based on the grain size data by use of the B-P artificial neural network analysis method. However,viewing the results as a whole,the modern aeolian sands and beach sands in south China coast could not be significantly discriminated based on grain size by using two quantitative discriminated methods.

关 键 词：华南海岸 沙丘沙 海滩沙 粒度 定量识别 

分 类 号：P931.3[天文地球—自然地理学]