Search Results (1,746)

The Loess Plateau in China is highly susceptible to frequent landslides and other geological disasters, which have led to substantial losses of natural and human resources and are frequently reported in the news media. Yuzhong County, located east of Lanzhou City, is a mountainous region with considerable development potential. On 7 August 2025, this area experienced a large-scale geological disaster characterized by a compound event involving both landslides and debris flows, resulting in nearly several hundred casualties. With the ongoing urban expansion of Yuzhong County in recent years, the prediction and prevention of geological disasters have become increasingly critical. This study employed three machine learning algorithms—Multiple Logistic Regression (LR), Random Forest (RF), and XGBoost (XG)—to assess landslide susceptibility in Yuzhong County. A total of 169 historical landslide points, supplemented by additional sites identified through field investigations, were compiled, along with 200 non-landslide locations. Multiple environmental factors were incorporated into the models to analyze landslide susceptibility across different areas. Because LR can effectively capture the generalized influence of precipitation variability, it was selected as the primary model for the final susceptibility mapping. To more accurately evaluate the impact of precipitation on landslide occurrence, average seasonal precipitation across the four seasons was used as a predictive factor. To refine the risk assessment at the township level, both raster-based and landslide-unit-based evaluation approaches were adopted. Overlay analyses were then performed by integrating urban infrastructure, population distribution, and predicted landslide hazard zones, while also accounting for the potential influence of extreme precipitation events. The results reveal that the mountainous areas in eastern Mapo Township, southern Xiaokangying Township, southern Xiaguanying Town, and the south-central part of Qingshuiyi Township are high-risk zones prone to group-occurrence landslide disasters. Full article

In carbonate petroleum reservoirs, permeability impairment caused by asphaltene precipitation and deposition remains a major challenge that limits long-term productivity. This study introduces a green polymeric nanocomposite (KCl/SiO₂/Xanthan/Origanum vulgare, NCs) designed to control interfacial dynamics and preserve flow capacity in carbonate formations. Using a multi-technique approach—interfacial tension (IFT) analysis, atomic force microscopy (AFM), and rock-core, fluid-flooding experiments at simulated subsurface conditions—the NCs’ abilities were evaluated in terms of their potential to modify properties at fluid–fluid and fluid–rock interfaces. The NCs increased the CO₂–brine/oil IFT slope in certain pressure regions by up to 40.77%. These results indicate competitive adsorption that stabilizes interfaces. Adsorption isotherms confirmed a monolayer mechanism with a high capacity of 294.12 mg/g. AFM topographic mapping revealed order-of-magnitude changes in surface roughness (reductions in average roughness by ~75%, root-mean-square by ~83%, peak-to-valley by ~93%). These results directly link nanoscale smoothing to reduced capillary pinning. Core flooding tests demonstrated that NCs treatment decreased formation damage by up to 67.45% at 4000 psi, maintaining a high permeability ratio (k/k_i = 0.87) and preserving porosity (φ/φ_i = 0.887, representing 88.7% porosity retention). These results establish that the studied NCs coherently manipulate fluid physics in relation to molecular adsorption and macroscopic permeability. Consequently, these NCs offer a sustainable, high-performance strategy for flow assurance and formation damage control in geological and geothermal reservoirs. Full article

