Search Results (24)

Accurate forecasting of mining-induced surface deformation is critical for coal-mine safety assessment and hazard mitigation. InSAR deformation time series are often short, temporally sparse, and strongly nonlinear. These characteristics can make purely data-driven predictors unreliable in small-sample settings. To address this issue, we propose a generation–prediction–interpretation framework that combines generative augmentation with physics-informed forecasting. We first develop a TCN-TimeGAN model to synthesize high-fidelity deformation sequences and expand the training set. Recurrent modules in the generator and discriminator are replaced with causal TCN residual blocks, and a temporal self-attention layer is further stacked on top of the TCN backbone to adaptively reweight informative time steps. We then construct a physics-informed Kolmogorov–Arnold Network, termed PI-KAN. Subsidence-consistency and smoothness priors are embedded in the learning objective to promote physically plausible predictions while retaining spline-based interpretability. Experiments on SBAS-InSAR deformation series from the Guqiao coal mine show that the framework achieves an RMSE of 0.825 mm and an R² of 0.968. It outperforms TGAN-KAN, CNN-BiGRU, and BiGRU under the same evaluation protocol. Visualizations of the learned spline-based edge functions further reveal stronger nonlinear responses for lagged inputs closer to the forecast horizon, providing interpretable evidence of short-term temporal sensitivity under sparse observations. Full article

Land subsidence and structural instability at the Slănic Prahova salt mine have evolved significantly over 190 years of underground extraction, particularly following the mine’s expansion in 1970. This study reconstructs the complete geomechanical history from 1835 to 2025 and forecasts deformation trajectories up to 2050 using a calibrated creep-based numerical model. A high-fidelity geological model was developed in Leapfrog Works, with the numerical mesh generated in Rhinoceros and converted to FLAC^3D format via the Griddle plug-in. Salt creep was characterized using a Norton power-law constitutive model, with initial parameters derived from the steady-state phases of laboratory creep tests, and subsequently with calibrated parameters identified at the mine scale as n = 2.03 and A = 3 × 10⁻²⁵ s⁻¹ MPa⁻ⁿ. The simulation results demonstrate a high degree of correlation with field observations. These parameters were subsequently refined at the mine scale by integrating surface leveling data (1994–2025) and underground displacement records (2004–2019). The simulation results demonstrate a high degree of correlation with field observations, highlighting critical deformation zones. Maximum surface subsidence increased from approximately −560 mm in 1970 to −1020 mm by 1992, reflecting the intensified impact of later mining phases. The current maximum cumulative displacement is estimated at −1640 mm (2025) and is projected to reach −2060 mm by 2050. Underground, the largest displacement rates are concentrated in the eastern sector, driven by the synergistic effects of overburden loading and regional horizontal stress. Full article

Mining operations conducted beneath water-bearing strata pose significant risks associated with the development of water-conducting fracture zones in the overburden. The height criterion for this parameter is critical to ensuring the stability of underground mine workings and preventing the risk of water inrush incidents. The research is based on physical and numerical simulations and aims to forecast the development of the water-conducting fracture zone. The methodology is based on in situ hydrogeology data, geotechnical boreholes, physical 2D modeling of rock strata, discrete element modeling using UDEC, and finite–discrete element modeling using Prorock software. A physical model of layered rock mass is constructed to simulate unfilled excavation areas induced deformation under real polymetallic ore field conditions. Based on the results, relationships between vertical subsidence, layer curvature, inclination, and the height of the water-conducting fracture zone were obtained. Particular attention is given to the effects of tectonic discontinuities, chamber geometry, and backfilling on fracture development. A stepwise excavation sequence is simulated to reproduce field conditions and assess the evolution of stress and deformation fields in the overburden. The study reveals that the propagation of the fracture zone around a mine excavation adheres to a polynomial law, characterized by an increase in height concurrent with the expansion of the excavation. This approach enables the design of safe extraction strategies beneath aquifers or surface water bodies. The proposed framework is expected to enhance prediction accuracy and reduce uncertainties. Full article

Ground subsidence in mined-out areas has irreversible impacts on residents’ lives and infrastructure, making its monitoring and prediction crucial for ensuring safety, protecting the ecological environment, and promoting sustainable development. This study employed the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to process Sentinel-1A satellite images of Liaoyuan’s Northern New District from August 2022 to March 2025, deriving ground deformation data. The SBAS-InSAR results were validated using unmanned aerial vehicle (UAV) measurements. Monitoring revealed deformation rates ranging from −26.80 mm/year (subsidence) to 13.12 mm/year (uplift) in the area, with a maximum cumulative subsidence of 59.59 mm observed near the Xi’an Sixth District. Based on spatiotemporal patterns, most mining-induced subsidence in the study area is in its late stage, primarily caused by progressive compaction of fractured rock masses and voids within the collapse and fracture zones. Using subsidence data from August 2022 to March 2024, three prediction models—LSTM, GRU, and TCN-GRU—were trained and subsequently applied to forecast subsidence from March 2024 to August 2025. Comparisons between the predictions and SBAS-InSAR measurements showed that all models achieved high accuracy. Among them, the TCN-GRU model yielded predictions closest to the actual values, with a correlation coefficient exceeding 0.95, validating its potential for application in time-series settlement monitoring. Full article

