Search Results (286)

Fracture orientation data in structural geology are commonly affected by non-negligible angular uncertainty, which can significantly impact the reliability of classification and interpretation of deformation patterns. In this work, we address the problem of discriminating between two groups of directional observations. To account for measurement uncertainty inherent in field data, we adopt a deconvolution-based circular kernel discriminant rule specifically designed for noisy angular observations. This approach explicitly incorporates the measurement-error mechanism into the estimation process, allowing for more robust classification in the presence of observational noise. The methodology is applied to measurements arising in structural geology, where the discrimination of fracture orientations is relevant to the interpretation of deformation patterns and to applications in rock engineering. Specifically, we consider two datasets from Ordovician turbidites, involving different types of orientation data. The first dataset consists of $L_0^1$ axes, representing linear features described by Plunge–Azimuth coordinates, while the second dataset concerns axial-plane cleavage surfaces, expressed in terms of Dip and Dip direction. We assess the performance of the estimator under varying levels of angular uncertainty and alternative error distributions, with a focus on its ability to correctly separate the two geological groups. Results show that explicitly modeling measurement error leads to improved discrimination accuracy and more reliable identification of structural patterns compared to standard methods that neglect noise. Full article

Liquefaction phenomena are strongly influenced by the depositional evolution of the area, including sediment grain size, depositional age, shallow layering, and groundwater depth. This study focuses on a 560 km² wide sector of the eastern Po River Plain (northern Italy), encompassing part of the modern Po Delta, to evaluate the susceptibility of the different geological units to liquefaction. A comprehensive dataset was compiled, integrating lithological, chronological (¹⁴C), geomorphological, hydrological, and hydrogeological information, together with satellite imagery, historical and modern maps, archaeological evidence, and subsurface data from core drilling and CPTu tests. The integrated analysis allowed us to reconstruct a liquefaction susceptibility map recognizing four classes: very high (4% of the investigated area), high (26%), moderate (20%), and non-susceptible (50%). CPTu-based statistical analyses confirm that the Liquefaction Potential Index (LPI) increases with higher susceptibility classes and decreases with increasing groundwater depth (0.5, 1.5, and 3.0 m scenarios). These results provide a scientific basis to support sustainable land management and governance strategies in the Po Delta, an area of high environmental, cultural, and economic value, a large sector of which is included in the Natura 2000 network. Full article

Pegmatite dikes are important prospecting indicators for rare-metal deposits, whereas traditional methods for pegmatite dike identification are constrained by the limited capability of human visual interpretation to capture information from remote sensing imagery, resulting in low identification accuracy and efficiency. In recent years, global research on semantic segmentation of different surface features and remote sensing-based mineral exploration using deep learning methods and high-resolution remote sensing imagery has made significant progress; however, studies on surface-exposed geological bodies such as pegmatite dikes remain highly insufficient. To address the key problem of efficiently identifying pegmatite dikes in remote sensing imagery, this study proposes an improved model based on UNet++, termed GAD-UNet++. In the field of remote sensing geology, this study constructed a pegmatite dike semantic segmentation dataset based on high-resolution RGB imagery by using 0.66 m RGB imagery for visual delineation and ZY1F hyperspectral data for spectral constraint and label refinement; on this basis, semantic segmentation of surface pegmatite dikes in the Yilanlike area of the South Tianshan Mountains, Xinjiang, was conducted using RGB remote sensing image patches as model input. Specifically, because pegmatite dikes are small targets characterized by slender structures, indistinct boundaries, and sparse regional distribution, this study introduced a lightweight feature extraction structure (GhostNetV2) and a long-range dependency attention module (DFC) at the encoder stage, and further incorporated the Coordinate Attention module (CA) to enhance spatial localization and boundary representation of the targets. Finally, focal cross-entropy loss and a deep supervision strategy were adopted to improve the accuracy of semantic information extraction for pegmatite dikes, as well as the training stability and segmentation accuracy under class-imbalance conditions. The results show that the proposed model achieved an mIoU of 93.11% and an F1-score of 94.95% on the test set. Compared with existing semantic segmentation models, the proposed model achieved superior performance in both identification accuracy and computational efficiency for pegmatite dikes. In addition, this study delineated 18 potential pegmatite dike enrichment zones in the Yilanlike area, providing technical support for remote sensing-based rare-metal prospecting and geological interpretation in the study area. Full article