The Longmenshan Fault Zone, as the steep boundary on the eastern edge of the Qinghai–Xizang Plateau, is the seismogenic structure that includes strong earthquakes such as the 7.9 magnitude Wenchuan earthquake in 2008 and the 6.6 magnitude Lushan earthquake in 2013. Based on the U.S. Geological Survey (M ≥ 2.5) earthquake catalogue from 2000 to 2025, this study systematically analyzed the spatio-temporal evolution of seismic activities in this area. We determined the completeness of the seismic magnitude by time periods and drew a spatial B-value distribution map using the maximum likelihood estimation method to reveal its variation characteristics. The analysis is divided into three intervals: 2000–2007 (pre-Wenchuan), 2008–2012 (co- and post-Wenchuan), and 2013–2025 (long-term postseismic stage; the 2008–2025 interval includes an observational and forecast assessment window). Low b values persist in the central and southern parts of the LMSF, indicating that the degree of stress concentration in these two regions is relatively high. After 2008, the b value of the Wenchuan Fault Zone rose briefly. After 2013, the b value gradually declined. This fluctuation confirmed the re-accumulation process of regional stress. The analysis results of the Z-value rate change show that there is obvious stillness in the central fault zone (Z > 2), while there is slight activation in some southern areas of the LMSF (Z ≈ −0.5 to 0). These patterns are roughly consistent with the spatial B-value structure, and our research results also provide diagnostic conclusions for interpreting the long-term seismic activity evolution and stress heterogeneity of the LMSF. Full article

Full-field water saturation is central to waterflood surveillance but cannot be observed continuously across a reservoir, whereas well histories provide sparse dynamic evidence. We formulate target-time saturation reconstruction as a mapping from static geology, well locations, scheduled controls, simulated multi-well production responses, and development time to a two-dimensional saturation field. The water-balance-guided saturation network (WB-SatNet) combines a U-Net spatial pathway, a fixed-order gated recurrent unit (GRU) history encoder, explicit time conditioning, and a closed-boundary water-storage consistency term. Experiments used 400 geological realizations, 800 simulation runs, six target times, realization-wise train/validation/test splitting, and three random seeds. On the held-out test set, WB-SatNet achieved a mean absolute error (MAE) of 0.01175, a coefficient of determination (${\mathrm{R}}^2$) of 0.98145, a structural similarity index measure (SSIM) of 0.99177, a flooded-area intersection over union (IoU) of 0.95523, and a global storage-consistency error of 0.00944. Its mean MAE was 6.31% lower than that of TCN-U-Net, the strongest temporal convolutional network baseline. Target-time, component-ablation, flooded-area, storage-consistency, history-window, noise, and operating-regime analyses support a monitoring-oriented interpretation. These results indicate that WB-SatNet provides an effective framework for production-history-conditioned water-saturation reconstruction and waterflood state monitoring in the investigated setting. Full article

With the acceleration of global urbanization and the large-scale development of underground spaces, underground engineering faces extremely complex and variable geological environments and high-risk construction disturbances. The widespread application of the Internet of Things and novel sensing technologies has given rise to structural health monitoring data increasingly characterized by massive volume, high dimensionality, multi-source heterogeneity, and strong spatiotemporal coupling. Traditional data processing methods based on mechanical analysis, empirical formulas, or numerical simulation have increasingly exposed limitations of insufficient accuracy, lengthy computation times, and weak generalization capability when confronted with such engineering big data. Machine learning and deep learning technologies, by virtue of their superior nonlinear mapping capability, advantages in feature extraction from massive data, and flexible architectural design, provide solutions for efficient knowledge extraction and intelligent assessment of underground engineering monitoring data. This paper reviews the current application status and frontier advances of machine learning technologies in the field of underground engineering monitoring data processing in recent years. First, the development trajectory of analytical algorithms evolving from classical shallow machine learning, through temporal and spatial deep learning, to physics-data dual-driven approaches is delineated. Second, targeting the critical challenges of missing field data and sparse sensor deployment, spatiotemporal fusion imputation techniques and spatial reconstruction methods incorporating mechanical prior knowledge are thoroughly evaluated, elucidating the paradigm shift in monitoring philosophy from discrete point-based alarming to inference-augmented sparse sensing that approximates full-field state awareness through model-dependent estimation rather than direct measurement. Third, the applications of machine learning in underground structural deformation mechanism interpretation, key influencing factor identification based on explainable artificial intelligence (AI), and rapid back-analysis of geomechanical parameters are summarized. Finally, composite network architectures and physics-constrained guidance strategies for non-stationary deformation time series prediction under complex and variable working conditions are discussed. A methodological audit of the 73 included studies—of which 33 enter the quantitative comparison tables—reveals that 26 of the 33 audited studies (78.8%) validate exclusively on single-project data, only 1 study conducts rigorous out-of-distribution generalization testing, and none of the 33 studies (0%) provides uncertainty quantification. These findings highlight cross-project generalization and probabilistic prediction as important methodological challenges. This paper aims to provide theoretical references and methodological guidance for safety early warning, intelligent construction, and full life-cycle health management of underground engineering. Full article

