Search Results (1,054)

Accurate registration of multi-temporal LiDAR point clouds is essential for reliable monitoring of mining subsidence. Systematic errors in point clouds acquired at different times can arise from GNSS/INS positioning drift, sensor calibration bias, and differences in observation geometry. These errors typically manifest as global reference shifts or gradual distortions. When such errors are superimposed on real terrain changes, they can mask subsidence signals and introduce observational pseudo-differences, thereby increasing the difficulty of separating actual subsidence from artifacts. To address this issue, this study proposes Robust-Registration-Based Systematic Error Correction for Time-Series Point Clouds (RR-SEC), which establishes a consistent reference framework across epochs. The method does not assume that stable areas remain strictly unchanged. Instead, it identifies regions whose local change patterns are more temporally consistent using an information entropy analysis of multi-temporal differences. Under complex terrain, the method selects points with lower difference entropy as stable control points and uses them to constrain the registration process. It then performs Generalized Iterative Closest Point (GICP) rigid registration under these constraints to estimate the overall three-dimensional translation and rotation between point clouds from different periods. The estimated transformation is applied to the entire point cloud to correct inter-epoch reference mismatches and unify the coordinate reference across all epochs. Comprehensive validation using simulated complex terrain data containing rigid reference biases and non-rigid deformations, as well as UAV LiDAR data collected from the MuduChaideng Coal Mine, shows that, compared with the baseline GICP method, RR-SEC reduces alignment errors. It decreases the mean residual in stable areas by approximately 85%. The subsidence values computed from the corrected point clouds are more consistent with measured values, and the spatial deformation patterns are easier to interpret. RR-SEC demonstrates robust performance and can serve as a practical approach to improve the accuracy of deformation monitoring in mining areas and potentially other geoscientific applications. Full article

To address the engineering challenges of frequent flexural deformation and instability of composite roadway roofs and the difficulty in accurately controlling the support strength range during deep coal mining, this study takes the soft–hard interbedded composite roof of the working face in the West No. 1 Mining Area of Shuangyang Coal Mine in Shuangyashan as the engineering background. Typical fine sandstone (hard rock) and tuff (soft rock) from the on-site roof were selected to prepare layered composite specimens, and indoor four-point bending tests were conducted. Combined with theoretical calculations, strain monitoring, and acoustic emission (AE) real-time localization technology, the regulatory mechanisms of three key factors—lithological combination, loading rate, and span—on the flexural mechanical properties, deformation and failure modes, and fracture evolution laws of layered composite rock masses were systematically investigated. The research results show the following: (1) The flexural performance of layered composite rock masses is dominated by the interlayer interface effect. Their flexural strength is 46.7% and 41.1% lower than that of single hard rock and soft rock specimens, respectively, and the competitive mechanism between interface slip and delamination fracture is the core inducement of strength deterioration. (2) The strength and deformation characteristics of layered composite rock masses exhibit a significant loading rate effect. When the loading rate increases from 0.002 mm/s to 0.02 mm/s, the flexural strength decreases by 51.8% and the mid-span deformation deflection reduces by 50.1%. High loading rates will exacerbate the deformation mismatch between soft and hard rock layers, trigger premature failure of interface bonding, and inhibit the full development of structural plastic deformation. (3) Increasing the span significantly optimizes the flexural bearing performance of layered composite rock masses. When the span increases from 170 mm to 190 mm, the flexural strength increases by 65.7% and the mid-span deformation deflection synchronously increases by 65.7%. A large span can extend the flexural deformation path, promote the coordinated deformation of rock layers, and suppress local stress concentration. (4) The flexural failure of layered composite rock masses is dominated by Mode II shear cracks, while single-lithology specimens are mainly dominated by Mode I tensile cracks. Loading rate and span significantly change the crack propagation mode and energy release law. This study establishes a calculation method for the equivalent flexural stiffness of layered composite rock masses and reveals the mesoscopic mechanism of flexural failure of heterogeneous layered rock masses. The research results can provide a theoretical basis and experimental support for the optimization of support schemes and the prevention and control of roof collapse hazards for composite roofs of deep coal mine roadways. Full article

