Fuquan Tang

Monitoring surface subsidence in mining areas is essential for geological disaster early warning and safe production. Existing geometric difference methods heavily rely on the local consistency of multi-temporal point clouds. When horizontal displacement and vertical subsidence are coupled, horizontal movements often cause local misalignments, leading to spatial deviations and discrete anomalies in vertical estimations. To address this issue, this paper proposes DL-C2C, a deep learning model for subsidence extraction from bi-temporal ground point clouds. Within a unified framework, the model introduces horizontal displacement as an auxiliary constraint into the vertical solving process, effectively improving the stability of vertical subsidence estimation through continuous cross-temporal alignment and correlation updating. For feature extraction, DL-C2C employs a PointConv multi-scale pyramid combined with a proposed scale-adaptive Transformer to enhance cross-scale information interaction under sparse and non-uniform sampling conditions. Furthermore, the network constructs dynamic local associations through iterative alignment within a recursive framework, and introduces diffusion-based residual correction at the fine-scale stage to compensate for detail errors at subsidence basin boundaries and in data-missing regions. Experiments on simulated and real-world datasets—covering aeolian sand and mountainous gully landforms—demonstrate that the method achieves mining 3D error (M3DE) of 0.16 cm and 0.22 cm in simulated scenarios. In real-world mining area validations, compared to existing methods, DL-C2C significantly reduces discrete anomalous points, yields an error distribution closer to zero, and exhibits superior performance in boundary transition continuity and non-subsidence area stability. In conclusion, this model provides reliable technical support for large-scale, high-precision intelligent monitoring of geological disasters in mining areas.

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.

The repeated extraction of coal seams in the Loess Plateau mining region has greatly increased the severity of surface deformation and associated damage. Accurately characterizing the spatio-temporal evolution of subsidence and the underlying mechanisms is a critical engineering challenge for mining safety. Taking the Dafosi Coal Mine located in the loess gully region as a case study, this paper thoroughly examines the variations in surface deformation and damage characteristics caused by single and repeated seam mining. The analysis integrates surface movement monitoring data, global navigation satellite system (GNSS) dynamic observations, field surveys, unmanned aerial vehicle (UAV) photogrammetry, and numerical simulation methods. Notably, to ensure the accuracy of prediction parameters, a refined Particle Swarm Optimization (PSO) algorithm incorporating a neighborhood-based mechanism was employed specifically for the inversion of probability integral parameters. The results indicate that the subsidence factor and horizontal movement factor increase markedly following repeated mining. The maximum surface subsidence velocity also increases substantially, and this acceleration remains evident after normalizing by mining thickness and face-advance rate. The fore effective angle is smaller in repeated mining than in single-seam mining, and the duration of surface movement is substantially extended. Repeated mining fractured key strata and caused a functional transition from the classic three-zone response to a two-zone connectivity pattern, while the thick loess cover responds as a disturbed discontinuous-deformation layer, which together aggravates step-like and slope-related damage. The severity of surface damage is strongly influenced by topographic features and geotechnical properties. These findings demonstrate that the proposed integrated approach is highly effective for geological hazard assessment and provides a practical reference for engineering applications in similar complex terrains.