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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
Source PublicationENVIRONMENTAL EARTH SCIENCES
ISSN1866-6280
Volume78Issue:1Pages:11
AbstractLand 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.
KeywordSeabed subsidence GIS Spatial regression GWR Prediction
DOI10.1007/s12665-018-8022-1
Funding OrganizationNational 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
Language英语
Funding ProjectNational Natural Science Foundation of China[41831293] ; National Natural Science Foundation of China[41772341]
Funding OrganizationNational 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 Research AreaEnvironmental Sciences & Ecology ; Geology ; Water Resources
WOS SubjectEnvironmental Sciences ; Geosciences, Multidisciplinary ; Water Resources
WOS IDWOS:000455022900020
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.iggcas.ac.cn/handle/132A11/90198
Collection页岩气与地质工程院重点实验室
Corresponding AuthorMa, Fengshan
Affiliation1.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
Recommended Citation
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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