Land subsidence (LS) is a major global geo-environmental issue that profoundly affects the suitability and safety of land use planning (LUP). However, existing LUP systems generally neglect the dynamic evolution of LS and lack a systematic framework for assessing conflicts between land use and subsidence. To address this gap, this study develops an integrated evaluation framework that combines SBAS-InSAR, GeoDetector, and a spatial conflict detection model. A total of 166 Sentinel-1A images acquired from 2017 to 2024 were processed using SBAS-InSAR to derive the spatiotemporal characteristics of LS. GeoDetector was subsequently applied to identify the dominant driving factors and their interactions. A sensitivity classification scheme for current land use (CLU) and LUP types with respect to LS hazards was then developed, and a spatial conflict detection model was constructed to delineate conflict zones and quantify conflict intensity. Using Huainan City as a case study, the results show the following: (1) from 2017 to 2024, LS was generally characterized by slight or negligible subsidence, with severe subsidence mainly concentrated in coal mining areas; ongoing and recently suspended mines exhibited pronounced LS, whereas early-closed and unmined areas showed an overall uplift trend. (2) LS in Huainan was primarily driven by soil type, annual rainfall, and mining activities, and two-factor interactions generally exhibited enhancement effects. (3) Compared with CLU, LUP has, to some extent, incorporated LS risk considerations and implemented corresponding mitigation measures, although certain areas still insufficiently account for LS risks. (4) The proposed framework demonstrates strong rationality and applicability in LS monitoring, driving factor identification, and spatial conflict assessment, providing scientific support for LS risk management and land use spatial optimization. Full article

Honey is consumed worldwide for its nutritional and medicinal value, but it can also expose people to toxic metals from environmental contamination. This study analyzes heavy metal levels and assesses health risks using inductively coupled plasma optical emission spectrometry (ICP-OES) in honey collected from three areas in the Kellem Wollega Zone, Ethiopia: Dambi Dollo, Gawo Kebe, and Anafilo. The concentrations followed the order: Zn > Fe > Pb > Mn > Cu > Ni > Cd. Notably, Pb and Cd levels exceeded the WHO/FAO maximum permissible limits. The assessment of non-carcinogenic health risks for adult consumers based on the average daily dose, target hazard quotient, and hazard index indicated that all calculated values were below the critical threshold of 1. This result suggests that honey consumption poses no significant non-carcinogenic risk. In contrast, the estimated target cancer risk and cumulative cancer risk (∑TCR) exceeded safety thresholds, indicating potential moderate lifetime carcinogenic risk from chronic exposure. Likely sources of high metal levels include local mining activities, agricultural inputs, and improper honey storage. Consequently, these findings highlight the need for continuous environmental monitoring, stricter regulations, and improved apicultural practices to ensure honey safety and protect public health. Full article

To address the potential threat of surface subsidence caused by coal mining to the safe operation of buried gas pipelines in goaf collapse areas, this study investigates the geological conditions of the Mugu Coal Mine in Shanxi Province, China, and a gas pipeline passing through its surface mining area. Using a combination of numerical simulations and physical analog modeling, the mechanical response and deformation characteristics of the pipeline under mining-induced influences were systematically analyzed from three perspectives: the failure mechanisms of surface soil, the pipe–soil interaction behavior, and the damage evolution of the pipeline within the goaf. The research reveals a separation-induced failure pattern of the gas pipeline in mining-affected areas, referring to the mechanism in which differential settlement causes pipe–soil detachment, leading to unsupported bending deformation and stress concentration. Results show that the subsidence basin expands rapidly when the working face advances between 150 m and 210 m. Before this stage, the pipeline and surface soil deform synergistically with a symmetric subsidence curve centered on the goaf and uniformly distributed loads, showing no significant damage. During this stage, non-synergistic deformation occurs, leading to separation between the pipeline and soil. The maximum subsidence point shifts away from the center, destroying symmetry and causing stress concentration at the mining boundary, the advancing working face, and the goaf center, resulting in severe bending and rapid failure. After this stage, the pipe–soil interaction restabilizes with reduced separation height and extent, though pipeline deformation and damage continue to increase gradually. These findings provide a theoretical basis for engineering design optimization, targeted reinforcement measures, and monitoring strategies for gas pipelines in similar goaf collapse areas. Full article

