Search Results (39)

Surface deformation monitoring is essential for controlling instability processes such as urban infrastructure deformation, mining-induced subsidence, and landslide deformation. However, missing data often disrupt the continuity of the various deformation time series and compromise the reliability of monitoring results. This issue is particularly critical in long-term landslide studies, where conventional missing data imputation methods often neglect the nonlinear characteristics of slope deformation and fail to account for external influences under complex environmental conditions. To address these limitations, this study proposes a deep learning-based imputation method that integrates multi-source monitoring data. A Seq2Seq LSTM (sequence-to-sequence long short-term memory) model is constructed to reconstruct missing deformation values, and a posterior correction module is integrated to optimize the preliminary outputs, thereby enhancing imputation accuracy. The proposed approach is validated using a case study of the southern dump slope landslide at the Hesigewula South Open-Pit Coal Mine in Inner Mongolia, China. Experimental results on the test set demonstrate that the Seq2Seq LSTM–Posterior Correction model significantly outperforms traditional methods such as linear regression and baseline LSTM models. This method offers an effective solution to data gaps in landslide deformation monitoring, demonstrating strong potential for accurate nonlinear imputation in complex environments and providing a practical approach for long-term InSAR-based landslide studies in areas affected by missing SAR data. Full article

This study focuses on the monitoring and early warning of landslide hazards induced by blasting caving in the Shizhuyuan polymetallic mine. A 30-channel microseismic monitoring system was deployed to capture the spatiotemporal characteristics of rock mass fracturing during a large-scale directional stratified blasting operation (419 tons) conducted on 21 June 2012. A total of 85 microseismic events were recorded, revealing two distinct zones of intense rock failure: Zone I (below 630 m elevation, P1–P3, C6–C8) and Zone II (above 630 m elevation, P4–P5, C1–C6). The upper slope collapse occurred within 5 min post-blasting, as documented by real-time monitoring and video recordings. Principal component analysis (PCA) was applied to 54 microseismic events in Zone II to determine the kinematic characteristics of the slip surface, yielding a dip direction of 324.6° and a dip angle of 73.2°. Complementary moment tensor analysis further revealed that shear failure dominated the slope instability, with pronounced shear fracturing observed in the 645–700 m height range. This study innovatively integrates spatial microseismic event distribution with geomechanical mechanisms, elucidating the dynamic evolution of blasting-induced landslides. The proposed methodology provides a novel approach for monitoring and forecasting slope instability triggered by underground mining, offering significant implications for disaster prevention in similar mining contexts. Full article

Accurate landslide time prediction holds critical significance for ensuring safety and efficient production in open-pit mining operations. While the inverse velocity method serves as a prevalent data-driven forecasting approach, conventional single-point monitoring implementations frequently yield substantial deviations. This study proposes a multi-point collaborative inverse velocity landslide time prediction methodology using nonlinear least squares, which is based on slope radar multi-point group displacement monitoring data. Systematic stability evaluations were conducted for both single-point predictions and multi-point ensemble forecasts. Experimental results demonstrate that single-point-based predictions generally confine errors within 5 h, including the case of traditional smoothing treatments of velocity curves. The developed multi-point collaborative methodology achieves prediction errors below 1 h, with temporal forecast position variations and spatial point quantity adjustments inducing marginal error fluctuations under 2 h based on strict data exclusion. Enhanced data volume implementation significantly improves prediction accuracy and stability. These findings will provide substantive technical references and methodological guidance for advancing landslide temporal prediction research in open-pit mining engineering. Full article

Geotechnical slope failures—often precursors to catastrophic landslides and collapses—pose significant risks to mining operations and regional socioeconomic stability. Focusing on the Jiangte Xikeng lithium open-pit mine, this study integrates field reconnaissance, laboratory testing, and multi-physics numerical modeling to elucidate the mechanisms governing slope stability. Geological surveys and core analyses reveal a predominantly granite lithostratigraphy, bisected by two principal fault systems: the NE-striking F01 and the NNE-oriented F02. Advanced three-dimensional finite element simulations—accounting for gravitational loading, hydrogeological processes, dynamic blasting stresses, and extreme rainfall events—demonstrate that strain localizes at slope crests, with maximum displacements reaching 195.7 mm under blasting conditions. They indicate that differentiated slope angles of 42° for intact granite versus 27° for fractured zones are required for optimal stability, and that the integration of fault-controlled instability criteria, a coupled hydro-mechanical-blasting interaction model, and zonal design protocols for heterogeneous rock masses provides both operational guidelines for hazard mitigation and theoretical insights into excavation-induced slope deformations in complex metallogenic environments. Full article