Mine-induced surface subsidence threatens infrastructure and can trigger cascading geohazards, so accurate and computationally efficient monitoring and forecasting are essential for early warning. We integrate Persistent Scatterer InSAR (PS-InSAR) time series with a Bayesian-optimized CNN–LSTM designed for spatiotemporal prediction. The CNN extracts spatial deformation patterns, the LSTM models temporal dependence, and Bayesian optimization selects the architecture, training hyperparameters, and the most informative exogenous drivers. Groundwater level and backfilling intensity are encoded as multichannel inputs. Endpoint anchoring with affine calibration aligns the historical series and the forward projections. PS-InSAR indicates a maximum subsidence rate of 85.6 mm yr⁻¹, and the estimates are corroborated against nearby leveling benchmarks and FLAC^3D simulations. Cross-site comparisons show acceleration followed by deceleration after backfilling and groundwater recovery, which is consistent with geological engineering conditions. A symmetry-aware preprocessing step exploits axial regularities of the deformation field through mirroring augmentation and documents symmetry-breaking hotspots linked to geological heterogeneity. These choices improve generalization to shifted and oscillatory patterns in both the spatial CNN and the temporal LSTM branches. Short-term forecasts from the BO–CNN–LSTM indicate subsequent stabilization with localized rebound, highlighting its practical value for operational planning and risk mitigation. The framework combines automated hyperparameter search with physically consistent objectives, reduces manual tuning, enhances reproducibility and generalizability, and provides a transferable quantitative workflow for forecasting mine-induced deformation in complex goaf systems. Full article

With an abundance of data provided by satellite-based measurements, such as Synthetic Aperture Radar Interferometry (InSAR) or the Global Navigation Satellite System (GNSS), an interest has grown in training highly complex data-driven models for geophysical applications, including displacement modeling. These methods, including machine learning (ML) and deep learning (DL) algorithms, represent a new approach to forecasting ground surface displacements. Yet, the effectiveness of such methods, including their generalization capabilities and performance on non-linear data, remains underexplored. This paper examines the performance of various data-driven algorithms, including regression models and deep neural networks, in predicting mining-induced subsidence. Ground surface displacement data obtained from the Small Baseline Subset (SBAS) InSAR were used as time series samples for training and validation. ML and DL models were evaluated over varying forecast horizons. The results show that data-driven approaches can effectively model InSAR-derived ground subsidence in mining areas. Deep learning models outperform other ML-based models, indicating that increased model complexity can lead to better forecasting accuracy. Nevertheless, it is shown that careful examination of performance metrics and forecast errors in the spatial domain is essential for appropriate model evaluation. The findings demonstrate that combining SBAS-InSAR measurements with data-driven modeling offers a promising direction for developing automated systems for monitoring and forecasting mining-induced ground deformation. Full article

Mining subsidence is a pervasive geohazard in coal basins, and precise and reliable deformation monitoring is essential to effective risk mitigation. Conventional time-series Interferometric Synthetic Aperture Radar (InSAR) suffers from vegetation-induced decorrelation and atmospheric delays. Most predictive models leverage only temporal information. We introduced an integrated DS InSAR + CNN LSTM framework for subsidence monitoring and forecasting. Forty-three Sentinel-1A scenes (2017–2018), corrected with Generic Atmospheric Correction Online Service for InSAR (GACOS) data, were processed to derive cumulative deformation, cross-validated against multi-view SBAS InSAR, and used to train a CNN LSTM network that predicts trends one year in advance. The findings indicate that (1) DS InSAR provides 2.83 times the monitoring density of SBAS InSAR, with deformation rate R² = 0.83, RMSE = 0.0028 m/a, and MAE = 0.0019 m/a at common pixels. The RMS average decrease in GACOS atmospheric delay phase correction is 2.52 mm. (2) High- and low-settlement zones comprise 0.11% and 92.32% of the area, respectively; maximum velocity reaches 190.61 mm/a, with a cumulative subsidence of −338.33 mm. (3) Across the five zones with the most severe subsidence, the CNN–LSTM model attains R² values of 0.97–0.99 and RMSE below 1 mm, markedly outperforming the standalone LSTM network. (4) Deformation correlated strongly with geological structures, groundwater decline (R² = 0.66–0.78), and precipitation (slope > 0.33), highlighting coupled natural and anthropogenic control. Full article