High-fidelity reservoir simulation is too costly for multi-query tasks such as history matching and production optimisation. Existing reduced-order models (ROMs) mitigate this cost but generally lack uncertainty quantification, spatial sparsity, and interpretable mode-to-geology mappings. We introduce USX-PGD (Uncertainty-aware, Sparse, and eXplainable Proper Generalised Decomposition), a non-intrusive ROM for two-phase immiscible flow that addresses all three gaps within a single greedy Alternating Least Squares framework. USX-PGD is benchmarked against Proper Orthogonal Decomposition (POD), standard Proper Generalised Decomposition (PGD), and an intermediate Uncertainty-aware Sparse PGD (US-PGD) variant, on a formation-aware upscaled coarse-grid ($30\times 110\times 34$ cells) representation of the SPE10 Model 2 benchmark, a heterogeneous two-phase reservoir with permeability contrasts spanning six orders of magnitude. US-PGD adds sparsity-promoting thresholding and bootstrap resampling to certify a confidence interval on reconstruction accuracy; USX-PGD further adds formation energy decomposition, mode dominance mapping, breakthrough attribution, and mode sensitivity indexing, attributing the reduced-order modes to identifiable geological formations. All four methods reproduce the reference production curves to within $11.03$–$11.05\%$ NRMS at online reconstruction times of 51–63 ms; the sparse and explainable variants achieve comparable accuracy while additionally providing $41.8\%$ spatial sparsity and a certified $95\%$ confidence interval. All four ROMs compress and replay an already-simulated trajectory, not predict new, unsimulated scenarios; USX-PGD is offered as a reproducible, physically transparent foundation for such multi-query workflows, with predictive extension identified as future work. Full article

Geological conditions fundamentally constrain land-use suitability, terrain stability, and long-term sustainability, yet they remain underutilized in urban planning frameworks. This study presents a GIS-based geological suitability assessment model, tested on the City of Valjevo, Serbia, integrating lithological, structural, land use/land cover change and spatial data to guide urban development decisions. Seven lithological units—Quaternary deposits, lacustrine sediments, carbonate rocks, volcanic and pyroclastic rocks, ophiolites, ophiolitic mélanges, and Jadar Block sediments—were classified into four suitability classes based on lithological composition, structural characteristics, and rock mass behavior. An Integrated Geological Index (Igeo) was derived using an area-weighted approach across a regular hexagonal grid, enabling spatially explicit suitability mapping. Comparison with land use/land cover changes for 2012–2021 revealed a notable mismatch between geological suitability and observed development patterns: favorable and conditionally favorable terrains cover 72.5% of the study area but account for only 48.7% of recent urban expansion, while unfavorable terrains, occupying just 20.9% of the territory, absorbed 51.3% of new development. No expansion occurred within highly unfavorable terrains. These findings expose critical gaps in integrating geological criteria into planning practice and demonstrate a reproducible methodology for embedding geological suitability into sustainable urban development strategies across geologically heterogeneous regions. Full article