Soil heavy metal pollution caused by mineral resource extraction activities poses a serious threat to the ecological environment within and surrounding mining areas. As a highly concealed toxic heavy metal, arsenic (As) urgently requires the establishment of efficient pollution monitoring methods to achieve pollution prevention and control, as well as environmental remediation in mining areas. This study investigated the feasibility of hyperspectral remote sensing inversion for soil heavy metal arsenic based on ZY-1 02E hyperspectral satellite imagery, focusing on a mining area and its surrounding soils in Sanlisha’an, Wuxuan County, Guangxi. Full Constrained Least Squares (FCLS) was employed to separate mixed pixels and enhance soil spectral contributions in ZY-1 02E imagery, thereby mitigating vegetation interference. Six mathematical transformations, including RT, AT, FD, RTFD, ATFD, and SD, were applied to both the original and enhanced spectra to enhance spectral features. The correlations between the transformed spectra, as well as the original image spectra (S), and soil As concentration were analyzed; then the spectra strongly correlated with soil As concentration were selected to construct Ratio Spectral Index (RSI) and Normalized Difference Spectral Index (NDSI). Correlation matrices were calculated between RSI/NDSI indices and As concentration. Sensitive features were screened using an improved Successive Projection Algorithm (SPA). As concentration inversion was also performed with four models: traditional regression models, PLSR and MLR, and ensemble learning models (RF and XGBoost). In the soil contribution-enhanced spectral modeling results, the optimal transformation–index combination is ATFD-NDSI. The performance indicators of each model are as follows: MLR test set R² = 0.65, PLSR test set R² = 0.62, RF test set R² = 0.7, and XGBoost test set R² = 0.64. The results indicate that the ATFD-NDSI-RF ensemble model provides the best performance. By integrating multiple decision trees, RF effectively handles complex nonlinear relationships, thus enhancing the accuracy and generalization ability of predication. The analysis of NDSI–ATFD–RF inversion results based on sampling points indicates that model error correlates with the pollution intensity gradient, showing greater errors, especially in high-concentration areas, but still maintaining strong correlations (tailings reservoir: r = 0.92, forested areas: r = 0.96, and cropland: r = 0.83). The spatial distribution reveals that the inversion results are closely similar to the spatial distribution of IDW interpolation. Areas with high As concentrations are concentrated in the tailings reservoir and in the southeastern part of the study area. The correlation coefficient between the inversion results and IDW interpolation is 0.6, which further verifies that the inversion results effectively reproduce the spatial distribution trend of highly polluted areas. Full article

Two-dimensional phase unwrapping (PU) of interferometric synthetic aperture radar (InSAR) data remains difficult when steep deformation gradients and multi-source disturbances violate the Itoh condition. This study proposes FPUNet, a Fourier-enhanced encoder–decoder for joint denoising and 2-D PU, in which frequency-domain global context modeling is combined with complementary multi-scale spatial aggregation and attention-based feature refinement. Specifically, the bottleneck cascades a Fourier Mixed Residual Block (FMRB), atrous spatial pyramid pooling (ASPP), and a convolutional block attention module (CBAM) to suppress noise while preserving deformation-related fringe structures. FPUNet is trained end-to-end on realistically simulated Sentinel-1 interferograms generated from Shuttle Radar Topography Mission (SRTM) digital elevation models using a physics-informed composite loss that enforces data fidelity, gradient consistency, spectral regularization, and selective rewrapping consistency. On a synthetic benchmark of 1800 test interferograms, FPUNet achieves an RMSE of 0.79 rad, improving over a plain U-Net (1.61 rad) and producing fewer large fringe-number errors than least-squares, SNAPHU, PUNet, and DLPU. Experiments on real Sentinel-1 data over the Datong mining area and the 2022 Menyuan and Luding earthquakes further indicate improved phase closure and rewrapping consistency, particularly in high-gradient coseismic fringes, supporting FPUNet as a robust PU module for InSAR deformation monitoring. Full article