The forthcoming joint NASA/ASI (National Aeronautics and Space Administration/Italian Space Agency) Surface Biology and Geology Thermal Infrared (SBG-TIR) mission will operate in a sun-synchronous polar orbit collecting data on a global scale. The mission will acquire thermal infrared observations together with limited visible and near-infrared (VNIR) observations, consisting of two spectral bands and one panchromatic channel. In this context, and particularly given the limited number of VNIR bands, accurate retrieval of Vegetation Fractional Cover (FC) and Leaf Area Index (LAI) is particularly relevant. This is because it enables the synergistic use of VNIR and TIR observations to support vegetation monitoring and surface energy flux estimation during the mission. This study evaluates different machine learning approaches under different configurations for the retrieval of FC and LAI using the VNIR observations expected from the SBG-TIR mission. Synthetic datasets generated with the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) radiative transfer model were used for model training and validation. Different input configurations were tested, including VNIR bands, the panchromatic channel, vegetation indices, and observation geometry variables. Model performance was assessed on independent test data, including uncertainty quantification. The optimal configuration, using Gaussian Process Regression (GPR), achieved RMSE values of 0.046 for FC and 0.053 m²/m² for LAI using a seven-channel input set, while yielding R² values greater than 0.9 for both variables. These results are consistent with previous studies, supporting the validity of the proposed approach. The trained models were subsequently applied to Sentinel-2 and evaluated against GBOV (Ground-Based Observations for Validation) reference measurements and standard Sentinel-2 biophysical products. The results showed strong statistical agreement with the Biophysical Processor implemented in the ESA Sentinel Application Platform (SNAP) toolbox, confirming the robustness of the proposed framework for operational estimation and mapping of FC and LAI in the context of the SBG-TIR space mission. Full article

In many African nations, including Nigeria, groundwater remains the most readily available source of clean water. However, finding and developing these resources in heterogeneous terrain, such as the Federal Capital Territory (FCT), Abuja, is challenging due to the uneven distribution of the aquifers and complex geological settings. Using a GIS-based machine learning approach that incorporates surface and subsurface hydrogeological parameters, this study defines groundwater potential zones (GWPZ). Nine conditioning factors were derived from remote sensing, geophysical and climatic datasets. Aquifer thickness, depth to bedrock, geology, rainfall, slope, LULC, lineament density, drainage density and distance from river were among these variables. Three machine learning models: Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Random Forest (RF) were trained and validated using 2410 borehole records (productive and abortive). Hold-out validation (80:20), 10-fold cross-validation, ROC-AUC, and confusion matrix were used to assess each model’s performance. The ensemble models outperformed the SVM, achieving higher predictive accuracy and better generalisation (XGBoost: 0.89, RF: 0.88 and SVM: 0.87). The generated maps categorised the study area into five GWPZs: very high, high, moderate, low and very low. These findings provide a scientific foundation for groundwater exploration and sustainable water resource management in the study area. Full article

In this paper, we provide new insight into the Quaternary tectonics of the Floridia Graben (southeastern Sicily) and develop 3D geologic and ground-flow models of its subsurface. The Floridia Graben is a structural depression bounded by NW–SE trending normal faults and represents the main water reservoir that supplies the city of Siracusa (southeastern Sicily) and its countryside. The knowledge of the subsurface geology and neo-tectonic evolution of the Floridia Graben, as well as the spatial distribution of groundwater volumes is crucial for the management and protection of water resources. Within the government project of the new Italian geological cartography (ISPRA-CARG, Sheet N. 646 Siracusa), field and well data (both publicly available and newly acquired) have been collected and reinterpreted. NW–SE and NE–SW buried tectonic–structural features, inferred in the sub-surface of the graben, are consistent with the orientations of Quaternary faults diffusely observed inside and outside the investigated area. The Quaternary tectonic activity of bounding and buried faults has had a strong influence on the control of the morpho-structural pattern and, consequently, the groundwater flow of the Floridia Graben. The study allowed for the redefinition of the timing of these structures as well as their tectonic–structural control on the graben’s architecture and related water flow. The study represents a valuable tool for the better prediction of the spatial distribution of geologic and hydrogeologic volumes, thus enhancing the efficiency in the management and protection of natural resources. Full article