Radon-222, a decay product of the uranium-238 series, is the principal source of natural ionizing-radiation exposure in most indoor environments and an established cause of lung cancer. In New Mexico, uranium-bearing geology, a legacy of uranium mining and milling, and arid, variably constructed housing create elevated but poorly characterized geogenic radon potential. This evidence-informed narrative review examines radon protection in New Mexico by synthesizing the radiological basis of the hazard (uranium-series sources, radium-226 emanation, and soil–gas transport into buildings) with measurement, monitoring, and mapping evidence relevant to under-resourced regions. We emphasize that representative, high-resolution indoor radon measurements for the state are still lacking. We show that the central challenge is not only geologic potential but the uneven distribution of measurement, testing, and mitigation capacity across housing type, tenure, geography, and jurisdiction, including Tribal lands governed by consent-based data agreements. We evaluate monitoring and outreach interventions by evidence strength and outline a phased, standards-based program (statewide measurement and data systems, high-resolution mapping, school and rental testing, workforce development, and mitigation support) to convert radiological knowledge into measurable, equitable exposure reduction. New Mexico serves as a well-documented representative example; the synthesis is intended to inform other rural, Tribal, and under-resourced jurisdictions. Full article

Rare metal exploration increasingly relies on the integration of heterogeneous geological datasets and advanced analytical methods to improve the efficiency and reliability of mineral prospecting. This study presents the development of a web-based Geographic Information System, Geospatial Information System for Optimized Rare Metal Exploration in Eastern Kazakhstan (GISORMEK), using the central part of the Kalba–Narym rare-metal belt (Eastern Kazakhstan) as a case study. A comprehensive geospatial database was developed through the digitization of archival geological maps and the integration of geological, geochemical, tectonic, geophysical, geomorphological, and mineral occurrence datasets. To complement historical mapping data, Landsat-8 multispectral imagery was incorporated to improve lithological discrimination and identify hydrothermal alteration zones. Two remote sensing techniques were applied: Principal Component Analysis (PCA) for lithological mapping and enhancement of geological features, and band ratio (BR) analysis for the calculation of geological spectral indices, including the iron oxide index and the hydroxyl-bearing (Al–OH) mineral index. The resulting spectral indices were subsequently integrated to generate a predictive hydrothermal alteration map. GISORMEK integrates historical and contemporary datasets within a unified web-GIS framework, ensuring spatial consistency, reproducibility, and accessibility. The proposed framework enhances the interpretation of the mineralization potential of the Kalba–Narym region and provides geospatial platform for supporting rare metal exploration and future mineral prospectivity assessments. Full article

Rock fissure parameters, such as length, width, and density, are essential for analyzing the progressive instability of open-pit mine slopes. Under the combined effects of engineering disturbance, geological conditions, and environmental factors, slope fissures continuously propagate and evolve. However, large variations in fissure scale, complex rock-surface textures, blurred boundaries, and weak micro-fissure features increase the difficulty of intelligent fissure segmentation, identification, and parameter extraction. Consequently, many mining enterprises still rely on manual interpretation, which is time-consuming and susceptible to subjective errors. To address these challenges, this study develops Crack-YOLO, a task-oriented fissure detection and instance-segmentation model based on YOLOv8-Seg. A total of 500 original UAV images were collected from multiple open-pit mines and processed to construct a dataset containing 3600 fissure image patches, including 3240 images for training and 360 images for testing. In Crack-YOLO, selected C2f modules are replaced with contextual semantic enhancement modules (CoT Blocks), and a texture information enhancement module (SM Block) is incorporated to strengthen contextual semantic representation and fine-grained texture-feature extraction. The model achieved segmentation precision, recall, mAP50, and mAP50:95 values of 0.896, 0.787, 0.854, and 0.392, respectively. For object detection, the corresponding values were 0.968, 0.862, 0.959, and 0.773, respectively. The segmentation results were further processed using K3M skeleton extraction and physical-scale calibration to quantitatively extract geometric parameters, including fissure length, equivalent average width, and azimuth. Validation using an image containing seven representative fissures yielded mean absolute errors of 0.016 m, 0.010 m, and 0.90° for fissure length, equivalent average width, and azimuth, respectively, indicating the feasibility of the proposed parameter-quantification workflow. In an application test conducted in a typical open-pit mine scene, the proposed workflow identified 196 fissures within approximately 22 s and quantitatively analyzed their geometric parameters and distribution characteristics. The results indicate that the proposed method has potential for fissure identification and geometric-parameter quantification in open-pit mine slopes and may provide quantitative data support for slope-fissure monitoring and stability analysis. Full article

