Search Results (2,295)

Coal mining can cause the rupture of the overlying strata, and the energy released by large-scale fractures can therefore induce earthquake disasters, which in turn can cause more secondary disasters. In the past 50 years, countless earthquakes induced by coal mining have been reported. In this paper, the main factors relating to the mining-induced seismicity, including the mechanical properties, geometry of the space, excavation advance, and excavation rate, are investigated using both experimental and numerical methods. The sensitivity of these factors behaves differently with regard to the stress distribution and failure mode. Space geometry and excavation advances have the highest impact on the surface settlement and the failure, while the excavation rate in practical engineering projects has the least impact on the failure mode. The numerical study coincides well with the experimental observation. The result indicates that the mechanical properties given by the geological survey report can be effectively used to assess the risk of mining-induced seismicity, and the proper adjustment of the tunnel geometry can largely reduce the surface settlement and improve the safety of mining. Full article

Earthquakes, as one of the most destructive natural disasters, often cause significant casualties and severe economic losses. Accurate prediction of earthquake fatalities is of great importance for pre-disaster prevention and mitigation planning, as well as post-disaster emergency response deployment. To address the challenges of small sample sizes, high dimensionality, and strong nonlinearity in earthquake fatality prediction, this paper proposes an integrated modeling approach (PCA-IWOA-XGBoost) combining Principal Component Analysis (PCA), the Improved Whale Optimization Algorithm (IWOA), and Extreme Gradient Boosting (XGBoost). The method first employs PCA to reduce the dimensionality of the influencing factor data, eliminating redundant information and improving modeling efficiency. Subsequently, the IWOA is used to intelligently optimize key hyperparameters of the XGBoost model, enhancing the prediction accuracy and stability. Using 42 major earthquake events in China from 1970 to 2025 as a case study, covering regions including the west (e.g., Tonghai in Yunnan, Wenchuan, Jiuzhaigou), central (e.g., Lushan in Sichuan, Ya’an), east (e.g., Tangshan, Yingkou), north (e.g., Baotou in Inner Mongolia, Helinger), northwest (e.g., Jiashi in Xinjiang, Wushi, Yongdeng in Gansu), and southwest (e.g., Lancang in Yunnan, Lijiang, Ludian), the empirical results showed that the PCA-IWOA-XGBoost model achieved an average test set accuracy of 97.0%, a coefficient of determination (R²) of 0.996, a root mean square error (RMSE) and mean absolute error (MAE) reduced to 4.410 and 3.430, respectively, and a residual prediction deviation (RPD) of 21.090. These results significantly outperformed the baseline XGBoost, PCA-XGBoost, and IWOA-XGBoost models, providing improved technical support for earthquake disaster risk assessment and emergency response. Full article

Granite formations provide suitable geological conditions for building gravity dams. However, the presence of intruding granite creates a fractured zone. The interaction of this fractured zone with structural planes and faults can create geological conditions that are unfavorable for the anti-sliding stability of gravity dams. This paper identifies the dominant structural planes that affect the anti-sliding stability of dams by studying the three-dimensional intersection relationships between groups of structural planes, faults, and fracture zones. The three-dimensional distribution and occurrence of the dominant structural planes directly impact the anti-sliding stability and sliding failure mode of gravity dams. Through comprehensive field investigations and systematic analysis of engineering geological data, the spatial distribution characteristics of structural planes and fracture zones were quantitatively characterized. Subsequently, the potential for deep-seated sliding failure of the gravity dam was rigorously evaluated and conclusively dismissed through application of the rigid body limit equilibrium method. It was established that the sliding mode of the foundation of the dam under this combination of structural planes is primarily shallow sliding. Additionally, based on the engineering geological data of the area around the dam, a three-dimensional finite element numerical model was developed to analyze stress–strain calculations under seepage stress coupling conditions and compared with calculations made without considering seepage stress coupling. The importance of seepage in the anti-sliding stability of the foundation of the dam was determined. The research findings provide engineering insights into enhancing the anti-sliding stability of gravity dams in granite distribution areas by (1) identifying critical structural planes and fracture zones that control sliding behavior, (2) demonstrating the necessity of seepage-stress coupling analysis in stability assessments, and (3) guiding targeted reinforcement measures to mitigate shallow sliding risks. Full article

