GIS Analysis for Mining-Induced Land Subsidence: Improving Infrastructure Resilience

By Dr Silpaja Chandrasekar, PhDReviewed by Laura ThomsonMar 5 2025

A recent article in Applied Sciences highlighted the impact of underground coal mining on land subsidence, which threatens infrastructure like Highway E75 in Serbia. Using the MITSOUKO software and the stochastic Patarić–Stojanović method, the study accurately predicted subsidence and deformations, validated by geodetic measurements. Geographic information system (GIS)- based spatial analysis assessed the highway’s sustainability, aiding risk mitigation strategies. The findings support feasibility assessments for reopening the Aleksinac mine amid energy challenges.

Related Work

Previous research on mine-induced subsidence has explored various prediction methods, including profile function, influence function, and empirical, physical, and numerical modeling. Studies from Germany, France, England, Poland, China, and the USA have analyzed subsidence impacts on infrastructure, particularly highways and railways. Serbian research, initiated in the 1980s, developed the Patarić–Stojanović stochastic method, later enhanced with GIS integration for spatial analysis. Long-term geodetic measurements validated its accuracy, aiding risk assessment and mitigation strategies for mining-induced subsidence.

Stochastic Subsidence Modeling

The stochastic Patarić–Stojanović method provides a mathematical model for predicting subsidence and deformations based on mathematical statistics. It builds on Pokrovsky and Litwiniszyn's assumptions that rock masses are layered and fragmented, leading to stochastic movements.

While nature does not strictly adhere to this assumption, it holds statistically since pressure variations in a homogeneous medium follow a symmetrical distribution. It allows the method to model force distribution using a binary law approximating a normal distribution. Planar subsidence is analyzed using empirical patterns, incorporating coefficients linked to geometric displacement characteristics. The model primarily applies to surface observations, aligning with long-term mining studies. However, it does not account for real-time displacement changes due to limited validation data in existing literature.

Subsidence, or vertical displacement, is measured as the elevation change of a benchmark, determined by periodic leveling. It is calculated using the difference between initial and final elevations, and its maximum values indicate the extent of subsidence over time. The shape and size of the subsidence trough depend on geological and mining conditions, making theoretical models essential for predicting displacement patterns.

The stochastic method assumes a fragmented rock mass where element movements follow a probability function. The mathematical formulation considers seam depth, dip angle, and excavation dimensions, leading to equations that define subsidence profiles. The model extends to inclined seams, but asymmetry in displacement introduces anisotropy, requiring additional adjustments in calculations.

Surface slope, or tilt, represents the gradient of vertical displacement and is calculated as the difference in subsidence between two benchmarks divided by their horizontal distance. It is typically measured in millimeters per meter, with maximum slope values occurring at inflection points of the subsidence trough.

The slope gradually decreases toward the center and edges of the trough, reaching zero. This factor is crucial for assessing structural impacts on buildings, roads, and other infrastructure. Uneven slopes can cause long-term issues such as misalignment of doors and drainage problems. Understanding surface slopes helps predict potential damages caused by underground excavation, aiding in infrastructure protection and planning.

If the subsidence curve equation is known, the slope at any given point can be determined by considering multiple vertical planes through that point. The main slope is identified in the plane where it reaches its maximum value. Using mathematical derivations, the slope direction at a point M is defined by an angle that determines the subsidence contour. The extreme slope value corresponds to a specific plane orientation, highlighting the need for precise calculations in subsidence studies. This theoretical framework allows for accurate prediction of surface deformations, supporting better decision-making in mining and construction projects.

Aleksinac Mining Risks

The Aleksinac coal and oil shale deposit, located between the South Morava and Moravica rivers, spans 30 km in length and 10 km in width. Mining, active since 1878, faced challenges due to complex tectonics, methane hazards, and high self-ignition coal. The Morava Pit, divided into Northern and Southern Districts, saw frequent accidents, including a 1989 disaster that halted operations. A study using the Patarić–Stojanović stochastic method and MITSOUKO software assessed past subsidence and potential reactivation impacts, particularly on the E75 highway. Despite rich reserves, mining remains uncertain due to past failures and safety concerns.

The MITSOUKO program automates subsidence and deformation calculations using the Patarić–Stojanović method and integrates with ArcGIS for visualization. It refines predictions by adjusting input parameters when applied to the Rembas coal mine. Modeling at the Morava Pit shows increasing subsidence affecting the E75 highway. While slope values remain within limits, curvature deviations may impact road safety, requiring further assessments.

Conclusion

This study integrated the stochastic Patarić–Stojanović model with MITSOUKO software and GIS analysis to assess subsidence risks at the Aleksinac coal mine and its impact on the E75 highway. The findings confirmed the model's reliability for predicting mining-induced deformations and emphasized the need for continuous monitoring and adaptive engineering solutions. The research supports infrastructure protection by improving risk assessment for policymakers and mining feasibility evaluations. Future work should refine model parameters with additional field data to enhance subsidence prediction accuracy.

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