Groundwater sustains human activity in arid crystalline terrains where surface water is scarce and hydrogeological data are limited. However, most groundwater potential mapping approaches depend on deterministic weighting methods without quantifying model variability. This study describes an uncertainty-aware Remote Sensing and Geographic Information Systems (RS–GIS) framework to delineate groundwater potential zones in the Wadi Arab Watershed, Northeastern Sudan. Nine thematic factors—geology and lithology, rainfall, slope, drainage density, lineament density, soil, land use/land cover, topographic wetness index, and height above nearest drainage—were integrated using the Analytical Hierarchy Process (AHP), with acceptable consistency (Consistency Ratio (CR) < 0.1). To address subjectivity in weights, a Dirichlet-based Monte Carlo simulation (500 iterations) was implemented to perturb AHP weights whilst preserving compositional constraints. The resulting Groundwater Potential Index (GWPI) classified 32.69% of the watershed as high to very high potential, primarily associated with alluvial deposits and fractured crystalline rocks. Model validation using Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve (AUC) of 0.704, indicating acceptable predictive performance. Uncertainty assessment showed low spatial variability (mean standard deviation (SD) = 0.215) and stable exceedance probabilities, verifying the robustness of predicted high-potential zones. The proposed probabilistic AHP framework augments decision reliability and provides a transferable, cost-effective tool for groundwater planning in data-limited arid basement environments. Full article

Water accumulation in goafs in Chinese coal mines is a major hidden hazard that can trigger water inrush accidents and may also affect aquifer integrity and regional water security. Reliable delineation of goaf water distribution and identification of water-source types are therefore essential for mine water-hazard control and groundwater protection. This paper reviews the main technical routes for goaf groundwater investigation, including geophysical prospecting, hydrogeochemical and isotopic identification, direct inspection tools, and data-driven intelligent workflows. For geophysical detection, the mechanisms, engineering applicability, and key constraints of the Transient Electromagnetic Method (TEM), Surface Nuclear Magnetic Resonance (NMR), the High-Density Resistivity Method (HDRM), and the Coherent Frequency Component (CFC) electromagnetic wave reflection coherence method are synthesized, with emphasis on interpretation boundaries and uncertainty sources under complex geological conditions. For source identification, conventional hydrochemistry, stable isotopes, and laser-induced fluorescence are summarized, and intelligent recognition models such as neural networks and support vector machines are discussed in terms of workflow positioning and practical performance limits. A unified evaluation rationale is established and a semi-quantitative method–metric matrix is constructed to compare techniques in terms of reliability, deployability, cost level, environmental adaptability, and information value, thereby clarifying their functional roles and complementarities within staged engineering workflows. The synthesis indicates that major bottlenecks include limited deep capability under strong interference, pronounced interpretational non-uniqueness caused by complex geology and irregular goaf geometries, and constrained timeliness and generalization for mixed-source identification. Future directions are summarized as multi-method integration with fusion-driven interpretation, intelligent and quantitative decision support with quality control, and sensor–platform advances enabling more practical three-dimensional investigation, aiming to improve the reliability and engineering usability of goaf groundwater hazard assessment. Full article

Understanding nitrogen distribution in spring-fed karst rivers is important for interpreting ecosystem responses in populated Mediterranean landscapes. Nitrogen, in its various forms, is a key physicochemical quality element influencing biological communities and ecological quality of freshwater ecosystems. Elevated nitrogen availability may trigger eutrophication and other processes associated with biodiversity loss, posing risks to both aquatic ecosystem integrity and drinking water quality. However, translating nitrogen measurements into effective monitoring and management strategies remains challenging. Monitoring programs are often resource-intensive and require site-specific adaptation, particularly in heterogeneous systems such as karst catchments. General guideline values may not fully capture local hydrological variability, groundwater–surface water interactions, or combined stressors, including nutrient mixtures and salinity intrusion. These factors introduce uncertainty and complicate the interpretation of nitrogen dynamics. This pilot-scale exploratory study assessed total nitrogen (TN) across four environmental matrices—water and sediments, as well as tissue TN in aquatic bryophytes, and in benthic macroinvertebrates—at four spring-fed sites within the Koiliaris River Basin (Crete, Greece). The Koiliaris Critical Zone Observatory (CZO) is a representative karst watershed with highly permeable carbonate geology and long-term human pressures. TN concentrations were low in water (0.9–1.4 mg/L) and sediments (0.2–1.1 g/kg) but substantially higher in biotic compartments, particularly in macroinvertebrates (29.8–47.1 g/kg), while moss tissue TN ranged between 16.9 and 20.4 g/kg. Spatial variability among sites was observed, with consistently higher TN values at the coastal spring influenced by seawater intrusion. Although the limited sample size precluded formal statistical inference, exploratory analyses indicated positive associations between water TN and tissue TN in mosses and macroinvertebrates. These preliminary findings suggest that dissolved nitrogen may represent an important pathway of nitrogen availability to aquatic biota in this karst system. The study provides an exploratory framework for integrating abiotic and biotic nitrogen measurements and may inform the design of future, larger-scale investigations in Mediterranean spring-fed rivers. Full article

