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Assessment of mining-related seabed subsidence using GIS spatial regression methods: a case study of the Sanshandao gold mine (Laizhou, Shandong Province, China)
Cao, Jiayuan1,2,3; Ma, Fengshan1,2; Guo, Jie1,2; Lu, Rong1,2,3; Liu, Guowei1,2,3
2019
发表期刊ENVIRONMENTAL EARTH SCIENCES
ISSN1866-6280
卷号78期号:1页码:11
摘要Land subsidence in the Sanshandao area, Laizhou, Shandong Province, China, has been a consequence of underground gold mining. This paper identifies the statistically significant mining subsidence factors, which are: (1) a digital elevation model of the surface; (2) the surface slope; (3) the slope aspect; (4) the thickness of the gold deposits; and (5) the depth of the gold deposits below the ground. The vertical displacement of the GPS monitoring in the Xishan gold mine (one of the Sanshandao gold mine) was selected as the dependent variable and five mining subsidence factors as the independent variables. Subsidence modeling was carried out in geographic information systems first with the ordinary least squares (OLS) method and then with the geographically weighted regression (GWR) method. Finally, the seabed subsidence was predicted with the geographically weighted regression model for the Xinli gold mine (another of the Sanshandao gold mine), in which the gold deposits are located under the sea. The results of the GWR analysis showed a marked improvement compared to those of the OLS analysis. The R-2 value of the GWR model equals 0.82, which indicates that the model captured the spatial heterogeneity of the independent variables. The accuracy of determining subsidence in the area used for validation is +/- 8.5mm with a maximum calculated subsidence of - 329.26mm. The maximum subsidence predicted with the model for the seabed is - 63mm with a mean subsidence of - 50mm.
关键词Seabed subsidence GIS Spatial regression GWR Prediction
DOI10.1007/s12665-018-8022-1
资助者National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China
语种英语
资助项目National Natural Science Foundation of China[41831293] ; National Natural Science Foundation of China[41772341]
资助者National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China
WOS研究方向Environmental Sciences & Ecology ; Geology ; Water Resources
WOS类目Environmental Sciences ; Geosciences, Multidisciplinary ; Water Resources
WOS记录号WOS:000455022900020
出版者SPRINGER
引用统计
文献类型期刊论文
条目标识符http://ir.iggcas.ac.cn/handle/132A11/90198
专题页岩气与地质工程院重点实验室
通讯作者Ma, Fengshan
作者单位1.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Shale Gas & Geoengn, Beijing 100029, Peoples R China
2.Chinese Acad Sci, Inst Earth Sci, Beijing 100029, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714
Cao, Jiayuan,Ma, Fengshan,Guo, Jie,et al. Assessment of mining-related seabed subsidence using GIS spatial regression methods: a case study of the Sanshandao gold mine (Laizhou, Shandong Province, China)[J]. ENVIRONMENTAL EARTH SCIENCES,2019,78(1):11.
APA Cao, Jiayuan,Ma, Fengshan,Guo, Jie,Lu, Rong,&Liu, Guowei.(2019).Assessment of mining-related seabed subsidence using GIS spatial regression methods: a case study of the Sanshandao gold mine (Laizhou, Shandong Province, China).ENVIRONMENTAL EARTH SCIENCES,78(1),11.
MLA Cao, Jiayuan,et al."Assessment of mining-related seabed subsidence using GIS spatial regression methods: a case study of the Sanshandao gold mine (Laizhou, Shandong Province, China)".ENVIRONMENTAL EARTH SCIENCES 78.1(2019):11.
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