Remote sensing image change detection serves as a core technology in environmental monitoring. While the widespread availability of high-resolution remote sensing data provides essential support for detailed detection, it also presents technical challenges such as complex terrain interference, subtle change recognition, and large-scale scene processing. Current mainstream deep learning methods, despite their global modeling advantages, demonstrate limitations in cross-temporal fine-grained correlation mining and are prone to ambiguous edge localization in changing areas due to spatial detail loss. This paper proposes a high-resolution change detection network (TiBT-Net) that integrates bi-temporal space enhancement with token interaction. The model achieves precise change detection through dynamic token interaction and adaptive enhancement (TDIAE), utilizing deformable attention to capture semantic correlations. It constructs a Bi-Temporal Information Interaction Module (BTII) that enhances spatial details via multi-scale convolutions and channel attention, while introducing a delayed fusion mechanism (DLF) to dynamically balance dual-branch feature contributions. Experimental validations on LEVIR-CD, WHU-CD, and DSIFN-CD datasets achieved F1 scores of 90.38%, 86.74% and 96.28%, respectively, with Intersection-Union Ratios (IoU) of 82.46%, 76.59% and 92.82%. The overall accuracy (OA) reached up to 99.04%. This model effectively resolves the integration conflict between semantic information and spatial details, providing a reliable technical solution for high-precision change detection in complex scenarios. Full article

Background/Objectives: Sepsis is a leading cause of morbidity and mortality due to its rapid progression and variability in care delivery. While existing predictive models estimate sepsis risk using clinical variables, they typically rely on static attributes and overlook temporal, behavioral, and process-related characteristics of care pathways. In particular, deviations from recommended protocols and process inefficiencies are rarely incorporated into early deterioration prediction. This study proposes a Conformance-Aware Predictive Process Monitoring (CAPPM) framework to enable early detection of sepsis deterioration using incomplete care pathways. Methods: The proposed framework integrates process mining with predictive modeling. Using the publicly available Sepsis Cases Event Log, we first discovered the reference care pathway and generated prefix-level representations of ongoing cases. Temporal and behavioral features were engineered alongside alignment-based and declarative conformance metrics to quantify pathway deviations. These features were used to train and evaluate multiple supervised learning models, including Adaptive Boosting and Gradient Boosting. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUROC). Results: Incorporating conformance and pathway-based features improved predictive performance compared to models relying solely on traditional attributes. Adaptive Boosting and Gradient Boosting achieved the strongest results, with AUROC values of 0.744 and 0.731, respectively, demonstrating enhanced early detection ability. Conclusions: The findings indicate that early deviations in care pathways and temporal progression patterns provide meaningful predictive signals for sepsis deterioration. Integrating process mining with machine learning offers a promising approach for time-critical clinical decision support and proactive intervention. Full article

Regional curtain grouting for water interception serves as a critical technique for achieving safe and efficient mining under complex hydrogeological conditions in deep mine deposits. This study focuses on the Cuihongshan Iron-Polymetallic Mine, where repeated incidents of water inrush and sand outbursts have occurred due to complex hydrogeological conditions. By identifying the water-conducting pathways and characterizing the spatial distribution of relative aquitards within the mining area, a precise hydrogeological model was established. On this basis, the engineering application and performance evaluation of horizontal curtain grouting were systematically investigated. Through field monitoring and multi-method joint analysis, the water-blocking effectiveness of the grouting technique was comprehensively assessed. The results demonstrate a significant sequential reduction in grout take per meter for primary, secondary, and tertiary grouting holes, indicating a clear cumulative grouting effect. The grout effectively filled karst fractures, forming a continuous and stable water-resisting curtain. The project successfully severed the hydraulic connection between the highly water-rich Quaternary aquifer and the mine workings, substantially reducing mine water inflow. This study provides important theoretical support and practical reference for water hazard control in similar deep metal mines. Full article