Hyperspectral image (HSI) denoising is a crucial step in image preprocessing as its effectiveness has a direct impact on the accuracy of subsequent tasks such as land cover classification, target recognition, and change detection. However, existing methods suffer from limitations in effectively integrating multi-scale features and adaptively modeling complex noise distributions, making it difficult to construct effective spatial–spectral joint representations. This often leads to issues like detail loss and spectral distortion, especially when dealing with complex mixed noise. To address these challenges, this paper proposes a multi-scale differentiated denoising network based on spatial–spectral cooperative attention (MDSSANet). The network first constructs a multi-scale image pyramid using three downsampling operations and independently models the features at each scale to better capture noise characteristics at different levels. Additionally, a spatial–spectral cooperative attention module (SSCA) and a differentiated multi-scale feature fusion module (DMF) are introduced. The SSCA module effectively captures cross-spectral dependencies and spatial feature interactions through parallel spectral channel and spatial attention mechanisms. The DMF module adopts a multi-branch parallel structure with differentiated processing to dynamically fuse multi-scale spatial–spectral features and incorporates a cross-scale feature compensation strategy to improve feature representation and mitigate information loss. The experimental results show that the proposed method outperforms state-of-the-art methods across several public datasets, exhibiting greater robustness and superior visual performance in tasks such as handling complex noise and recovering small targets. Full article

Accurate forecasting of river siltation is essential for ensuring inland waterway navigability and guiding sustainable sediment management. This study investigates the downstream reach of the Shihutang navigation power hub along the Ganjiang River in Jiangxi Province, China, an area characterized by pronounced seasonal sedimentation and hydrological variability. To enable fine-scale prediction, we developed a data-driven framework using a random forest regression model that integrates high-resolution bathymetric surveys with hydrological and meteorological observations. Based on the field data from April to July 2024, the model was trained to forecast monthly siltation volumes at a 30 m grid scale over a six-month horizon (July–December 2024). The results revealed a marked increase in siltation from July to September, followed by a decline during the winter months. The accumulation of sediment, combined with falling water levels, was found to significantly reduce the channel depth and width, particularly in the upstream sections, posing a potential risk to navigation safety. This study presents an initial, yet promising attempt to apply machine learning for spatially explicit siltation prediction in data-constrained river systems. The proposed framework provides a practical tool for early warning, targeted dredging, and adaptive channel management. Full article

The description and assessment of braided river architecture are usually limited by the paucity of real geological datasets from field observations; due to the complexity and diversity of rivers, traditional evaluation models are difficult to apply to braided river systems in different climatic and tectonic settings. This study aims to establish an architectural model suitable for the study area setting by introducing a hierarchical analysis approach through well-exposed three-dimensional outcrops along the Second Songhua River. A micro–macro four-level hierarchical framework is adopted to obtain a detailed anatomy of sedimentary outcrops: lithofacies, elements, element associations, and archetypes. Fourteen lithofacies are identified: three conglomerates, seven sandstones, and four mudstones. Five elements provide the basic components of the river system framework: fluvial channel, laterally accreting bar, downstream accreting bar, abandoned channel, and floodplain. Four combinations of adjacent elements are determined: fluvial channel and downstream accreting bar, fluvial channel and laterally accreting bar, erosionally based fluvial channel and laterally accreting bar, and abandoned channel and floodplain. Considering the sedimentary evolution process, the braided river prototype, which is an element-based channel filling unit, is established by documenting three contact combinations between different elements and six types of fine-grained deposits’ preservation positions in the elements. Empirical relationships are developed among the bankfull channel depth, mean bankfull channel depth, and bankfull channel width. For the braided river systems, the establishment of the model promotes understanding of the architecture and evolution, and the application of the hierarchical analysis approach provides a basis for outcrop, underground reservoir, and tank experiments. Full article

