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Land Subsidence Estimation for Aquifer Drainage Induced by Underground Mining

Author

Listed:
  • Artur Guzy

    (Department of Mining Areas Protection, Geoinformatics and Mining Surveying, Faculty of Mining Surveying and Environmental Engineering, AGH University of Science and Technology, 30-059 Cracow, Poland)

  • Wojciech T. Witkowski

    (Department of Mining Areas Protection, Geoinformatics and Mining Surveying, Faculty of Mining Surveying and Environmental Engineering, AGH University of Science and Technology, 30-059 Cracow, Poland)

Abstract

Land subsidence caused by groundwater withdrawal induced by mining is a relatively unknown phenomenon. This is primarily due to the small scale of such movements compared to the land subsidence caused by deposit extraction. Nonetheless, the environmental impact of drainage-related land subsidence remains underestimated. The research was carried out in the “Bogdanka” coal mine in Poland. First, the historical impact of mining on land subsidence and groundwater head changes was investigated. The outcomes of these studies were used to construct the influence method model. With field data, our model was successfully calibrated and validated. Finally, it was used for land subsidence estimation for 2030. As per the findings, the field of mining exploitation has the greatest land subsidence. In 2014, the maximum value of the phenomenon was 0.313 cm. However, this value will reach 0.364 m by 2030. The spatial extent of land subsidence caused by mining-induced drainage extends up to 20 km beyond the mining area’s boundaries. The presented model provided land subsidence patterns without the need for a complex numerical subsidence model. As a result, the method presented can be effectively used for land subsidence regulation plans considering the impact of mining on the aquifer system.

Suggested Citation

  • Artur Guzy & Wojciech T. Witkowski, 2021. "Land Subsidence Estimation for Aquifer Drainage Induced by Underground Mining," Energies, MDPI, vol. 14(15), pages 1-36, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4658-:d:606074
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    Citations

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    Cited by:

    1. Leszek Chomacki & Janusz Rusek & Leszek Słowik, 2022. "Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines," Energies, MDPI, vol. 15(11), pages 1-23, May.
    2. Xuzi Jiang & Xinju Li & Jing Li & Xiao Hu, 2024. "Examining subsidence change regularity in high groundwater level coal mining areas using Sentinel-1A time-series data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(12), pages 11239-11253, September.
    3. Piotr Strzałkowski, 2022. "Predicting Mining Areas Deformations under the Condition of High Strength and Depth of Cover," Energies, MDPI, vol. 15(13), pages 1-17, June.

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