Soil heavy metal contamination in mining areas poses a serious environmental challenge, requiring monitoring approaches with both wide coverage and high accuracy. Hyperspectral remote sensing provides an effective solution, yet its performance in complex mining environments is often limited by mixed-pixel effects and nonlinear spectral responses. To address these issues, this study proposes a Physically-Constrained Collaborative Endmember Extraction (PCCEE) framework that integrates spectral unmixing with machine learning for multi-element inversion. Using Gaofen-5 hyperspectral imagery, a collaborative workflow combining Pixel Purity Index (PPI), Vertex Component Analysis (VCA), and prior-spectral-constrained Spectral Angle Mapper (SAM) was developed to improve endmember purity and physical interpretability. Among three unmixing models (LMM, NMF, and SVR), the Linear Mixing Model achieved the best balance between accuracy and efficiency. Random Forest regression using retrieved abundances enabled high-accuracy inversion of eight heavy metals (mean R² = 0.85). Spatial analysis revealed significant co-enrichment of Pb, Cd, and Zn related to sulfide weathering, while PCA distinguished compound and independent pollution sources. The proposed PCCEE framework effectively mitigates mixed-pixel interference and provides a transferable approach for heavy metal monitoring and risk assessment in complex mining environments. Full article

Underground mining environments pose significant challenges for automated hazard detection due to low illumination, restricted visibility, and the absence of Global Navigation Satellite System (GNSS) coverage. These factors limit situational awareness and delay inspection efforts, particularly after disruptive events when rapid assessment is essential for safety. This study addresses this problem by developing a dual-pipeline framework for 2D–3D detection that uses 360° imaging and LiDAR-based machine learning to identify people, vehicles, and positional changes in underground settings without requiring personnel to re-enter hazardous areas. The objective was to create a system capable of recognizing objects and monitoring spatial changes under real underground mine conditions. The 2D component used a Ricoh Theta Z1 camera to collect panoramic images, and a YOLO (You Only Look Once) v8n model was fine-tuned using datasets representing low light, shadowed underground scenes. The 3D component employed an Ouster OS1-070-64 LiDAR sensor, and point clouds were processed through denoising, ICP alignment, surface reconstruction, manual annotation, and 2D projection. A YOLO-based model was then trained to detect objects and measure displacement between LiDAR scans. Results demonstrated strong performance for both components. The fine-tuned YOLOv8n model reliably detected personnel and vehicles despite challenging lighting and visual clutter, while the 3D pipeline localized objects in the registered LiDAR frame and quantified vehicle displacement between consecutive scans by comparing 3D bounding-box centroids after ICP alignment (displacement vector and magnitude). These findings indicate that the combined 2D–3D system can effectively support automated hazard recognition and environmental monitoring in GNSS-denied underground spaces. Full article

High-intensity mining activities in coal mining areas have produced large-gradient surface deformation, posing severe challenges to deformation monitoring using Interferometric Synthetic Aperture Radar (InSAR) techniques based on C-band Synthetic Aperture Radar (SAR) data. This study systematically evaluated the applicability of L-band LuTan-1 SAR (L-SAR) data versus C-band Sentinel-1A data for monitoring mining-induced surface deformation, using the Guqiao Coal Mine in Huainan as the study area. Based on 10 ascending-track and 13 descending-track L-SAR images and 42 Sentinel-1A images, deformation retrievals were performed using Differential InSAR (DInSAR) and the Small Baseline Subset (SBAS) InSAR approach, respectively, and the results were validated against independent levelling measurements. Results indicate that the mean coherence of descending- and ascending-track L-SAR interferometric pairs are 0.42 and 0.45, respectively, substantially higher than Sentinel-1A’s 0.25. In the DInSAR analysis along profile A–A′, the maximum line-of-sight (LOS) displacement obtained from descending- and ascending-track L-SAR are −0.40 m and −0.43 m, respectively, compared with −0.25 m from Sentinel-1A. In the SBAS-InSAR time-series analysis, descending- and ascending-track L-SAR yield 209,418 and 228,388 coherent points, respectively, clearly revealing the temporal evolution of surface deformation; their maximum LOS deformation rates are approximately −1.54 m·yr⁻¹ and −2.0 m·yr⁻¹, respectively. By contrast, Sentinel-1A selects only 81,669 coherent points, with severe loss of coherence in the subsidence center and a maximum LOS deformation rate of about −0.48 m·yr⁻¹. Accuracy validation shows that the Root Mean Square Error (RMSE) of vertical displacements obtained from DInSAR monitoring results based on descending and ascending L-SAR data is 16.1 mm, satisfying the requirement of centimeter-level accuracy for mining area surface subsidence monitoring. The study demonstrates the pronounced advantages of L-SAR for monitoring large-gradient, nonlinear deformation in mining environments. L-band data outperform C-band Sentinel-1A across coherence preservation, deformation sensitivity, and monitoring accuracy, providing a scientific basis for the broader application of domestic L-band SAR satellites in disaster risk assessment and long-term time-series monitoring of mining-induced subsidence. Full article

