刘祥奇, 宋磊, 吴奇龙, 李国民, 毛欣. 基于粒度分布曲线的邻近传播聚类算法在沉积环境识别中的应用——以白洋淀地区为例[J]. 海洋地质与第四纪地质, 2020, 40(1): 198-209. DOI: 10.16562/j.cnki.0256-1492.2018100802
引用本文: 刘祥奇, 宋磊, 吴奇龙, 李国民, 毛欣. 基于粒度分布曲线的邻近传播聚类算法在沉积环境识别中的应用——以白洋淀地区为例[J]. 海洋地质与第四纪地质, 2020, 40(1): 198-209. DOI: 10.16562/j.cnki.0256-1492.2018100802
LIU Xiangqi, SONG Lei, WU Qilong, LI Guomin, MAO Xin. Application of the affinity propagation clustering algorithm based on grain-size distribution curve to discrimination of sedimentary environment——A case study in Baiyangdian area[J]. Marine Geology & Quaternary Geology, 2020, 40(1): 198-209. DOI: 10.16562/j.cnki.0256-1492.2018100802
Citation: LIU Xiangqi, SONG Lei, WU Qilong, LI Guomin, MAO Xin. Application of the affinity propagation clustering algorithm based on grain-size distribution curve to discrimination of sedimentary environment——A case study in Baiyangdian area[J]. Marine Geology & Quaternary Geology, 2020, 40(1): 198-209. DOI: 10.16562/j.cnki.0256-1492.2018100802

基于粒度分布曲线的邻近传播聚类算法在沉积环境识别中的应用——以白洋淀地区为例

Application of the affinity propagation clustering algorithm based on grain-size distribution curve to discrimination of sedimentary environment——A case study in Baiyangdian area

  • 摘要: 以白洋淀地区出露的22个沉积剖面中沉积物的粒度数据为研究对象,利用邻近传播聚类算法(AP聚类算法)进行聚类分析,并与已知典型沉积环境形成的沉积物粒度频率分布曲线进行对比,探讨了基于沉积物粒度特征的邻近传播聚类算法在沉积环境识别中应用的可行性以及白洋淀地区不同沉积环境的粒度特征。结果表明:邻近传播聚类算法能够将相同或者相近动力条件下形成的沉积物聚为一类,挖掘沉积物粒度数据中蕴含的沉积动力信息;所有样品的沉积物粒度频率分布曲线划分为11类簇;考虑到同一沉积环境中动力条件的变化,将聚类结果得到的分布范围、形状相近的11簇曲线进一步合并为4组曲线,并与已知典型沉积环境粒度曲线进行对比,识别出湖沼相、湖相、河流相、洪积相等4种主要的沉积相。其中,湖沼相与湖心相沉积环境、湖滨相与河漫滩相沉积环境形成的沉积物粒度组分相近。湖沼相与湖心相、湖滨相与漫滩相、河床相的沉积物粒度主要组分分别为细粉砂、粗粉砂、细砂或粗砂,洪积物中粗粉砂、中砂、粗砂含量相近,呈多峰态。邻近聚类传播算法可为沉积环境动力条件反演、分区等提供潜在的新手段。

     

    Abstract: In the paper, 22 sedimentary sections are selected from the Baiyangdian region as research carriers. The affinity propagation (AP) clustering algorithm is adopted to cluster the similar grain size distribution of sediment into clusters. The results are then compared with the characteristics of grain size distribution which are known in typical sedimentary environments. The grain size distribution patterns in different sedimentary environments in Baiyangdian area are concluded and the feasibility of application of the affinity propagation clustering algorithm based on sediment grain characteristics to environmental interpretation discussed. The results suggests that the AP clustering algorithm can gather the sediments formed under the same or similar dynamical conditions into groups, and dig out the sedimentary dynamical information contained in the grain size data; The distribution curves of grain size for all samples are subdivided into 11 clusters. Considering the change in dynamic conditions in the same sedimentary environment, the 11 cluster curves are further categorized into 4 sets of curves, which could be compared with the grain size curves from known environment. 4 sedimentary facies i.e. the lacustrine-swamp facies, lacustrine facies, fluvial facies and alluvial facies are recognized. Among them, in terms of sedimentary dynamics, the lacustrine-swamp facies are similar with the central lake facies, while the lake shore facies similar with alluvial flat facies. The lacustrine-swamp facies and central lake facies are mainly composed of fine silty sand, whereas the lake shore facies and alluvial flat facies composed of coarse silt. River bed facies consist of sand or coarse sand. The contents of coarse silt, medium sand and coarse sand are similar in the flooding deposits, characterized by multi-peak curves. The performance proves that the AP clustering algorithm can provide a new mean for inversion and zonation of sedimentary environment conditions.

     

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