The Monterrey Metropolitan Area (MMA), the largest urban and industrial center in northeastern Mexico, faces increasing groundwater stress driven by rapid urban expansion, recurrent drought, and limited surface-water availability. Since 2024, the San Juan River has been considered a potential source of treaty water under the 1944 U.S.–Mexico Water Treaty, further intensifying pressure on regional water resources. This study evaluates changes in groundwater recharge potential between 1990 and 2022 using an integrated Remote Sensing–Geographic Information System framework combined with the Analytic Hierarchy Process. Eight thematic layers—geology, structural lineaments, slope, geomorphology, precipitation, drainage density, Normalized Difference Vegetation Index, and soil type—were weighted to derive a Groundwater Potential Index and delineate recharge zones. Results show a pronounced redistribution of recharge capacity over 32 years. Very low recharge areas increased by 1021.3 km², while very high recharge zones decreased by 100.4 km². In total, more than 1100 km² experienced degradation in recharge potential, mainly associated with urban growth and land-use change. These findings highlight the urgent need for sustainable groundwater management, stronger land-use planning, and protection of recharge areas. Coordinated action among stakeholders and robust regulatory enforcement will be essential as the region navigates future growth and international water obligations. Full article

To address the challenges of designing geologically compatible, large-scale gas drainage strategies in gassy coal mines, this study introduces an integrated workflow combining detailed gas-geological unit subdivision with the Analytic Hierarchy Process (AHP) for the Baode Coal Mine. This approach aims to transform gas drainage technology selection from empirical judgment to a systematic, quantitative decision-making process, thereby enhancing control precision and mine safety. First, the No. 8 coal seam was refined into ten distinct gas-geological units (II-i to II-x), forming the foundation for a targeted management strategy. For these units, a quantitative evaluation index system was constructed, integrating key factors such as permeability, structural characteristics, and unit area. The AHP was then employed to assess the adaptability of four primary drainage technologies: ULB-uni/bi (underground long borehole unidirectional/bidirectional drainage), UULB (underground ultra-long directional borehole drainage), UDLB-SHF (underground directional long borehole drainage with staged hydraulic fracturing), and FHWS (fractured horizontal wells drilled from the surface). The decision analysis reveals significant regional differentiation in technical suitability. FHWS ranks highest in structurally complex and water-rich zones. UDLB-SHF and UULB serve as viable, cost-effective alternatives to FHWS in various scenarios, with UULB being particularly advantageous for “large-area pre-drainage” in extensive panels with relatively simple geology. ULB-uni/bi is confirmed as the most economical option but is suitable only for minor blocks with simple conditions. Consequently, the study proposes a hierarchical, zone-specific strategy: prioritizing surface-based FHWS for high-risk zones, employing UDLB-SHF for active permeability enhancement in low-permeability resource-rich areas, utilizing UULB for efficient large-area drainage, and restricting ULB-uni/bi to small, geologically normal blocks. Ultimately, this research establishes a robust technical selection system that integrates fine geological subdivision, AHP-based multi-criteria evaluation, and targeted technology matching. It provides a scientific basis for balancing risk control and cost optimization in gas drainage design for the Baode Coal Mine. In summary, the methodological framework proposed in this study provides a systematic approach for coal mine gas control under complex geological conditions. Its core value lies in achieving the unity of scientificity and practicality in gas control technology decisions through standardized analysis logic and differentiated adaptation mechanisms, thereby providing support for the precise and efficient development of coal mine gas control. Full article