This study proposes a feature-level fusion method that integrates Differential Interferometric Synthetic Aperture Radar (D-InSAR) and Unmanned Aerial Vehicle photogrammetry (UAV-P) for monitoring mining-induced subsidence basin (MSB). The method begins by extracting key subsidence characteristics based on the patterns of coal-mining-related surface displacement; the centimeter-level subsidence boundary is determined from D-InSAR data, while the meter-scale deformation at the subsidence center is derived from UAV-P. These extracted features are then used to invert the parameters of the probability integral method (PIM). The subsidence basin predicted by the inverted parameters serves as a criterion to select the superior dataset between the D-InSAR and UAV-derived results. Finally, the selected subsidence data are fused to generate a composite subsidence map. The proposed method was applied to the 2S201 panel in the Wangjiata Coal Mine using eight Sentinel-1A images and two UAV surveys. The fusion results were evaluated for their regional and overall accuracy against 30 ground control points measured by total station and GPS. The results demonstrate that the fusion method not only accurately extracts large-scale deformations in the mining area, with a maximum subsidence of 2.5 m and a root mean square error (RMSE) of 0.277 m in the subsidence center area, but also precisely identifies the subsidence boundary region with an accuracy of 0.039 m. The fused subsidence basin exhibits an overall accuracy of 0.182 m, which represents a significant improvement of 83.6% and 27.8% over the results obtained using D-InSAR and UAV alone, respectively. This method effectively reconstructs the complete morphology of the mining-induced subsidence basin, confirming its feasibility for practical applications. Full article

Village landscapes within an 80 km radius of Lubumbashi (south-eastern Democratic Republic of the Congo) are undergoing rapid spatial transformation driven by subsistence agriculture, charcoal production, and mining activities. This study analyzes how these transformations are spatially perceived and organized across five village territories of the Lubumbashi Charcoal Production Basin using an adapted version of Kevin Lynch’s perceptual model. Landscape elements were independently identified by trained cartographic observers and by local community members. A comparison of the resulting maps yields a Sørensen similarity index ranging between 70% and 75% across villages, indicating strong convergence in spatial interpretation despite differences in expertise. Among the perceptual components, districts and landmarks account for nearly half of all identified elements and comprise the most perceptible anthropogenic disturbances. Spatial analysis shows that areas perceived as negatively impacted represent between 40% and 79% of total village surfaces. Deforestation associated with post-cultivation fallow dominates in Makisemu (47.6%) and Texas (64.4%), while woodland degradation linked to charcoal production is particularly pronounced in Mwawa (39.0%) and Luisha (25.1%). Mining-related disturbances, including soil and water alteration, are especially evident in Nsela (24.6%). These findings demonstrate that Lynch’s framework, although originally developed for urban systems, can effectively structure perception in diffuse rural woodland environments when methodologically adapted. Perception-based cartography therefore provides a robust complementary tool to biophysical monitoring for understanding the spatial footprint of anthropogenic pressures at the village scale and informing ecosystem restoration strategies. Full article