Surface subsidence induced by coal mining poses severe threats to global ecosystems and infrastructure. A critical challenge in subsidence prediction lies in the sensitivity of existing probabilistic integral parameter inversion methods to gross errors, leading to unstable predictions and compromised reliability. To address this limitation, we propose the IGGIII-BFGS algorithm that integrates robust estimation with unconstrained optimization method, enhancing resistance to gross errors during parameter inversion. Through systematic comparison of four robust estimation methods (Huber, L1, Geman–McClure, IGGIII) fused with BFGS, the IGGIII-BFGS method demonstrated superior stability and accuracy, reducing relative errors in key parameters (subsidence factor q, horizontal displacement coefficient b, and tangent of major influence angle tan β) to near-zero levels. Validation on the Huainan mining case study showed that the IGGIII-BFGS method achieved a 25.8% reduction in subsidence RMSE compared to standard BFGS, with predicted curves exhibiting strong agreement with field measurements. This advancement enables precise forecasting of subsidence and horizontal displacement, which hold significant value for the sustainable development of the surface ecological environment and social stability. Full article

Underground coal mining in expansive basins has led to land subsidence, posing significant threats to the aboveground structures and the environment. Addressing this issue is paramount, necessitating accurate and reliable prediction methods. This study introduces a novel approach to forecast subsidence and deformations along Highway E75, Belgrade–Niš, situated in the influence zone of the Morava Pit at the Aleksinac mine. The prognostic calculation was conducted using the MITSOUKO software package rooted in the stochastic Patric-Stojanović method, and based on the obtained results, a spatial analysis within the geographic information system (GIS) was performed. Additionally, the sustainability of the highway segment affected by mining activities was evaluated. The data from geodetic measurements indicate that the developed model demonstrates exceptional proficiency in simulating mine subsidence and deformation processes. Precise forecasts of subsidence and deformation, along with accurate risk assessments, are essential prerequisites for developing and implementing effective measures to mitigate the impacts of mine-induced subsidence on highways. This investigation could play a vital role in the feasibility assessment of reopening the Aleksinac mine, potentially presenting lucrative and environmentally sound solutions amidst current energy challenges. Full article

With economic development and coal resource exploitation, the area of mined-out zones is expanding continuously. The traditional waste disposal methods no longer meet the current demands, making it urgent to evaluate and reuse the surface stability of these mined-out zones. Surface residual deformation is a process where voids and fissures within the mined-out zones are gradually filled and compacted, affecting the overlying rock structure. Additionally, groundwater significantly impacts the strength of the overlying rock, leading to increased subsidence. Therefore, predicting surface residual deformation while considering the effects of groundwater is crucial for forecasting surface deformation and assessing stability in mined-out zones. This study, taking into account the characteristics of subsidence zones and the impact of groundwater on the compaction of fractured rock masses, uses equivalent mining height and probability integral methods to develop a predictive model for surface residual deformation incorporating groundwater effects. Predictions for the study area show that groundwater exacerbates surface residual deformation, with various deformation values ranging from 33.8% to 51.9%. The surface stability categories are divided into stable and essentially stable regions based on the residual deformation’s impact on the working face. This model fully considers the influence of groundwater on residual deformation in mined-out zones, refining existing mining subsidence theories, addressing deformation issues caused by adverse groundwater factors, and providing a theoretical basis for predicting residual deformation and evaluating stability in mined-out zones, promoting the sustainable development of land and environmental resources in mining areas. Full article

Surface subsidence resulting from block caving mining causes considerable environmental and economic harm in mining areas, highlighting the critical need for accurate predictions of surface subsidence. Given the unique features of the block caving technique and the resemblance between the released ore pillars and the mining processes, this paper developed a lightweight model to forecast surface settlement utilizing the probability integration approach to address the issue of predicting surface settlement in metallic mines. This study focuses on the Pulang Copper Mine, situated in the northeast of Shangri-La County within the Yunnan Province, as a case example. This mine employs the block caving method, which results in substantial surface subsidence. A visual mining simulation program is designed to combine the ore mining plan with the prediction model, manage the ore output of each mining point in batches, treat the ore pillars released in the planning cycle as strip work, and simulate and calculate the surface area above the ore pillars settlement value. The calculated values of surface subsidence induced by ore drawing are then interpreted as the downward displacement of the surface subsidence beneath the strip workings. Furthermore, to verify the reliability of the model, three-dimensional laser point cloud data of the Pulang Copper Mine in recent years were collected, and the differences between the predicted surface and the measured surface were calculated and analyzed. Full article