With the gradual depletion of mineral resources in temperate regions, cold regions have become primary areas for mineral extraction. However, the freeze–thaw phenomena induced by temperature fluctuations pose significant threats to the stability of rock masses on open−pit mine slopes, further affecting normal mining operations. To investigate the strength degradation and stability evolution patterns of freeze–thaw slope rock masses, this study takes the Wushan Open−Pit Mine as its engineering context. We analyzed the relationship between rock temperature and burial depth, conducted freeze–thaw cyclic tests under realistic temperature ranges, and developed a mechanical parameter characterization model for freeze–thaw rock masses by integrating the generalized Hoek–Brown strength criterion. Slope safety factors and potential landslide mechanisms were determined through numerical simulations and the strength reduction method. Key findings include the following: (1) Shallow rock temperatures exhibit high synchronization with atmospheric temperature, characterized by large fluctuations and rapid variation rates, whereas deep rock demonstrates opposite trends. (2) As freeze–thaw cycles increase and burial depth decreases, the internal friction angle and cohesion of slope rock masses follow negative exponential decay functions. After 20 freeze–thaw cycles, the internal friction angle and cohesion of rock at a 5.27 m depth decreased by 18.36% and 33.92%, respectively. In contrast, rock at a 0.10 m depth showed more severe reductions of 31.81% and 50.14%. (3) Increasing freeze–thaw cycles progressively lower the safety factors of slope benches, with potential slip surfaces displaying reduced average depths and curvature, alongside elevated dip angles. These findings provide critical insights for preventing freeze–thaw−induced landslide hazards in cold−region open−pit mine slopes. Full article

Large reservoir-induced landslides pose a persistent threat to the safety of the Three Gorges Project and the Yangtze River shipping channel. A comprehensive multi-field monitoring system has been established to observe potential landslide areas within the Three Gorges Reservoir Area. The tasks of effectively utilizing these extensive datasets and exploring the underlying correlation among various monitoring objects have become critical for understanding landslide movement patterns, assessing stability, and informing disaster prevention measures. This study focuses on the No. 1 riverside sliding mass of the Huangtupo landslide, a representative large-scale landslide in the Three Gorges Area. We specifically analyze the deformation characteristics at multiple monitoring points on the landslide surface and within underground tunnels. The analysis reveals a progressive increase in deformation rates from the rear to the front and from west to east. Representative monitoring points were selected from the front, middle, and rear sections of the landslide, along with four hydrological factors, including two reservoir water factors and two rainfall factors. These datasets were classified using the K-means clustering algorithm, while the FP-Growth algorithm was employed to uncover correlations between landslide deformation and hydrological factors. The results indicate significant spatial variability in the impacts of reservoir water levels and rainfall on the sliding mass. Specifically, reservoir water levels influence the overall deformation of the landslide, with medium-to-low water levels (146.32 to 163.23 m) or drawdowns (−18.70 to −2.16 m/month) accelerating deformation, whereas high water levels (165.37 to 175.10 m) or rising water levels (4.45 to 17.33 m/month) tend to mitigate it. In contrast, rainfall has minimal effects on the front of the landslide but significantly impacts the middle and rear areas. Given that landslide deformation is primarily driven by periodic fluctuations in reservoir water levels at the front, the movement pattern of the landslide is identified as retrogressive. The association rules derived from this study were validated using field monitoring data, demonstrating that the data mining method, in contrast to traditional statistical methods, enables the faster and more intuitive identification of reservoir-induced landslide deformation patterns and underlying mechanisms within extensive datasets. Full article