In order to improve the inversion accuracy of electrical resistivity tomography (ERT) and overcome the limitations of traditional linear methods, this paper proposes an improved shuffled frog leaping algorithm (SFLA). First, an equilibrium grouping strategy is designed to balance the contribution weight of each subgroup to the global optimal solution, suppressing the local optimum traps caused by the dominance of high-quality groups. Second, an adaptive movement operator is constructed to dynamically regulate the step size of the search, enhancing the guiding effect of the optimal solution. In synthetic data tests of three typical electrical models, including a high-resistivity anomaly with 5% random noise, a normal fault, and a reverse fault, the improved algorithm shows an approximately 2.3 times higher accuracy in boundary identification of the anomaly body compared to the least squares (LS) method and standard SFLA. Additionally, the root mean square error is reduced by 57%. In the engineering validation at the Baota Mountain mining area in Jurong, the improved SFLA inversion clearly reveals the undulating bedrock morphology. At a measuring point 55 m along the profile, the bedrock depth is 14.05 m (ZK3 verification value 12.0 m, error 17%), and at 96 m, the depth is 6.9 m (ZK2 verification value 6.7 m, error 3.0%). The characteristic of deeper bedrock to the south and shallower to the north is highly consistent with the terrain and drilling data (RMSE = 1.053). This algorithm provides reliable technical support for precise detection of complex geological structures using ERT. Full article

The Mw 7.8 Gorkha Earthquake of 25 April 2015 triggered over 25,000 landslides across central Nepal, with 4775 events concentrated in Gorkha District alone. Despite substantial advances in landslide susceptibility mapping, existing studies often overlook the compound role of post-seismic rainfall and lack robust spatial validation. To address this gap, we validated an ensemble machine learning framework for co-seismic landslide susceptibility modeling by integrating seismic, geomorphological, hydrological, and anthropogenic variables, including cumulative post-seismic rainfall. Using a balanced dataset of 4775 landslide and non-landslide instances, we evaluated the performance of Logistic Regression (LR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) models through spatial cross-validation, SHapley Additive exPlanations (SHAP) explainability, and ablation analysis. The RF model outperformed all others, achieving an accuracy of 87.9% and a Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) value of 0.94, while XGBoost closely followed (AUC = 0.93). Ensemble models collectively classified over 95% of observed landslides into High and Very High susceptibility zones, demonstrating strong spatial reliability. SHAP analysis identified elevation, proximity to fault, peak ground acceleration (PGA), slope, and rainfall as dominant predictors. Notably, the inclusion of post-seismic rainfall substantially improved recall and F1 scores in ablation experiments. Spatial cross-validation revealed the superior generalizability of ensemble models under heterogeneous terrain conditions. The findings underscore the value of integrating post-seismic hydrometeorological factors and spatial validation into susceptibility assessments. We recommend adopting ensemble models, particularly RF, for operational hazard mapping in earthquake-prone mountainous regions. Future research should explore the integration of dynamic rainfall thresholds and physics-informed frameworks to enhance early warning systems and climate resilience. Full article

The lacustrine to deltaic depositional systems of the Upper Jurassic Qigu Formation in the Yongjin area constitute a significant petroleum reservoir in the central Junggar Basin, China. Based on core observations, petrology analyses, paleoenvironment indicators and modern sedimentary analyses, sequence stratigraphy, lithofacies associations, sedimentary environment, evolution, and models were investigated. The Qigu Formation can be divided into a third-order sequence consisting of a lowstand systems tract (LST) and a transgressive systems tract (TST), which is further subdivided into six fourth-order sequences. Thirteen lithofacies and five lithofacies associations were identified, corresponding to shallow-water delta-front deposits. The paleoenvironment of the Qigu Formation is generally characterized by an arid freshwater environment, with a dysoxic to oxic environment. During the LST depositional period (SQ1–SQ3), the water depth was relatively shallow with abundant sediment supply, resulting in a widespread distribution of channel and mouth bar deposits. During the TST depositional period (SQ4–SQ6), the rapid rise in base level, combined with reduced sediment supply, resulted in swift delta retrogradation and widespread lacustrine sedimentation. Combined with modern sedimentary analysis, the shallow-water delta in the study area primarily comprises a composite system of single main channels and distributary channel-mouth bar complexes. The channel-bar complex eventually forms radially distributed bar assemblages with lateral incision and stacking. The distributary channel could incise a mouth bar deeply or shallowly, typically forming architectural patterns of going over or in the mouth bar. Reservoir test data suggest that the mouth bar sandstones are favorable targets for lithological reservoir exploration in shallow-water deltas. Full article