Grouting technology in overburden separation is recognized as an effective method to prevent surface subsidence and reuse solid waste. This study used mechanical analysis to explore deflection characteristics of key strata and accurately predict and control surface subsidence. Conceptualizing the coal–rock mass beneath the key strata as an elastic foundation, we developed a method to calculate the elastic foundation coefficients for various regions and established an equation for key strata deflection, validated through discrete element numerical simulations. This simulation also examined subsidence behavior under different grout injection–extraction ratios. Additionally, combining the equivalent mining height theory with the probability integral method, we formulated a predictive model for surface subsidence during grouting. Applied to the 8006 working face of the Wuyang Coal Mine, this model was supported by numerical simulations and field data, which showed a maximum surface subsidence of 546 mm at a 33% injection–extraction ratio, closely matching the theoretical value of 557 mm and demonstrating a nominal error of 2%. Post-grouting, the surface tilt was reduced to below 3 mm/m, meeting regulatory standards and eliminating the need for ongoing surface structure maintenance. These results confirm the model’s effectiveness in forecasting and controlling surface subsidence with grouting. The study can provide a basis for determining the grouting injection–extraction ratios and evaluating the effectiveness of surface subsidence control in grouting into overburden separation projects. Full article

Surface subsidence hazards in mining areas are common geological disasters involving issues such as vegetation degradation and ground collapse during the mining process, which also raise safety concerns. To address the accuracy issues of traditional prediction models and study methods for predicting subsidence in open-pit mining areas, this study first employed 91 scenes of Sentinel-1A ascending and descending orbits images to monitor long-term deformations of a phosphate mine in Anning City, Yunnan Province, southwestern China. It obtained annual average subsidence rates and cumulative surface deformation values for the study area. Subsequently, a two-dimensional deformation decomposition was conducted using a time-series registration interpolation method to determine the distribution of vertical and east–west deformations. Finally, three prediction models were employed: Back Propagation Neural Network (BPNN), BPNN optimized by Genetic Algorithm (GA-BP), and BPNN optimized by Artificial Bee Colony Algorithm (ABC-BP). These models were used to forecast six selected time series points. The results indicate that the BPNN model had Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) within 7.6 mm, while the GA-BP model errors were within 3.5 mm, and the ABC-BP model errors were within 3.7 mm. Both optimized models demonstrated significantly improved accuracy and good predictive capabilities. Full article

To attain precise forecasts of surface displacements and deformations in goaf areas (a void or cavity that remains underground after the extraction of mineral resources) following coal extraction, this study based on the limitations of individual time function models, conducted a thorough analysis of how the parameters of the model impact subsidence curves. Parameter estimation was conducted using the trust-region reflective algorithm (TRF), and the time function models were identified. Then we utilized a combined model approach and introduced the sliding window mechanism to assign variable weights to the model. Based on this, the combined model was used for prediction, followed by the application of this composite prediction to engineering scenarios for the dynamic forecasting of surface movements and deformations. The results indicated that, in comparison with DE, GA, PSO algorithms, the TRF exhibited superior stability and convergence. The parameter models obtained using this method demonstrated a higher level of predictive accuracy. Moreover, the predictive precision of the variable-weight time function combined model surpassed that of corresponding individual time function models. When employing six different variable-weight combination prediction models for point C22, the Weibull-MMF model demonstrated the most favorable fitting performance, featuring a root mean square error (RMSE) of 32.98 mm, a mean absolute error (MAE) of 25.66 mm, a mean absolute percentage error (MAPE) of 7.67%; the correlation coefficient R² reached 0.99937. These metrics consistently outperformed their respective individual time function models. Additionally, in the validation process of the combined model at point C16, the residuals were notably smaller than those of individual models. This reaffirmed the accuracy and reliability of the proposed variable-weight combined model. Given that the variable-weight combination model was an evolution from individual time function models, its applicability extends to a broader range, offering valuable guidance for the dynamic prediction of surface movement and deformation in mining areas. Full article

Numerical investigation facilitates the development and exploitation phase of a coal mine, incorporating geological settings by forecasting the overall stability. This study proposes a generalized numerical simulation calibration approach to predict potential geotechnical hazards in an explored coal mine, focusing on the Khalashpir coal basin in Bangladesh. This research investigates the feasibility of initiating mining at the central block, which is associated with major faults by the finite element method (FEM), which is a valuable tool for understanding the variations of stress distribution in the rock mass. The study verifies the findings of the FEM by further assessing the seam convergence, vertical stress, and strain safety factor using the boundary element method (BEM), which involves numerical discretization in a reduced spatial dimension. The results illustrate that there will be significant displacements in the formation, which infer subsidence and increase vastly along the fault lines. This numerical investigation approach provides essential insights for future research concerning newly explored coal mines, particularly ones in the Gondwana basin. Full article