Compared with single coal seam mining, the stratum damage induced by shallow multi-seam mining is more severe and poses a risk of mine disasters that threaten the safety of coal mine personnel. In order to reveal the characteristics and mechanism of strata damage, in this paper, field measurement, numerical simulation and mechanical analysis are used to study the development characteristics and dynamic evolution laws of overburden and explain the dynamic evolution mechanism of a water-conducting fracture zone (WCFZ) and surface cracks. The height of the WCFZ to the mining height exceeds 31.68, which is higher than the empirical value of the study area. There are self-healing and activation laws for overburden fissures in shallow multi-seam mining, which is related to the hinge rotation of overburden and the deflection of the inclined structure. However, the maximum subsidence coefficient and crack angle of the surface induced by shallow multi-seam mining does not alter, but the complexity of surface crack activity increases. The dynamic development law of WCFZ is closely related to the breaking of key strata, while the dynamic evolution of surface crack is controlled by the form of surface block fracture instability and topography. In addition, a shallow multi-seam horizontal staggered mining model that is conductive to reducing surface damage is constructed, and a method has been proposed to lessen the risk of landslides brought on by surface cracks. Full article

Open-pit mining often exposes weak rock layers, the strength of which significantly affects the stability of slopes. If these rock layers are also prone to disintegration and expansion, cyclic rainfall can exacerbate instability. Rainfall-induced changes in the seepage field also indirectly threaten the stability of slopes. Therefore, investigating the characteristics of mudstone limestone and the impact of the seepage field on slope instability under different wet–dry cycles is of great significance for the safe mining of open-pit mines. This paper takes the mudstone limestone slope of a certain open-pit mine in the southwest as the starting point and conducts experiments on saturated density, water absorption rate, permeability coefficient, compressive strength, and variable angle shear strength. Combined with scanning electron microscopy and phase analysis of X-ray diffraction analysis, the macroscopic and microscopic characteristics of the samples are comprehensively analyzed. FLAC3D software is used to explore the changes in the seepage field and the mechanism of instability. Our research found that for the preparation of mudstone limestone samples, a particle size of less than 1 mm and a drying temperature of 50 °C are optimal, with specific values for initial natural and saturated density, and natural water content. As the number of wet–dry cycles increases, the saturated density of mudstone limestone increases; the water absorption rate first rises sharply and then rises slowly; the permeability coefficient first rises sharply and then stabilizes, finally dropping sharply; the compressive and shear strength decreases slowly, and the internal friction angle changes little; frequent cycles also lead to mudification and seepage filtration. At the microscopic level, pores become larger and more regular, and the distribution is more concentrated; changes in mineral content weaken the strength. Combined with numerical simulation, the changes in the seepage field at the bottom of the slope exceed those at the slope surface and top, the transient saturated area expands, and the overall and local slope stability coefficients gradually decrease. During the third cycle, the local stability is lower than the overall stability, and the landslide trend shifts. In conclusion, wet–dry cycles change the pores and mineral content, affecting the physical and mechanical properties, leading to the deterioration of the transient saturated area, a decrease in matrix suction, and an increase in surface gravity, eventually causing slope instability. Full article

Underground coal mining induces surface subsidence, which in turn impacts the stability of slopes in mountainous regions. However, research that investigates the coupling relationship between surface subsidence in mountainous regions and the occurrence of multiple surface hazards is scarce. Taking a coal mine in southwestern China as a case study, a detailed catalog of the surface hazards in the study area was created based on multi-temporal satellite imagery interpretation and Unmanned aerial vehicle (UAV) surveys. Using interferometric synthetic aperture radar (InSAR) technology and the logistic subsidence prediction method, this study investigated the evolution of surface subsidence induced by underground mining activities and its impact on the triggering of multiple surface hazards. We found that the study area experienced various types of surface hazards, including subsidence, landslides, debris flows, sinkholes, and ground fissures, due to the effects of underground mining activities. The InSAR monitoring results showed that the maximum subsidence at the back edge of the slope terrace was 98.2 mm, with the most severe deformation occurring at the mid-slope of the mountain, where the maximum subsidence reached 139.8 mm. The surface subsidence process followed an S-shaped curve, comprising the stages of initial subsidence, accelerated subsidence, and residual subsidence. Additionally, the subsidence continued even after coal mining operations concluded. Predictions derived from the logistic model indicate that the duration of residual surface subsidence in the study area is approximately 1 to 2 years. This study aimed to provide a scientific foundation for elucidating the temporal and spatial variation patterns of subsidence induced by underground coal mining in mountainous regions and its impact on the formation of multiple surface hazards. Full article

