Assessment of mining-related seabed subsidence using GIS spatial regression methods: a case study of the Sanshandao gold mine (Laizhou, Shandong Province, China)
文献类型：期刊论文
| 作者 | Cao, Jiayuan1,2,3; Ma, Fengshan1,2; Guo, Jie1,2; Lu, Rong1,2,3; Liu, Guowei1,2,3 |
| 刊名 | ENVIRONMENTAL EARTH SCIENCES
 |
| 出版日期 | 2019 |
| 卷号 | 78期号:1页码:11 |
| 关键词 | Seabed subsidence GIS Spatial regression GWR Prediction |
| ISSN号 | 1866-6280 |
| DOI | 10.1007/s12665-018-8022-1 |
| 英文摘要 | 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. |
| 资助项目 | National Natural Science Foundation of China[41831293] ; National Natural Science Foundation of China[41772341] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000455022900020 |
| 出版者 | SPRINGER |
| 资助机构 | 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 ; 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 |
| 源URL | [http://ir.iggcas.ac.cn/handle/132A11/90198]  |
| 专题 | 地质与地球物理研究所_中国科学院页岩气与地质工程重点实验室 |
| 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. |
入库方式： OAI收割
来源：地质与地球物理研究所
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