The permeability of deep limestone strata in North China-type coalfields exhibits strong vertical heterogeneity, which complicates the accurate assessment of mining-induced water inrush risk using traditional vulnerability index methods. Conventional approaches typically rely on averaging and planarizing the permeability characteristics of aquifers, overlooking their strong vertical heterogeneity. This can propagate and amplify errors. To address this issue, this paper proposes a vulnerability index tomography method that accounts for vertical permeability heterogeneity. The method involves subdividing the strata into multiple layers, and calculating vulnerability indices for each layer separately, thus replacing the two-dimensional generalized description of aquifer properties and mitigating the interference of local information on overall accuracy. Layer-by-layer water inrush risk zonation allows precise identification of high-risk areas in both horizontal and vertical dimensions. Grout is then injected only in the high-risk areas, overcoming the overly conservative water control strategy of full-floor grout injection, and greatly reducing unnecessary costs. A case study conducted at the Dengcao coal mine revealed marked differences in vulnerability among different layers, highlighting how the strong vertical heterogeneity cannot be ignored. The combination of the sensitivity analysis results and the results from geophysical prospecting further validated the accuracy of the proposed method. This paper provides a theoretical foundation and practical reference for assessing the water inrush risk of rock strata with strong vertical permeability heterogeneity, such as the deep limestone in North China-type coalfields, and offers insights for formulating more effective water prevention measures.
Water scarcity has prompted the development of underground reservoirs in coal mines, in which coal pillar dams ensure long-term stability and hydraulic isolation. However, the coupled hydro-mechanical behavior of coal pillar dams under complex stress paths and varying pore water pressures remains poorly understood. This study investigated the permeability evolution of coal pillar dams under different stress paths and pore water pressures. Two representative mining-induced stress paths were investigated: a high-deviatoric-stress path and a high-confining-pressure path. Although the peak strengths were nearly identical, the permeability evolution behaviors differed greatly. The high-deviatoric-stress path induced brittle failure accompanied by a rapid post-peak permeability ratio surge, while the high-confining-pressure path led to ductile failure with a delayed increase in permeability ratio. Pore water pressure played a dual role, reducing coal strength while simultaneously accelerating early-stage damage and suppressing ultimate instability. A novel dynamic porosity-stress sensitivity (DPSS) model was developed, integrating fracture deformation, damage mechanics, and pore pressure effects. The model successfully captured progressive permeability evolution under moderate pressures and non-monotonic loading, although it was less accurate for cases involving abrupt end-stage permeability jumps. These findings provide critical insights for reservoir pore water pressure and establishing permeability-based early-warning indicators, thereby enhancing the safety and sustainability of coal mine underground reservoirs.
The phosphate deposits in northwestern Hubei are characterized by thick ore bodies, gentle dips, and water-rich overlying karstic dolomite aquifers. Mining-induced rock-mass damage can connect confined aquifers with excavation spaces, increasing the risk of mine water inflow. Taking the ZYG phosphate deposit, which is currently at the geological exploration and mine-design stage, as a case study, this study established a coupled stress–damage–seepage (CSDS) model to investigate the evolution of water-conducting damage zones and groundwater under the proposed underground strip backfill mining. The results indicate that damage will initially be localized near mined-out strip roofs and pillar boundaries, and will then propagate upward and coalesce during Step II pillar recovery. The controlling mechanism will shift from mining-induced stress domination to coupled mining–seepage control. After fracture connection, groundwater will preferentially migrate through highly damaged zones, promoting crack opening and water-conducting channel formation. Coupled anomalies in shear stress, permeability, pore pressure, and flow velocity identified the areas near 100 m and 260 m along the monitoring line as the main channel-development zones. As the prescribed hydraulic pressure of the overlying confined aquifer increased from 3.0 MPa to 6.0 MPa, the calculated local pore pressure within the strata increased from ≈ 1.1–1.8 MPa to 2.2–3.5 MPa, and the final water inflow increased by ≈ 42
This study has developed an in situ sulfate removal technology that uses nitrogen injection to establish a reducing environment and stimulate the metabolic activity of sulfate-reducing bacteria (SRB) and validated it in the laboratory. A three-dimensional physical model experiment of a coal mine goaf demonstrated that nitrogen injection effectively reduced dissolved oxygen concentrations and oxidation–reduction potential in the mine water, thereby promoting SRB enrichment and metabolic activity. Hydrochemical evidence demonstrated that nitrogen injection established a reducing environment favorable for SRB growth, resulting in sulfate concentrations decreasing by up to 24
With the increasing intensity of deep coal mining in China, the risk of groundwater inrush due to complex hydrogeological conditions is becoming increasingly prominent. This study proposes a groundwater inrush risk assessment framework that integrates multi-source information such as hydrogeological information, geological structure, aquitard characteristics, and karst factors. The framework couples error indices with classification performance indices and performs statistical validation on spatial risk zoning. The performance of ensemble machine learning models (including Random Forest (RF) and RF-K, Support Vector Machine (SVM) and SVM-K, and Backpropagation Neural Network (BPNN) and BPNN-K) are compared. The results show that the RF-K model, which integrates karst factors, exhibits the best predictive ability (AUC 0.98, overall accuracy OA 0.95, F1 score 0.96). The ranking of the main controlling factors indicates that aquifer water pressure (117.1