When mineral resources are extracted using underground mining methods in hilly regions, landslides or slope failures can be induced frequently. In this study, slope collapse disasters in mountain mining areas were analyzed. The model test and numerical simulation of the slope impacted by repeated mining were carried out. The crack evolution and failure process were analyzed to reveal the instability mechanism. The results show that the rock mass would topple to the inside of the slope first, when the subsidence of overlying rock was induced by the mining of the upper coal seam. When repeated mining was performed in the lower coal seam, the mining induced macro-cracks that could connect with natural fissures, inducing the outward displacement of the slope. Then, the rock mass at the foot of the slope has to bear the upper load, which is also squeezed out by the collapsed rock mass, forming the potential slip zone. Finally, the instability is caused by the shear slip of the slope toe rock mass. Therefore, the instability evolution of the slope under underground repeated mining disturbance can be divided into four stages as follows: roof caving and overlaying rock subsidence, joint rock toppling, fracture penetration, and slope toe shearing and slope slipping. Full article

Landslides have become a common global concern because of their widespread nature and destructive power. The Gaizi Valley section of the Karakorum Highway is located in an alpine mountainous area with a high degree of geological structure development, steep terrain, and severe regional soil erosion, and landslide disasters occur frequently along this section, which severely affects the smooth flow of traffic through the China-Pakistan Economic Corridor (CPEC). In this study, 118 views of Sentinel-1 ascending- and descending-orbit data of this highway section are collected, and two time-series interferometric synthetic aperture radar (TS-InSAR) methods, distributed scatter InSAR (DS-InSAR) and small baseline subset InSAR (SBAS-InSAR), are used to jointly determine the surface deformation in this section and identify unstable slopes from 2021 to 2023. Combining these data with data on sites of historical landslide hazards in this section from 1970 to 2020, we constructed 13 disaster-inducing factors affecting the occurrence of landslides as evaluation indices of susceptibility, carried out an evaluation of regional landslide susceptibility, and identified high-susceptibility unstable slopes (i.e., potential landslides). The results show that DS-InSAR and SBAS-InSAR have good agreement in terms of deformation distribution and deformation magnitude and that compared with single-orbit data, double-track SAR data can better identify unstable slopes in steep mountainous areas, providing a spatial advantage. The landslide susceptibility results show that the area under the curve (AUC) value of the artificial neural network (ANN) model (0.987) is larger than that of the logistic regression (LR) model (0.883) and that the ANN model has a higher classification accuracy than the LR model. A total of 116 unstable slopes were identified in the study, 14 of which were determined to be potential landslides after the landslide susceptibility results were combined with optical images and field surveys. These 14 potential landslides were mapped in detail, and the effects of regional natural disturbances (e.g., snowmelt) and anthropogenic disturbances (e.g., mining projects) on the identification of potential landslides using only SAR data were assessed. The results of this research can be directly applied to landslide hazard mitigation and prevention in the Gaizi Valley section of the Karakorum Highway. In addition, our proposed method can also be used to map potential landslides in other areas with the same complex topography and harsh environment. Full article

Underground coal mining induces surface subsidence, leading to disasters such as damage to buildings and infrastructure, landslides, and surface water accumulation. Preventing and controlling disasters in subsidence areas and reutilizing land depend on understanding subsidence regularity and obtaining surface subsidence monitoring data. These data are crucial for the reutilization of regional land resources and disaster prevention and control. Subsidence hazards are also a key constraint to mine development. Recently, with the rapid advancement of UAV technology, the use of UAV photogrammetry for surface subsidence monitoring has become a significant trend in this field. The periodic imagery data quickly acquired by UAV are used to construct DEM through point cloud filtering. Then, surface subsidence information is obtained by differencing DEM from different periods. However, due to the accuracy limitations inherent in UAV photogrammetry, the subsidence data obtained through this method are characterized by errors, making it challenging to achieve high-precision ground surface subsidence monitoring. Therefore, this paper proposes a spatial domain filtering algorithm for UAV photogrammetry combined with surface deformation caused by coal mining based on the surface subsidence induced by coal mining and combined with the characteristics of the surface change. This algorithm significantly reduces random error in the differential DEM, achieving high-precision ground subsidence monitoring using UAV. Simulation and field test results show that the surface subsidence elevation errors obtained in the simulation tests are reduced by more than 50% compared to conventional methods. In field tests, this method reduced surface subsidence elevation errors by 39%. The monitoring error for surface subsidence was as low as 8 mm compared to leveling survey data. This method offers a new technical pathway for high-precision surface subsidence monitoring in mining areas using UAV photogrammetry. Full article