This study proposes a design method for near-slope roads along multi-level slopes that integrates excavation requirements and post-construction ecological restoration through sprayed eco-protection. Firstly, the design principles and procedural steps for near-slope roads are established. The planar layouts of multi-level slopes are categorized, including mixing areas, turnaround areas, berms, and access ramps. Critical technical parameters, such as curve radii and widths of berms and ramps, as well as dimensional specifications for turnaround areas, are systematically formulated with corresponding design formulas. The methodology is applied to the ecological restoration project of multi-level slopes in the Huamahu mountainous area, and a comparative technical-economic analysis is conducted between the proposed design and the original scheme. Results demonstrate that the optimized design reduces additional maintenance costs caused by near-slope roads by 6.5–8.0% during the curing period. This research advances the technical framework for multi-level slope governance and enhances the ecological design standards for slope protection engineering. Full article

We demonstrate the potential of using the convolutional equivalent layer to jointly process large gravity-gradient datasets. Based on the equivalent-layer principle, we assume a single fictitious physical property distribution on a planar layer can approximate all components of the gravity-gradient tensor. Estimating this distribution using the classical technique ensures physical consistency among components. However, the classical approach becomes computationally prohibitive for large datasets due to the need to solve a large-scale inversion with a massive sensitivity matrix. To overcome this limitation, we exploit the block-Toeplitz Toeplitz-block structure of the sensitivity matrix for data on a regular horizontal grid. This structure significantly reduces computational cost—by orders of magnitude—compared to the classical method. Applications to synthetic and real datasets show that our method offers a computationally efficient alternative for processing large gravity-gradient data from various acquisition systems (AGG and FTG), even when data are irregularly spaced or flight lines are misaligned. On a standard laptop configuration, our method processed over 290,000 AGG data points in a few tens of seconds. It also handled between 726,000 FTG and 1,250,000 AGG data points within seconds to a couple of minutes, demonstrating practical computational efficiency for large-scale datasets. Full article

On 22 May 2012, a magnitude Mw 5.6 earthquake struck the Pernik region of western Bulgaria, causing structural damage in nearby cities, including Sofia. This study assesses the seismic response of a six-story reinforced concrete building in central Sofia, utilizing real accelerogram data recorded at the basement (SGL1) and sixth floor (SGL2) levels during the earthquake. Using the Kanai–Yoshizawa (KY) model, the study estimates inter-story motion and assesses amplification effects across the structure. Analysis of peak ground acceleration (PGA), velocity (PGV), displacement (PGD), and spectral ratios reveals significant dynamic amplification of peak ground acceleration and displacement on the sixth floor, indicating flexible and dynamic behavior, as well as potential resonance effects. The analysis combines three spectral techniques—Horizontal-to-Vertical Spectral Ratio (H/V), Floor Spectral Ratio (FSR), and the Random Decrement Method (RDM)—to determine the building’s dynamic characteristics, including natural frequency and damping ratio. The results indicate a dominant vibration frequency of approximately 2.2 Hz and damping ratios ranging from 3.6% to 6.5%, which is consistent with the typical damping ratios of mid-rise concrete buildings. The findings underscore the significance of soil–structure interaction (SSI), particularly in sedimentary basins like the Sofia Graben, where localized geological effects influence seismic amplification. By integrating accelerometric data with advanced spectral techniques, this research can enhance ongoing site-specific monitoring and seismic design practices, contributing to the refinement of earthquake engineering methodologies for mitigating seismic risk in earthquake-prone urban areas. Full article