Martian crater detection plays a critical role in landing-site selection and route planning, and it directly influences whether a mission can be executed safely and whether scientific observations and surface material information can be reliably acquired and returned. These outcomes, in turn, affect subsequent investigations of Martian geology and environmental evolution. Despite the progress of recent detectors, missed detections still occur, especially for medium-to-large craters with weak or blurred rims and for small overlapping craters. At the same time, practical deployment often requires a lightweight architecture so that computational cost can be controlled under real operational constraints. To address these issues, we propose SKE-YOLO11, a lightweight model designed for robust crater detection. First, we construct KWConv and C3K2_KW to replace the standard convolution and the C3K2 module in YOLO11n. This design reduces computation while strengthening feature extraction for small craters. Second, we introduce a Strip Convolution Module (SCM) to enlarge the effective receptive field, which helps the network learn rim and texture cues of medium-to-large craters and reduces missed detections. Third, considering the geometric characteristics of crater annotations, we fuse EIoU and Inner-IoU into an Inner-EIoU loss to replace the CIoU used in YOLO11n, thus improving bounding-box regression. Experiments show that SKE-YOLO11 achieves 83.0% Precision, 75.0% Recall, and 82.8% mAP@0.5. Compared with YOLO11n, Recall and mAP@0.5 improve by 2.3% and 1.8%, and parameters and GFLOPs decrease by 0.08 M and 1.3, respectively. Full article

This study investigates how incorporating physical constraints can enhance the performance of machine learning models by ensuring that geotechnical drilling data predictions align with known physical conditions at the site. Machine learning-predicted soil property point cloud data has significant value for geotechnical project planning. The base model was trained on extensive borehole datasets of soil properties collected from an area of 32,133 square km covering five distinct physiographical regions. To incorporate physics-based constraints, a custom loss function was defined to penalize the model training loss whenever it violates known physical principles. Two distinct types of machine learning models—classification and regression models—are considered in this study for categorical and numerical geotechnical drilling datasets, respectively. Feature variables play a critical role in determining the accuracy of machine learning models and feature variables including location, geology, surface elevation, soil parent material, physiographical information (codes) and soil layer depth are adopted for training the machine learning models after parametric study of various feature variable combinations. Two case studies were conducted to demonstrate the effectiveness of incorporating physical constraints into machine learning models for categorical and regression datasets respectively. The study results demonstrate strong potential for applying physics-constrained machine learning models to generate reasonable estimated values across large regions, while also providing a better understanding of the historical data within the geotechnical drilling inventory. Full article

Enhancing the sustainability and safety of rock engineering requires understanding how micro-structural and alteration conditions influence the geomechanical properties of rocks in geotechnical projects. The determination of surface hardness using the Schmidt Hammer is an interdisciplinary experimental method employed in mining, geology, and civil engineering. This study quantitatively evaluates the effects of surface roughness, weathering degree, and evaluation procedures on Schmidt rebound values obtained from basalt and lapilli tuff. Field measurements on eight surfaces produced rebound values between 10 and 60, with standard deviations ranging from 2.4 to 11, reflecting substantial variability related to roughness and alteration. Laboratory results showed that cut surfaces yielded the highest hardness values (Mean ≈ 57–58) with very low variability (SD ≈ 1.1–1.6), whereas natural surfaces exhibited markedly lower rebound values (Mean ≈ 19–22) and greater scatter (SD ≈ 4–4.5). A strong correlation (R² > 0.97) was observed between JRC roughness and rebound values in laboratory-prepared samples. The percentage difference among the USBR, ASTM, and Sumner & Nel methods remained below 5% when the standard deviation of measurements was under 2, indicating that method selection becomes critical only for heterogeneous surfaces. Mineralogical heterogeneity further increased variability in lapilli tuff, whereas basalt provided highly consistent responses. Overall, this study introduces quantitative thresholds linking roughness, weathering, and statistical variability, offering a more rigorous and reproducible framework for interpreting Schmidt hardness measurements. Full article

The Cuitzeo Groundwater Flow System, located in central Mexico within a volcanic rock region, encompasses two of the largest lakes in the country: Lake Cuitzeo and Lake Pátzcuaro. These lakes are sustained by both surface water and groundwater discharge, playing a critical role in local ecosystems and the surrounding population. Groundwater is particularly important for maintaining the lakes’ existence. However, the behavior of the groundwater flow system in this region has not been previously described. This study compiles historical data from 170 groundwater sites within the system from different years and includes temperature (°C), pH, total dissolved solids (TDS), major ions, and geology in detail. The historical data provide a spatial analysis and initial characterization to study the hydrochemistry of the system, identify recharge and discharge zones, assess water-rock interaction processes, and trace the evolution of groundwater. The results highlight distinct chemical behaviors across the different zones of the study area, with the most notable being ion exchange consistent with the weathering of volcanic silicates and interaction with lacustrine sediments. This study is crucial as it offers valuable insights into the hydrochemistry and water levels of the groundwater flow system and highlights areas where additional data are needed to better understand its dynamics. Full article