This study presents a multidisciplinary investigation of the fluvial evolution of the northern meander of the Calore River at Cellarulo locality, near Benevento (southern Italy). The research integrates archaeological evidence, geological and geomorphological data, historical cartography, remote sensing imagery and geophysical surveys in a Geographic Information System (GIS) environment. Multi-temporal analysis of historical maps, aerial photographs and satellite images from 1824 to 2022 allowed the reconstruction of channel migration patterns and the identification of abandoned meanders and paleochannel traces. Stratigraphic data derived from boreholes revealed the presence of a channel of the Calore River dated at least in the Bronze Age (3900 years ago), abandoned in the nineteenth century. Geoelectrical investigations provided detailed information on subsurface resistivity anomalies, highlighting the presence of buried structures and possible ancient anthropogenic features located at shallow depths between 1 and 1.5 m. The combined interpretation of geomorphological, archaeological and geophysical data demonstrates significant data on the unveiling of an ancient river channel and its abandonment during the last 150 years, suggesting a strong interaction between natural fluvial dynamics and human occupation. The results confirm the effectiveness of an integrated multidisciplinary approach for reconstructing fluvial landscape evolution and for identifying buried archaeological and geomorphological features in complex floodplain environments. Full article

In open-pit mining, accurately mapping lithology, alteration, and mineralogy is vital for optimizing ore grade estimation, processing, and operational planning. Traditional geological pit-wall mapping is time-consuming, labor-intensive, and exposes technical staff to hazardous conditions. This study investigates a data-driven, hyperspectral classification workflow for pit-wall mapping at an open-pit gold mine. Three classification techniques were applied: Spectral Angle Mapper (SAM) using scene-derived reference spectra, Autoencoder + K-means (AE), and Band Ratio + K-means (BR), without relying on predefined training labels. Hyperspectral imagery was acquired using a tripod-mounted system integrating VNIR (400–1000 nm) and SWIR (970–2500 nm) sensors from two pit walls of contrasting geological complexity, with SWIR data driving primary mineral classification and VNIR data used separately for iron oxide mapping. Results were validated against laboratory hyperspectral scanning and XRD analyses of collected rock samples, as well as expert geological interpretation. Across both pit walls, Across both pit walls, SAM and BR produced closely consistent, geologically reliable classifications, with SAM showing the strongest correspondence to reference spectra and confirmed mineral assemblages; AE achieved comparable polygon-level agreement but with reduced mineralogical specificity, tending toward broader, less distinct class groupings. The study demonstrates that close-range tripod-based hyperspectral imaging with data-driven classification provides a practical, rapid alternative to manual pit-wall mapping, improving accuracy, reducing interpretation time, and minimizing personnel exposure to hazardous conditions. Full article