To proactively respond to the national fourteenth Five-Year Plan policy, we will adhere to a comprehensive land and sea planning approach, working together to promote marine ecological protection, optimize geological space, and integrate the marine economy. This paper provides a comprehensive review of the sustainable development of marine geological hazards (MGHs), with a particular focus on submarine landslides, the marine environment, as well as the marine economy. First, the novelty of this study lies in its review and summary of the temporal and spatial distribution, systematic classification, inducible factors, and realistic characteristics of submarine landslides to enrich the theoretical concept. Moreover, the costs, risks, and impacts on the marine environment and economy of submarine engineering activities such as oil and gas fields, as well as metal ores, were systematically discussed. Combined with the current marine policy, an analysis was conducted on the environmental pollution and economic losses caused by submarine landslides. Herein, the key finding is that China and Mexico are viable candidates for the future large-scale offshore exploitation of oil, gas, nickel, cobalt, cuprum, manganese, and other mineral resources. Compared to land-based mining, deep-sea mining offers superior economic and environmental advantages. Finally, it is suggested that physical model tests and numerical simulation techniques are effective means for investigating the triggering mechanism of submarine landslides, their evolutionary movement process, and the impact on the submarine infrastructure. In the future, the establishment of a multi-level and multi-dimensional monitoring chain for submarine landslide disasters, as well as joint risk assessment, prediction, and early warning systems, can effectively mitigate the occurrence of submarine landslide disasters and promote the sustainable development of the marine environment and economy. Full article

Since the beginning of spring 2022, successive landslides have occurred in the eastern pit slope of the Wolong Coal Mine in Qipanjing Town, Otog Banner, Inner Mongolia, which has adversely affected the mine’s production safety. This study aims to reveal the deformation patterns and failure mechanisms of landslides. Firstly, this study establishes the stratigraphic structure of the eastern pit slope of the Wolong Coal Mine using extensive field geological surveys combined with unmanned aerial vehicle photography, drilling, and comprehensive physical exploration techniques. Indoor geotechnical tests and microscopic experiments reveal that rock mass typically exhibits the characteristics of expansibility and water sensitivity. Moreover, the mechanical parameters of the rock mass were determined using a combination of the window sampling method, the Geological Strength Index, and the Hoek–Brown strength criterion estimation theory. Finally, this study consolidates the previously mentioned insights and employs FLAC3D (7.0) software to assess the stress–strain characteristics of the excavated slope. The results indicate that the deformation mode of the Wolong open pit coal mine is the toppling failure of soft-hard-interbedded anti-inclined layered rock slopes. The unloading effect and rock expansion-induced softening lead to stress concentration at the slope corners and more substantial deformation, thereby accelerating upper slope deformation. The deformation and destabilization process of landslides is categorized into four stages: the initial deformation stage, the development stage of lateral shear misalignment, the development stage of horizontal tensile-shear damage, and the slip surface development to the preslip stage. This research offers valuable references and engineering insights for future scientific investigations and the prevention of similar slope-related geological hazards. Full article

Deformation processes, both natural (e.g., subsidence, landslides, active tectonics) and induced (e.g., associated with mining, construction. groundwater exploitation), result in significant socioeconomic losses worldwide. Accurate detection and classification of these processes are crucial for effective risk management. In this study, we present a novel approach for the automatic classification of deformation processes using Interferometric Synthetic Aperture Radar (InSAR) data and machine learning techniques. Specifically, we use a decision tree-based classification algorithm to train a model capable of recognizing and distinguishing different types of deformation processes using time series of displacements, grouped into Active Deformation Areas (ADAs). We test this methodology in a large area in SE Spain. Our results demonstrate promising performance, with an Area Under the Curve (AUC) > 0.95, identifying several covariates of morphometric, geological, hydrogeological, and geotechnical nature as key factors. This automatic classification of InSAR data holds significant implications for risk management associated with ground deformation, providing a potentially valuable tool for decision makers in urban planning and land management officials. Full article