Rock Mechanics
Seyed Mahdi Moosavi; Hamid Aghajani; Reza Ghaedrahmati; Amir Aghabalaei
Abstract
Land subsidence is one of the most significant geomorphological challenges in arid and semi-arid regions, posing substantial risks to infrastructure, groundwater resources, and the environment. In this study, Sentinel-1 SAR data and the LiCSBAS-based SBAS-InSAR time-series approach were employed to investigate ...
Read More
Land subsidence is one of the most significant geomorphological challenges in arid and semi-arid regions, posing substantial risks to infrastructure, groundwater resources, and the environment. In this study, Sentinel-1 SAR data and the LiCSBAS-based SBAS-InSAR time-series approach were employed to investigate ground deformation across the Silakhor Plain in Lorestan Province, Iran, during the 2018–2024 period. A total of 299 Sentinel-1 SAR images acquired at 6-day temporal intervals were processed to characterize the spatial and temporal patterns of land subsidence. The results clearly identified the major subsidence zones and quantified their deformation rates across the study area. The findings indicate an average annual subsidence rate of approximately 40 mm yr⁻¹, with cumulative subsidence reaching up to 340 mm in some parts of the plain during the study period. The highest deformation rates are predominantly concentrated within agricultural areas and the alluvial aquifer of the Silakhor Plain, suggesting a potential relationship between groundwater depletion and land subsidence. Furthermore, the spatial distribution of subsidence indicates that critical infrastructure, including the Khorramabad–Borujerd highway, as well as several rural settlements, may be exposed to deformation-related hazards.
Rock Mechanics
P GANESAN; Ritesh D Lokhande; Siddhartha Roy; Hemant Agrawal
Abstract
Subsidence associated with underground coal mining is a significant geotechnical concern in many coal-producing regions. The extraction of coal over large areas from underground often leads to the collapse of overlying strata into the goaf, subsequently causing surface subsidence. The extent of this ...
Read More
Subsidence associated with underground coal mining is a significant geotechnical concern in many coal-producing regions. The extraction of coal over large areas from underground often leads to the collapse of overlying strata into the goaf, subsequently causing surface subsidence. The extent of this subsidence varies widely across mines, depending on several factors, including mine geometry, geological discontinuities, physico-mechanical properties of the overlying strata, extraction method, seam thickness, and depth of working. Among these, the angle of draw (AoD) plays a critical role in delineating the subsidence-affected zone, particularly in underground coal mining. Accurate prediction of AoD is essential for safe mine planning and the mitigation of subsidence-related hazards. In the present study, a comprehensive field investigation was conducted to collect mine operational parameters from various underground coal mines. Using this dataset, Genetic Programming (GP) was employed to model the relationship between AoD and key mining and geological parameters. The developed GP model demonstrated a strong correlation between predicted and measured AoD values, with a coefficient of determination (R) = 0.7921, highlighting the model’s predictive capability. Additionally, a sensitivity analysis (SA) was performed to identify the most influential input parameters affecting AoD. The analysis indicated that, while all five input variables significantly impact AoD, the compressive strength of overlying strata exhibited the highest influence (sensitivity score = 0.98). The findings of this study provide a data-driven approach to predict the angle of draw in underground coal mines, offering valuable insights for improved mine design, extraction strategies, and surface infrastructure protection.