The main objective of the present study was to conduct landslide susceptibility assessment and zoning analysis based on multi-scale imbalanced sampling and deep learning methods. Jiangkou Town, China, was selected as the study area. Grid cells with resolutions of 12.5 m, 25 m, and 50 m were chosen. Imbalanced sampling was applied using landslide/non-landslide ratios of 1:1, 1:2, and 1:3 to construct multi-scale modeling datasets. Susceptibility conditioning factors were screened using the frequency ratio (FR), Pearson correlation coefficient, and multicollinearity diagnostics and nine factors were obtained: slope, aspect, plane curvature, profile curvature, lithology, distance to river, distance to fault, annual rainfall, and land use. Six models—Logistic Model Tree (LMT), Kernel Logistic Regression (KLR), EfficientNet, ResNet, Transformer, and U-Net—were selected to establish 54 susceptibility evaluation models under various combinations of resolution and sampling ratios. The predictive reliability of the models was evaluated using receiver operating characteristic (ROC) curves and Kappa coefficients. Among the evaluated configurations, ResNet at a 12.5 m resolution with a 1:3 sampling ratio was retained as the preferred overall mapping configuration. It achieved a validation AUC of 0.963 and a Kappa coefficient of 0.778, together with strong susceptibility-zonation selectivity. The highest individual Kappa coefficient (0.819) was obtained by ResNet at a 25 m resolution with a 1:3 sampling ratio. Thus, the preferred configuration was identified through an integrated interpretation of the validation AUC, Kappa agreement, and susceptibility-zonation performance rather than by maximizing a single metric. The landslide susceptibility maps produced were classified into five levels and validated using the landslide distribution, landslide density and frequency ratio within each susceptibility zone. Most models showed good predictive performance in areas characterized by very high and very low susceptibility. According to the results of the comparison of the different susceptibility levels, it appears that ResNet and EfficientNet produced the most similar spatial predictions, while ResNet and Transformer presented the largest deviations. The deviations are mainly located near river valleys and areas with intense human activity. The proposed methodological framework and results can support disaster prevention, land-use planning, and regional risk management, particularly in mountainous areas with complex geological and topographic conditions. Full article

The Jiaoxibei gold cluster is one of the most significant gold-producing regions in China and retains substantial regional prospecting potential. However, the superposition of multiple mineralization events has resulted in strong spatial coupling, multi-scale variability, and substantial redundancy among structural, alteration, geophysical, and geochemical information, limiting the effective extraction of key ore-controlling features. This study developed a geologically constrained feature-engineering framework for regional mineral potential evaluation. An initial indicator system comprising 32 geologically meaningful factors was constructed from structural geometry, remote-sensing alteration, gravity, magnetic, and geochemical information. The previously developed SOMML method was extended by introducing borehole-derived geological constraints to construct G-SOMML and generate the comprehensive geochemical anomaly factor Chem_F. Correlation-based redundancy reduction, Random Forest importance evaluation, SHAP interpretation, and geological screening were then combined to identify nine core factors. The selected factors were subsequently transformed according to their mineralization-response directions and integrated through category-balanced fuzzy synthetic evaluation. By linking geology-guided feature construction, borehole-constrained geochemical anomaly extraction, data-driven feature diagnosis, and balanced evidence integration, the framework provides a reproducible and interpretable workflow for organizing heterogeneous geoscientific information in regional mineral potential evaluation. The resulting high-potential zones captured 21 of the 22 known gold occurrences in the Sanshandao and Jiaojia areas and 17 of the 23 occurrences in the other parts of the study area, yielding an overall deposit capture rate of 84.4%. Compared with the all-feature fuzzy synthetic evaluation model (AT-Fuzzy), feature engineering reduced the high-potential area ratio from 50.951% to 25.356% while increasing the deposit capture rate from 51.1% to 84.4%. At the map level, the proposed framework delineated a substantially smaller high-potential area than the Random Forest model while achieving higher deposit capture rates than both the Random Forest and Weights of Evidence models under the common evaluation domain and statistical thresholding criterion. These results support the applicability of the framework for interpretable regional mineral potential evaluation and target prioritization in complex metallogenic districts. Full article

This study investigates the stability of the high cutting slope at the Hejiaping Interchange on the Shiyi Expressway. Based on geological mapping, direct shear tests, and numerical calculations, the potential instability modes and shear strength parameters of the structural planes were determined. Under natural conditions, the friction coefficient is 0.35 with a cohesion of 0.02 MPa; under saturated conditions, these values are 0.26 and 0.09 MPa, respectively. Limit equilibrium methods were employed to quantitatively evaluate the slope stability and safety factors. The results indicate that the ultimate failure mode involves overall sliding along interlayer weak planes. Influenced by factors such as excavation blasting, unloading, and dominant structural planes, progressive bedding-slip instability may occur at the leading edge during excavation. After implementing a combined anti-sliding pile and anchor cable support system, the safety factor increased by about 0.5, verifying the effectiveness of the support design. Full article