Geochemical classification of the soil in a typical black soil area using the principal component analysis combined with K-means clustering algorithm
LIU Kai1,2,3(), DAI Hui-Min1,2,3, LIU Guo-Dong1,2,3, SONG Yun-Hong1,2,3, LIANG Shuai1,2,3(), YANG Ze1,2,3
1. Shenyang Center of China Geological Survey, Shenyang 110034, China 2. Key Laboratory of Black Soil Evolution and Ecological Effect, Ministry of Natural Resources, Shenyang 110034, China 3. Key Laboratory of Black Soil Evolution and Ecological Effect, Liaoning Province, Shenyang 110034, China
The geochemical classification of soils is significant for agricultural and ecological regionalization. Based on the data on major elements in soil obtained from the multi-purpose regional geochemical survey, this study conducted the geochemical classification for a typical black soil area in northeast China using the principal component analysis combined with the K-means clustering algorithm (also referred to as the principal component clustering method). The results are as follows. The soil parent materials are the main factor controlling the characteristics of major elements in the soil. It is the most appropriate to divide the soil samples from the typical black soil area into five categories using the principal component clustering method. Various samples had significantly different major element contents (P<0.05). The geochemical classification results corresponded to the Quaternary geological units to a certain degree and can better reflect the actual distribution of soil parent materials. Moreover, the high SiO2 content in the black soil area in the southern Songhua River indicates desertification, to which much attention should be paid in the protection of the black soil.
刘凯, 戴慧敏, 刘国栋, 宋运红, 梁帅, 杨泽. 基于主成分聚类法的典型黑土区土壤地球化学分类[J]. 物探与化探, 2022, 46(5): 1132-1140.
LIU Kai, DAI Hui-Min, LIU Guo-Dong, SONG Yun-Hong, LIANG Shuai, YANG Ze. Geochemical classification of the soil in a typical black soil area using the principal component analysis combined with K-means clustering algorithm. Geophysical and Geochemical Exploration, 2022, 46(5): 1132-1140.
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