[1] LI, Menghao, Yichen ZHANG, Jiquan ZHANG, Zhou WEN, Jintao HUANG and Haoying LI. Monitoring and Prediction of Subsidence in Mining Areas of Liaoyuan Northern New District Based on InSAR Technology. GeoHazards [online]. 2026, 7(1), 17. ISSN 2624-795X. Available at: doi:10.3390/geohazards7010017
[2] SONI, Rupika, Mohammad Soyeb ALAM and Gajendra K. VISHWAKARMA. Prediction of InSAR deformation time-series using improved LSTM deep learning model. Scientific Reports [online]. 2025, 15(1). ISSN 2045-2322. Available at: doi:10.1038/s41598-024-83084-1
[3] ZHOU, Bang, Yueguan YAN, Huayang DAI, Jianrong KANG, Xinyu XIE and Zhimiao PEI. Mining Subsidence Prediction Model and Parameters Inversion in Mountainous Areas. Sustainability [online]. 2022, 14(15), 9445. ISSN 2071-1050. Available at: doi:10.3390/su14159445
[4] KRATZSCH, Helmut. Mining Subsidence Engineering [online]. Berlin, Heidelberg: Springer Berlin Heidelberg, 1983 [2026-08-25]. ISBN 978-3-642-81925-4 978-3-642-81923-0. Available at: doi:10.1007/978-3-642-81923-0
[5] WANG, Lihui, Rongfei YANG, Yong LEI, Chengcheng FAN, Jibo LIU and Chuanjian REN. Applicability Of 3d Laser Scanning Technology In Mining Subsidence Monitoring. Engineering Heritage Journal. 2023, 8(1), 1–6.
Influence by Undermining
| Type of study | BachelorFollow-up Master |
|---|---|
| Language of instruction | Czech |
| Code | 544-0039/06 |
| Abbreviation | VP |
| Course title | Influence by Undermining |
| Credits | 5 |
| Coordinating department | Department of Geodesy and Mine Surveying |
| Course coordinator | doc. Ing. Juraj Gašinec, PhD. |
Osnova předmětu
1. General principles, mining-geological and geomechanical conditions of mining, the mechanism of overburden rock failure, and the impact of previous mining.
2. Observation stations for measuring movements in a subsidence basin, stabilization methods, and determination of the distances between observation station points.
3. Scope of mining influence, critical angle, total effective area, magnitude and direction of surface point movement.
4. Methods for preliminary calculation of surface subsidence in horizontally lying deposits, selection of effective and time functions.
5. Computational methods for theoretical calculation of surface subsidence and deformations.
6. Methods for determining vertical movements, the effect of subsidence on the initial elevation point, and determining the interval between repeated measurements.
7. Methods for determining horizontal movements and processing measured results.
8. Determination of the boundary of the affected area based on measured subsidence values.
9. Methods for determining inclination and curvature on the slope of a subsidence basin (direct, indirect, and analytical determination).
10. Determination of relative horizontal deformations; direct and indirect methods.
11. Methods for preliminary calculation of surface subsidence in an inclined deposit.
12. Support pillars, pillar angle, and the construction of support pillars.
13. New trends in the monitoring and prediction of subsidence basins: Use of remote sensing methods (InSAR satellite radar interferometry, airborne and terrestrial 3D laser scanning—ALS/TLS LiDAR, UAV photogrammetry) and applications of artificial intelligence (machine learning and deep learning) in predicting the spatiotemporal evolution of subsidence.
2. Observation stations for measuring movements in a subsidence basin, stabilization methods, and determination of the distances between observation station points.
3. Scope of mining influence, critical angle, total effective area, magnitude and direction of surface point movement.
4. Methods for preliminary calculation of surface subsidence in horizontally lying deposits, selection of effective and time functions.
5. Computational methods for theoretical calculation of surface subsidence and deformations.
6. Methods for determining vertical movements, the effect of subsidence on the initial elevation point, and determining the interval between repeated measurements.
7. Methods for determining horizontal movements and processing measured results.
8. Determination of the boundary of the affected area based on measured subsidence values.
9. Methods for determining inclination and curvature on the slope of a subsidence basin (direct, indirect, and analytical determination).
10. Determination of relative horizontal deformations; direct and indirect methods.
11. Methods for preliminary calculation of surface subsidence in an inclined deposit.
12. Support pillars, pillar angle, and the construction of support pillars.
13. New trends in the monitoring and prediction of subsidence basins: Use of remote sensing methods (InSAR satellite radar interferometry, airborne and terrestrial 3D laser scanning—ALS/TLS LiDAR, UAV photogrammetry) and applications of artificial intelligence (machine learning and deep learning) in predicting the spatiotemporal evolution of subsidence.
Povinná literatura
Doporučená literatura
[1] CHEN, Bingqian, Hao YU, Xiang ZHANG, Zhenhong LI, Jianrong KANG, Yang YU, Jiale YANG and Lu QIN. Time-Varying Surface Deformation Retrieval and Prediction in Closed Mines through Integration of SBAS InSAR Measurements and LSTM Algorithm. Remote Sensing [online]. 2022, 14(3), 788. ISSN 2072-4292. Available at: doi:10.3390/rs14030788
[2] BEHERA, Akash and Kishan SINGH RAWAT. A brief review paper on mining subsidence and its geo-environmental impact. Materials Today: Proceedings [online]. 2023. ISSN 2214-7853. Available at: doi:10.1016/j.matpr.2023.04.183
[3] GŁOWACKI, Tadeusz and Piotr BORTNOWSKI. Long-term prediction of post-mining land deformation with XGBoost, Bayesian optimization, and time series feature engineering. Measurement [online]. 2026, 259, 119630. ISSN 0263-2241. Available at: doi:10.1016/j.measurement.2025.119630
[4] MISA, Rafał. Knothe’s Theory Parameters-Computational Models and Examples of Practical Applications. Gospodarka Surowcami Mineralnymi [online]. 2023, 39. Available at: doi:10.24425/gsm.2023.148164
[2] BEHERA, Akash and Kishan SINGH RAWAT. A brief review paper on mining subsidence and its geo-environmental impact. Materials Today: Proceedings [online]. 2023. ISSN 2214-7853. Available at: doi:10.1016/j.matpr.2023.04.183
[3] GŁOWACKI, Tadeusz and Piotr BORTNOWSKI. Long-term prediction of post-mining land deformation with XGBoost, Bayesian optimization, and time series feature engineering. Measurement [online]. 2026, 259, 119630. ISSN 0263-2241. Available at: doi:10.1016/j.measurement.2025.119630
[4] MISA, Rafał. Knothe’s Theory Parameters-Computational Models and Examples of Practical Applications. Gospodarka Surowcami Mineralnymi [online]. 2023, 39. Available at: doi:10.24425/gsm.2023.148164