Advertisement

Assessment of mining-related seabed subsidence using GIS spatial regression methods: a case study of the Sanshandao gold mine (Laizhou, Shandong Province, China)

  • Jiayuan Cao
  • Fengshan MaEmail author
  • Jie Guo
  • Rong Lu
  • Guowei Liu
Original Article
  • 66 Downloads

Abstract

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 R2 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 ± 8.5 mm with a maximum calculated subsidence of − 329.26 mm. The maximum subsidence predicted with the model for the seabed is − 63 mm with a mean subsidence of − 50 mm.

Keywords

Seabed subsidence GIS Spatial regression GWR Prediction 

Notes

Acknowledgements

The research was supported by the National Natural Science Foundation of China (Grant nos. 41831293, 41772341). Grateful appreciation is expressed for the support.

References

  1. Blachowski J (2016) Application of GIS spatial regression methods in assessment of land subsidence in complicated mining conditions: case study of the Walbrzych coal mine (SW Poland). Nat Hazards 84:997–1014CrossRefGoogle Scholar
  2. Coal Industry Promotion Board, CIPB (1997) A study on the Mechanism of subsidence over abandoned mine area and the construction method of subsidence prevention. Coal Industry Promotion Board, Seoul, 97-06:1–67Google Scholar
  3. Dolezalova H, Kajzar V, Soucek K, Stas L (2009) Evaluation of mining subsidence using GPS data. Acta Geodyn Geomater 6(3):359–367Google Scholar
  4. Dolezalova H, Kajzar V, Soucek K, Stas L (2012) Analysis of surface movements from undermining in time. Acta Geodyn Geomater 9(3):389–400Google Scholar
  5. Foster AS, Gorr WL (1986) An adaptive filter for estimating spatially varying parameters: application to modeling police hours spent in response to calls for service. Manag Sci 32(7):878–889CrossRefGoogle Scholar
  6. Fotheringham AS, Brunsdon C, Charlton M (1998) Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environ Plan A 30(11):1905–1927CrossRefGoogle Scholar
  7. Gorr WL, Olligsehlaeger AM (1994) Weighted spatial adaptive filtering: Monte Carlo studies and application to illicit drug market modeling. Geogr Anal 26:67–87CrossRefGoogle Scholar
  8. Missavage RJ, Chugh YP, Roscetti T (1986) Subsidence prediction in shallow room and pillar mines. Int J Min Geol Eng 4:39–46CrossRefGoogle Scholar
  9. Nestbitt A (2003) Subsidence monitoring West Cliff Colliery longwall 5A4.APAS (Association of Public Authority Surveyors) 2003 Conference, Wollongong, Australia, 1–4 April, 133–139Google Scholar
  10. Sahu SP, Yadav M, Das AJ et al (2017) Multivariate statistical approach for assessment of subsidence in Jharia coalfields, India. Arab J Geosci 10:191CrossRefGoogle Scholar
  11. Siriwardane HJ, Amanat J (1986) Analysis of mining subsidence caused by underground mining. Int J Min Eng 2:271–290CrossRefGoogle Scholar
  12. Waltham AC (1989) Ground subsidence. Blackie & Son Ltd, New York, pp 49–97Google Scholar
  13. Wu S, Yu Z, Zhou D, Zhang H (2006) Structural features and Cenozoic evolution of the Tan-Lu fault zone in the Laizhou Bay, Bohai Sea. Mar Geol Quat Geol 26:101–110Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Jiayuan Cao
    • 1
    • 2
    • 3
  • Fengshan Ma
    • 1
    • 2
    Email author
  • Jie Guo
    • 1
    • 2
  • Rong Lu
    • 1
    • 2
    • 3
  • Guowei Liu
    • 1
    • 2
    • 3
  1. 1.Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and GeophysicsChinese Academy of SciencesBeijingChina
  2. 2.Institutions of Earth ScienceChinese Academy of SciencesBeijingChina
  3. 3.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations