Search Results (946)

Geoelectric field observations play a vital role in geophysical exploration, geological disaster early warning, and underground resource detection. Traditional contact non-polarisable electrodes, which require burial and electrolyte coupling, are hindered by several issues, such as limited adaptability to challenging terrain, significant electrode potential drift, and high susceptibility to environmental interference. Existing non-contact electric field sensors often exhibit insufficient coupling capacitance, poor impedance matching for ultra-weak high-impedance signals, and inadequate low-frequency noise suppression, rendering them unsuitable for the precise acquisition of natural microvolt-level geoelectric field signals. To address these challenges, this study introduces an innovative non-contact geoelectric field sensor with a three-layer composite structure. The sensor operates based on the principle of a parallel-plate capacitor, with a conductive silver paste layer at the top acting as the signal acquisition electrode plate, which forms an equivalent parallel-plate capacitance model with the ground to achieve non-contact capacitive coupling for geoelectric field detection. The intermediate layer uses lead zirconate titanate (PZT) piezoelectric ceramics as a support medium with a high dielectric constant. At the bottom is a silicon-based, flexible, sensitive ground-contacting layer with high elasticity, which allows it to adapt to micro-level surface irregularities, eliminating air gaps between the electrode plate and the ground, increasing plate-to-ground coupling capacitance, and ensuring the stability of the capacitance. The three-layer structure was created using a dry-press sintering integration approach, which eliminates interlayer bonding materials while ensuring consistent dielectric performance and efficient charge transfer. Additionally, a specialised signal-conditioning circuit was designed to match the ultra-high-impedance sensitive unit, utilising the ADA4528-2 ultra-low-noise precision operational amplifier, which achieved low-loss conversion and strong noise suppression for ultra-weak high-impedance charge signals. The circuit simulation results demonstrate that the designed circuit achieves an input impedance of no less than 10 TΩ, an effective operating bandwidth from 0.02 Hz to 20 kHz, and a voltage noise density lower than 1.5 μV/√Hz at 10 Hz, fully covering the ultra-low-frequency effective band of natural geoelectric fields. Field experiments comparing artificial and natural field signals revealed that the proposed sensor could be quickly deployed by simply attaching it to the ground without burial. Its time-domain waveform consistency and frequency-domain component matching were nearly identical to those of commercial standard solid non-polarisable electrodes, with a cross-correlation coefficient greater than 0.98, indicating no significant potential drift or power-frequency interference. By structurally eliminating the inherent electrode potential difference, the sensor offers advantages such as ease of deployment, strong environmental adaptability, high precision for weak signal acquisition, and excellent engineering substitutability. It is well suited for long-term geoelectric field observations in complex field scenarios, including deserts, Gobi areas, and frozen soil regions, and provides a high-performance, novel sensing solution for geoelectric field detection in extreme environments. Full article

The loess gully region is characterized by complex terrain with crisscrossing gullies, where coal mining can readily induce surface subsidence and slope deformation. Such deformation often leads to geological hazards and ecological issues, including collapses, landslides, soil erosion, vegetation dry up, and land degradation. Therefore, understanding the deformation behavior of mining-induced slopes is essential for the restoration and management of mine geological environments. This study focuses on five slopes within working faces 50205 and 50206 of the Zhen’er Coal Mine in Fugu County. Using a combination of 3DEC numerical simulations and orthophoto-based fracture identification, we systematically investigated mining-induced slope deformation under the complex topographic conditions of the loess gully region. The goal is to answer three key questions: where mining-induced slope deformation primarily occurs, how it evolves over time, and what the main controlling factors are. Spatially, the primary deformation zones and their propagation paths vary significantly among the five slopes. The largest deformation occurs in the slope body directly above the main section of the working face, gradually decreasing toward the edges of the working face. Temporally, mining-induced slope deformation exhibits a time lag, meaning that surface responses lag behind underground mining activities and continue to develop even after the working face is fully extracted. In the loess gully region, slope deformation induced by mining is controlled not only by mining activities but also by topographic factors such as slope shape, aspect, gradient, and height. The spatiotemporal evolution of deformation becomes even more complex for slopes that span multiple working faces. These findings provide a scientific basis for monitoring mining-induced slope deformation and preventing geological disasters in the loess gully region, while also offering practical guidance for safe mining operations and hazard control in similar settings. Full article

Landslide susceptibility assessment is essential for disaster-risk reduction, land-use regulation, and territorial spatial planning in mountainous and hilly regions. However, the practical application of machine learning-based susceptibility models is often limited by the trade-off between predictive accuracy and model interpretability, as well as the instability of factor importance across different algorithms and study areas. Taking Longchuan County in northeastern Guangdong Province, China, as a case study, this research develops a comparative explainable machine learning framework to evaluate landslide susceptibility and examine the cross-model stability of SHAP-based factor attribution under local geo-environmental conditions. Fifteen conditioning factors were initially derived from multi-source geological, topographic, hydrological, environmental, and anthropogenic datasets. After multicollinearity screening using Pearson correlation analysis, twelve key factors were retained for model construction. A total of 363 historical landslide points and an equal number of non-landslide samples were divided into training and testing datasets using a stratified 70:30 sampling strategy. Eight machine learning models were optimized through grid-search parameter tuning and then comparatively evaluated. The results show that all models achieved strong predictive performance, with test-set AUC values exceeding 0.938. Among them, the Gradient Boosting Decision Tree model performed best, with an AUC of 0.9520 and the most stable control of overfitting, followed closely by CatBoost with an AUC of 0.9512. SHAP-based interpretation further revealed that the normalized difference water index, relief, and distance to rivers were the dominant factors controlling landslide susceptibility in the study area, with the normalized difference water index serving as a key explanatory factor across models. The proposed framework improves the transparency and reliability of landslide susceptibility assessment and provides a methodological reference for localized, explainable machine learning applications in geohazard risk management. Full article

The surface subsidence caused by coal mining is a geological environmental disaster that restricts the sustainable development of mining areas. Traditional monitoring methods have limitations in long-term and high-precision observation. Therefore, this paper proposes a synergistic monitoring framework for mining subsidence under thick loose layers by integrating Interferometric Synthetic Aperture Radar (InSAR) technology and Unmanned Aerial Vehicle (UAV) photogrammetry. The research results show: (1) UAV photogrammetry can accurately obtain the large gradient deformation at the center of the subsidence basin, while InSAR has better accuracy at the basin edge. The proposed fusion method is significantly superior to a single method. (2) The parameters obtained by the probability integral method based on the fused data are in good agreement with the parameters obtained by leveling measurement data, and the relative error of the parameters is less than 4%. (3) The thick and loose-layered mining areas have the characteristics of larger subsidence, steeper gradient at the center, slow convergence at the edge, and wide influence range. This study provides a new approach for precise subsidence monitoring, and the revealed subsidence characteristics provide a scientific basis for disaster assessment and mining optimization in similar areas. Full article

Monitoring the activation of concealed water-conducting structures and predicting the evolution of mining-induced floor failure above a confined aquifer are critical for ensuring the safety and sustainability of deep coal mining. The present study formulates an optimized hydrophobic similar material, improves the bidirectional four-face stress-adjustable loading test platform, integrates water pressure-flow and excitation current monitoring systems, and innovatively introduces the electric current change rate (K value) as a core analytical indicator to systematically conduct physical simulation experiments on floor failure during coal seam mining above a confined aquifer containing concealed water-conducting structures. The results demonstrate the successful development of similar materials with tunable properties (density: 1605–1994 kg·m⁻³; uniaxial compressive strength: 0.07–0.41 MPa; water absorption: 0.2–3%; permeability: 6.8 × 10⁻⁶–7.68 × 10⁻⁴ cm·s⁻¹), effectively replicating the mechanical and seepage characteristics of the prototypical rock strata. The spatiotemporal evolution of the mining-induced fracture field was identified to occur in two distinct stages: “horizontal–vertical evolution” followed by “horizontal periodic evolution”, with a failure depth stabilizing above the No. 9 lower coal seam and a horizontal lag of 4.3–10.1 cm behind the working face. The K value parameter proves highly sensitive in dynamically characterizing the multi-field coupling process of stress–damage–seepage, enabling the clear delineation of the floor’s “six horizontal zones” and “three vertical zones” structure. Crucially, the K value analysis revealed the underlying mechanism of confined water conduction, showing a significant upward migration in the concealed structure area that approached, but did not breach, the key aquifuge layer. The present study provides a novel geophysical perspective and an effective technical parameter (K value) for deciphering the failure mechanism of mining-disturbed coal seam floors, thereby offering a diagnostic framework and a theoretical basis for water hazard early warning and the promotion of green and safe mining practices. Full article

Reliable landslide susceptibility mapping (LSM) depends not only on classifier selection but also on the construction of non-landslide samples. Conventional random or buffer-based sampling can retain candidate negatives that are environmentally similar to landslides, increasing label ambiguity and reducing model reliability. This study proposes an IF-KMeans negative sampling framework to refine candidate non-landslide samples for LSM in Wuzhou City, China. Isolation Forest was trained using 395 mapped landslides and then applied to 2000 candidate negative samples to remove samples with high similarity to the landslide feature space; K-Means clustering was subsequently used to stratify the retained candidates and select representative negative samples. The optimized samples were evaluated using six classifiers, including LR, SVM, MLP, RF, XGBoost, and LightGBM, and compared with conventional buffer-based sampling. The IF-KMeans framework consistently improved AUC across the six classifiers, with gains of 0.041–0.081, and the IF-KMeans-RF model achieved the highest AUC of 0.944. Additional diagnostics showed that the IF-removed samples were closer to known landslides in environmental feature space and were located in areas with higher local landslide density, indicating higher potential confusion risk. These findings suggest that positive-sample-guided negative-sample refinement can reduce ambiguity in LSM training data and improve the reliability of susceptibility mapping for geological disaster prevention and risk mitigation. Full article

Flash floods are among the most destructive natural hazards, often causing substantial loss of life and severe damage to infrastructure and property. Predicting flood-prone areas remains challenging because flood generation is controlled by complex interactions among topographic, hydrological, climatic, and environmental factors. In this study, six machine learning algorithms—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree Classifier (DTC), AdaBoost, and Artificial Neural Network (ANN)—were developed to predict flash-flood inundation locations using satellite-derived flood inventories from two major rainfall events in Wadi El-Darb and Wadi El-Allaqi, Egypt. Model performance was evaluated using accuracy, precision, recall, and F1-score. During model development, Random Forest and Decision Tree Classifier achieved the highest prediction accuracy (94%), followed by AdaBoost and ANN (92%), while Logistic Regression (89%) and SVM (88%) also produced satisfactory results. To evaluate model generalization, the trained models were independently validated using a rainfall event in Wadi Hodein (Egypt) and a major flash-flood event that occurred in Oman during April 2024. The external validation showed that AdaBoost achieved the highest predictive performance in both validation basins, with accuracies of 87% for Wadi Hodein and 83% for Oman, providing encouraging initial evidence of applicability across hydrologically similar arid watersheds, While AdaBoost and Logistic Regression maintained satisfactory performance during external validation, other algorithms exhibited noticeable reductions in recall and F1-score, particularly in the Oman case study, indicating variability in model generalization across independent watersheds These findings suggest that the proposed framework may support flood susceptibility assessment in ungauged arid environments with comparable hydrological characteristics, although further validation across a wider range of climatic and geological settings is needed. Overall, the results highlight the value of integrating satellite remote sensing with machine learning to support flood hazard assessment, disaster preparedness, early warning systems, and flood risk management in data-scarce regions. Full article

Water inrush disasters in karst tunnels pose a significant threat to construction safety, project timelines, and the long-term sustainability of infrastructure. Effective risk assessment is crucial for mitigating these hazards and ensuring the resilient development of underground transportation networks. This study proposes a quantitative risk assessment model that integrates the entropy weight method with attribute interval recognition theory to address the uncertainties inherent in complex geological environments. First, a hierarchical evaluation index system is established based on four primary controlling factors: stratigraphy, geological structure, topography, and hydrogeology. Subsequently, the entropy weight method is employed to objectively determine the weight of each index, thereby minimizing human bias. The attribute interval recognition model is applied to calculate the comprehensive attribute measure for each tunnel segment, effectively managing the fuzziness of risk classification boundaries. The risk grade is ultimately determined using the confidence criterion. The proposed model is applied to the Qigan Mountain karst tunnel in Chongqing, China, which is divided into 56 segments for detailed analysis. Results show 41.32% (4088 m) high-risk, 40.14% (3971 m) medium-risk and 18.55% (1835 m) low-risk sections, which are highly consistent with the theoretical water inflow calculation results. The model realizes accurate and quantitative water inrush risk assessment, providing a scientific basis for disaster prevention and control in karst tunnel construction, and further promoting the sustainability and safety of underground engineering in karst areas. Full article

As a natural event that poses a serious threat to human life, property, and the natural ecology, the effective identification, assessment, and early prevention of geological hazards are crucial. Hunan Province in China is a region with a high incidence of geological hazards, exhibiting complex chain-generated characteristics due to the influence of terraced topography, heavy rainfall, and human activities. Existing landslide monitoring methods have insufficient ability to capture weak deformation at small spatial scales, making it challenging to identify landslide disaster precursors in this region effectively. This paper proposes a multi-resolution SAR collaborative monitoring method using SBAS-InSAR technology for wide-area screening, followed by a joint PS/DS-InSAR processing framework to identify weak deformation signals at small spatial scales. Using 2441 registered geohazard sites in the work area as the background dataset, wide-area InSAR monitoring and remote-sensing interpretation delineated 180 suspected geohazard target areas. Field investigation confirmed 83 of the 180 candidate target zones as active hidden-danger points, corresponding to a field-confirmed rate of 46.11% among the interpreted candidates. Full article

As frequent and highly destructive geologic hazards, debris flows necessitate effective monitoring alongside rapid and accurate detection to support disaster prevention and mitigate losses of life and property. Current detection technologies, however, are often limited by high false alarm rates, insufficient accuracy, considerable model complexity that complicates deployment, and the elevated costs of contact-based detection. To overcome these limitations, this paper introduces a lightweight real-time debris flow detection model based on the Roboflow Detection Transformer (RF-DETR). First, we constructed a dataset of realistic debris flow scenarios including debris flow disaster events worldwide. Based on this, the lightweight vision transformer model EfficientFormerV2 is adopted as the backbone. By employing a dimension-consistent architecture, the model avoids the frequent switching between 4D and 3D features found in traditional Vision Transformers (ViTs), thereby reducing a significant number of inefficient operations. Additionally, we optimized the multiscale projection layer by removing downsampling and feature aggregation operations, which reduces redundant computations and improves feature extraction efficiency. Furthermore, the introduction of the Efficient Intersection over Union (EIoU) loss function achieves faster convergence and improved detection accuracy for debris flows. Ablation studies performed on our debris flow dataset demonstrate that the improved model reduces parameters by 54.2% and computational load by 36.4% while ensuring acceptable losses in detection accuracy and latency. This significant reduction in model size and complexity achieves effective lightweighting, fulfilling the requirements for deployment on edge devices and enabling real-time debris flow detection. Full article

The Yili River Valley in Xinjiang is characterized by complex geological structures and frequent geological hazards, which seriously threaten local lives, property, and infrastructure. Improving the accuracy and interpretability of geological hazard assessment is therefore of great significance. To address this, nine factors, including elevation, distance from fault, and slope, were selected to construct a WOE-BPNN-SHAP coupled model. The weights of evidence (WOE) method was first used for factor correlation testing and to optimize the input of the BP neural network. The evaluation accuracies of WOE, WOE-DNN, and WOE-BP models were then compared, and the SHAP model was introduced to analyze the coupling relationships among factors. Results show that the WOE-BP model achieves the best predictive performance, with an AUC of 83.65%. Areas of extremely high-risk account for 8.63% of the study area, while higher-risk areas account for 15.39%. Elevation (1688–2847 m), distance from fault (<3000 m), precipitation (192.6–290.8 mm), and slope (>16°) are identified as the main driving factors. This coupled method provides a new technical approach for regional geological hazard assessment and offers a theoretical basis for disaster prevention, mitigation, and resilience building in the Yili River Valley. Full article

A frequent and sudden two-phase (solid–liquid) geological hazard in mountainous areas, the evolution of debris flows involves the coupling of multiple physical fields, making monitoring and early warning particularly challenging. To accurately reveal the dynamic patterns of debris flow evolution and improve early-warning accuracy, this study focused on the Ni Chang Valley area in Shimian County, Ya’an City, Sichuan Province. Based on the COMSOL Multiphysics coupling simulation platform, a multiphysics bidirectionally strongly coupled numerical model was proposed and constructed, integrating the SPH (smoothed particle hydrodynamics) meshless particle method, FLO-2D shallow-water dynamics, and the MassFlow full-process simulation approach. Using COMSOL as a unified framework, this model employs MassFlow’s deep-integration, continuous medium method to simulate rainfall triggering and material source activation, FLO-2D’s shallow-water equations to describe macroscopic flow-deposition processes, and SPH’s mesh-free particle method to accurately capture large deformations and free-surface flow. The model fully reproduces the entire dynamic chain of debris flow processes, from rainfall triggering and soil mobilization to fluid transport and channel deposition. The reliability and accuracy of the model were verified by comparing it with field measurements from the 20 September 2022 historical debris flow event at Ni Chang Valley. Quantitative analysis indicates that when the viscosity coefficient increases from 0.1 Pa·s to 100 Pa·s, the flow velocity decreases by approximately 47% and the flow depth increases by approximately 62%. When the yield stress increases from 1 Pa to 100 Pa, the deposition area shrinks from 269,900 m² to approximately 109,000 m², a reduction of about 60%. Combining the results of the dynamic analysis, daily maximum temperature, daily precipitation, moisture content, mud-water level, and ground surface displacement were selected as core monitoring indicators. The analytic hierarchy process (AHP) was used to determine the weights of each indicator, and a data- and physics-driven weighted summation model for debris flow monitoring and early warning was constructed to achieve a five-level debris flow monitoring and early-warning system. Historical disaster cases demonstrate that this early-warning model can provide advance predictions of debris flow disasters up to 2 h and 40 min in advance. The warning lead time is sufficient, the grading logic is clear, and the model is capable of accurately capturing precursor information on disasters. Full article

Landslide susceptibility assessment acts as a core technical tool for geological disaster governance, ecological protection and long-term risk mitigation strategies. This modeling approach quantifies the possibility of slope-collapse events and delivers objective decision-making support for regional geologic environment supervision. To overcome the low computational efficiency and weak capacity of conventional evaluation frameworks to extract multi-level spatial grid rules, this paper takes Nanning City, the capital and largest city of the Guangxi Zhuang Autonomous Region in southern China, as the research object. Ten types of terrain and geological control factors combined with historical landslide inventory records are adopted to build a two-stage coupled evaluation framework integrating the information value method (IVM), a Bidirectional Temporal Convolutional Network (BiTCN) and Transformer, named IVM-BiTCN–Transformer. The hierarchical framework first adopts IVM to finish preliminary hazard grading and calculate factor contribution weights, then inputs classified grid samples into the BiTCN-Transformer module to realize local terrain feature and global factor fusion, which significantly lifts the overall identification precision. Ten widely adopted landslide evaluation algorithms are selected for contrast simulation, with multiple quantitative metrics adopted to judge model reliability. Experimental outcomes prove that the presented IVM-BiTCN–Transformer framework obtains superior hazard discrimination capacity, which can raise the precision and stability of landslide zoning and offer reliable technical support for targeted regional geological disaster prevention. Full article

Amid growing global instability and the escalating impacts of climate change, there is an increasing need to develop resilient human habitats, particularly underground environments. At the same time, resource-extraction activities have left behind extensive underground voids in abandoned mines, presenting a valuable opportunity to expand multifunctional spaces that can serve daily needs as well as emergency shelter functions (dual-use), while also supporting urban–rural transformation and sustainable development goals. Due to their geological conditions and mining methods, underground goafs offer inherent advantages for dual-use development. In light of this, this study proposes a theoretical approach to address the three fundamental challenges associated with the dual-use of underground goafs in gypsum mines from the perspective of architectural space creation. This study does not present a completed empirical validation; instead, it develops a conceptual and interdisciplinary methodological framework intended to guide future empirical research and engineering implementation. Specifically, the framework is as follows: (1) defining escape safety capacity under disaster impacts by constructing a dynamic assessment model integrating disaster physics, behavior simulation, and VR-calibrated experiments; (2) elucidating the correlation mechanism between spatial topological features and human response patterns using space syntax and multi-modal psychological experiments to reveal how spatial morphology influences orientation, emotion, and behavior; and (3) moving beyond the traditional notion that space should be adapted to functional requirements, proposing an innovative strategy involving adapting predefined functions to the space. Full article

Lake Sarez in Tajikistan, formed by a major earthquake-induced landslide in 1911, is located in the highly seismically active Pamir–Hindu Kush region. The lake is impounded by the Usoi Dam, one of the largest natural landslide dams in the world, which has raised concerns regarding its long-term stability and associated downstream flood hazards. Due to its geomorphological setting and potential exposure to multiple triggering mechanisms, including seismic activity and landslides, Lake Sarez is widely considered a high-consequence hazard system. Although the dam has remained stable for over a century and is currently monitored using modern geodetic and satellite-based technologies, uncertainties remain regarding its internal structure and response to extreme external forcing. While existing early warning systems enhance preparedness in downstream communities, effective long-term risk reduction requires continued monitoring, improved hazard modeling, and strengthened regional cooperation. This review synthesizes existing studies on the geological setting, hazard potential, stability assessments, and disaster risk management strategies related to Lake Sarez. It highlights the importance of integrated multi-hazard analysis and precautionary risk governance in managing low-probability but high-impact natural dam failure scenarios. Full article