This study compared two approaches to integrating leveling and GNSS data to develop relative vertical movements of the Earth’s crust. Novel approaches were tested using transformation and hybrid grid adjustment. The results from double-leveling measurements in Poland were used as test data, and GNSS measurements developed using the PPP technique were used as Supplementary Data. The least squares method was used for the adjustment, and the isometric, conformal and affine methods were used for the transformation, with and without Hausbrandt correction. So-called pseudo-nodal points, i.e., points identified as common in both networks, whose weight was determined according to the assumptions of scale-free network theory, were used as integration points. Both integration methods have similar results and are suitable for integrating leveling and GNSS data to determine the relative vertical movements of the Earth’s crust. The average unit error m₀ of the transformation was 0.1 mm/yr and the average error after adjustment of the hybrid network was 0.1 mm/yr. The use of the Hausbrandt correction does not significantly improve the transformation results. A 12-parameter affine transformation is recommended as the transformation method. Full article

Historical districts are the mark of the continuity of urban history and are non-renewable. Typhoon disasters rank among the most serious and frequent natural threats to China’s coastal regions. Improving the wind resilience of China’s coastal historical districts is of great significance for their protection and inheritance. Accurately analyzing the different characteristics of the influencing factors of wind resilience in China’s coastal historical districts can provide a theoretical basis for alleviating the damage caused by typhoons and formulating disaster prevention measures. This paper accurately identifies the main influencing factors of wind resilience in China’s coastal historical districts and constructs an influencing factor system from four aspects: block level, building level, typhoon characteristics, and emergency management. An IIM model for the systematic analysis of influencing factors of wind resilience in China’s coastal historical districts based on the Improved Decision Making Trial and Evaluation Laboratory (IDEMATEL), Interpretive Structural Modeling (ISM), and Matrices Impacts Croises-Multiplication Appliance Classement (MICMAC) methods is established. This allows us to explore the mechanism of action of internal influencing factors of typhoon disasters and construct an influencing factor system, in order to propose prevention measures from the perspective of typhoon disaster characteristics and the overall perspective of China’s coastal historical districts. The results show that the driving force of a building’s windproof design in China’s coastal historical districts is low, but its dependence is strong; the driving forces of block morphology, typhoon level, and emergency plan are strong, but their dependence is low. A building’s windproof design is a direct influencing factor of the wind resilience of China’s coastal historical districts; block morphology, typhoon level, and emergency plan are the most fundamental and key influencing factors of the wind resilience of China’s coastal historical districts. Full article

This study presents a comprehensive comparison of static and SLAM (Simultaneous Localization and Mapping) laser scanners of both new and old generation in a controlled indoor environment of a standard commercial building with long, linear corridors and recesses. The aim was to assess both global and local accuracy, as well as noise characteristics, of each scanner. Methods: A highly accurate static scanner was used to generate a reference point cloud. Five devices were evaluated: two static scanners (Leica RTC 360 and Trimble X7) and three SLAM scanners (GeoSLAM ZEB Horizon RT, Emesent Hovermap ST-X, and FARO Orbis). Accuracy analysis included systematic and random error assessment, axis-specific displacement evaluation, and profile-based local accuracy measurements. Additionally, noise was quantified before and after data smoothing. Static scanners yielded superior accuracies, with the Leica RTC 360 achieving the best performance (absolute accuracy of 1.2 mm). Among SLAM systems, the Emesent Hovermap ST-X and FARO Orbis—both newer-generation devices—demonstrated significant improvements over the older-generation GeoSLAM ZEB Horizon RT. After smoothing, the noise levels of these new-generation SLAM scanners (approx. 2.1–2.2 mm) approached those of static systems. The findings underline the ongoing technological progress in SLAM systems, with the new-generation SLAM scanners becoming increasingly viable alternatives to static scanners, especially when speed, ease of use, and reduced occlusions are prioritized. This makes them well-suited for rapid indoor mapping applications, provided that the slightly lower accuracy is acceptable for the intended use. Full article