New Subsidence Prediction Method Incorporating Asymmetry and Shape Flexibility: A Study Case of Salt Caverns in North Germany.
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| Title: | New Subsidence Prediction Method Incorporating Asymmetry and Shape Flexibility: A Study Case of Salt Caverns in North Germany. |
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| Authors: | Babaryka, Aleksandra1 (AUTHOR) Aleksandra.Babaryka@unileoben.ac.at, Benndorf, Jörg2 (AUTHOR) Joerg.Benndorf@mabb.tu-freiberg.de |
| Source: | Rock Mechanics & Rock Engineering. Aug2025, Vol. 58 Issue 8, p8737-8751. 15p. |
| Subjects: | Symmetry, Salt mining, Quantitative research, Risk assessment, Empirical research |
| Geographic Terms: | Northern Germany |
| Abstract: | Subsidence prediction serves as a crucial risk analysis and management tool in mining regions. To mitigate risks associated with ground subsidence, understanding not only the magnitude but also the relative position of mining-induced ground movements and deformations is essential. The source of subsidence stems from the convergence of an underground void due to the overlaying rock mass and related pressure. This is transferred to the surface, resulting in a typical subsidence trough. In empirical functional prediction methods, this shape is modeled by an influence function. Classical subsidence prediction offers an efficient solution for symmetrical shapes and Gaussian-distributed subsidence; however, real-world observations reveal asymmetrical and uniquely shaped patterns. Various mathematical approaches have been implemented to account for these patterns to improve subsidence prediction. Nonetheless, they possess significant disadvantages such as complexity, non-interpretability of parameters, and an inability to accommodate other patterns. This article introduces a novel solution for subsidence prediction, addressing both asymmetry and shape deviations concurrently or independently, while retaining compatibility with classical solutions. To evaluate the prediction method, the best estimated parameters are applied across different scenarios, including a full case study of subsidence above energy storage salt caverns in the Middle European region. The application of the new solution significantly improves the subsidence prediction accuracy, with up to a 25% reduction in mean square error compared to the classical subsidence prediction method and up to a 12% improvement over individual pattern approaches. Highlights: Development of a unifying subsidence prediction model that incorporates best practice approaches, enhancing the efficiency and accuracy of empirical models. Analyzes the impact of various subsidence patterns on model performance through a real full case study. Develops an analytical solution for an integral in the shape adaptation model, extensively used in the EU for the last decade. Evaluates different models' performance using a range of statistical tools. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Subsidence prediction serves as a crucial risk analysis and management tool in mining regions. To mitigate risks associated with ground subsidence, understanding not only the magnitude but also the relative position of mining-induced ground movements and deformations is essential. The source of subsidence stems from the convergence of an underground void due to the overlaying rock mass and related pressure. This is transferred to the surface, resulting in a typical subsidence trough. In empirical functional prediction methods, this shape is modeled by an influence function. Classical subsidence prediction offers an efficient solution for symmetrical shapes and Gaussian-distributed subsidence; however, real-world observations reveal asymmetrical and uniquely shaped patterns. Various mathematical approaches have been implemented to account for these patterns to improve subsidence prediction. Nonetheless, they possess significant disadvantages such as complexity, non-interpretability of parameters, and an inability to accommodate other patterns. This article introduces a novel solution for subsidence prediction, addressing both asymmetry and shape deviations concurrently or independently, while retaining compatibility with classical solutions. To evaluate the prediction method, the best estimated parameters are applied across different scenarios, including a full case study of subsidence above energy storage salt caverns in the Middle European region. The application of the new solution significantly improves the subsidence prediction accuracy, with up to a 25% reduction in mean square error compared to the classical subsidence prediction method and up to a 12% improvement over individual pattern approaches. Highlights: Development of a unifying subsidence prediction model that incorporates best practice approaches, enhancing the efficiency and accuracy of empirical models. Analyzes the impact of various subsidence patterns on model performance through a real full case study. Develops an analytical solution for an integral in the shape adaptation model, extensively used in the EU for the last decade. Evaluates different models' performance using a range of statistical tools. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 07232632 |
| DOI: | 10.1007/s00603-025-04444-5 |
