Search Results (526)

The Mabi Block is located in the southern Qinshui Basin, representing an underexplored region with high-rank coal seams that host significant Coalbed Methane (CBM) potential. Despite extensive CBM development in the nearby Anze and Zheng Zhuang blocks, the geological and geophysical controls on Coalbed Methane enrichment in Mabi remain insufficiently constrained. This study integrates the core data (63 samples) of isothermal adsorption tests, well-logging data from (13 wells), and 3D seismic attributes to systematically evaluate the key controlling factors, such as burial depth, roof and floor lithology, and sealing capacity, in the horizons of the No.3# and No.15# coal seams. Lithology is characterized using natural gamma ray (GR), acoustic (AC), deep resistivity (RD), compensated neutron log (CNL), and seismic wave impedance inversion. Coal quality parameters, ash content, and the Langmuir volume (V_L) are correlated with gas content, and structural controls are mapped using curvature, fault interpretation, and burial depth analysis. The results show that thick mudstone and limestone roofs, moderate burial depth (1100–1350 m), synclinal structural lows, and thicker coal seams (6–9 m) collectively enhance methane preservation. The ash content (%) exhibits a moderate negative correlation with the Langmuir volume (R² = 0.4) and gas content. Structural curvature (syncline) and fault intensity strongly govern lateral sealing integrity, where anticline zones and faulted regions display notable degassing. This integrated assessment contributes to a refined CBM optimization model for the Mabi Block and guides targeted future drilling, reservoir evaluation, and production optimization. Full article

Measure-While-Drilling (MWD) data provide real-time insight into subsurface conditions and drilling performance, yet their complexity and operational noise often hinder reliable modeling. This study demonstrates the role of Exploratory Data Analysis (EDA) in developing robust machine learning (ML) models for lithology classification and penetration rate (PR) prediction in mining operations. A structured EDA workflow—comprising data integrity assessment, feature distribution analysis, correlation mapping, and depth-wise parameter profiling—was implemented to identify redundant attributes, isolate non-productive intervals, and enhance dataset consistency. Through EDA-informed normalization and feature selection, data consistency and model performance were significantly improved. Machine learning algorithms, including Decision Tree, Random Forest, and Multi-Layer Perceptron, were trained on the refined dataset. The Random Forest Classifier achieved 98.45% accuracy in lithology prediction, while the Random Forest Regressor produced the most accurate PR estimation (R² = 0.83, RMSE = 0.52). These results highlight EDA as a critical foundation for constructing physics-informed, data-driven models that enhance predictive reliability and operational efficiency in mining environments. Full article

Floods represent one of the most frequent and damaging natural hazards in Mediterranean mountain regions, where intense rainfall and complex topography amplify runoff and inundation risk. This study aims to delineate flood-susceptible zones in the Monti Lucretili area of central Italy, an environmentally sensitive and culturally significant landscape that hosts archeological remains and UNESCO listed dry-stone heritage using an integrated Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) approach. Fifteen (15) conditioning factors, including elevation, slope, rainfall, soil, lithology, land use/land cover, drainage density, and proximity to rivers and roads, were derived from open-access satellite remote sensing and spatial datasets. The AHP model produced a flood susceptibility index ranging from 1.806 to 4.465, reclassified into five categories from very low to very high zones. The resulting map indicates that low- and moderate-susceptibility zones dominate the study area, while high and very high classes are primarily concentrated along valleys and drainage corridors. Model validation indicates strong regional-scale predictive performance, with 85.36% of modeled flood-prone areas located within high- to very-high-susceptibility zones and an AUC value of 0.82. Overall, the study highlights the potential of open-access AHP–GIS modeling as a practical screening tool for flood susceptibility assessment and heritage-aware spatial planning in Mediterranean environments. Full article

Harmonised land evaluation frameworks are essential for sustainable land planning and policy development. Assessing land suitability is crucial for predicting agricultural and forestry potential but also for mitigating land degradation risks. Current land suitability maps in Portugal vary greatly in scale and methodology. This study presents the first nationally consistent framework to produce a harmonised land suitability map for mainland Portugal at a 1:100,000 scale following a recently updated WRB soil map. The latter was obtained by integrating legacy soil data with delineated land units according to soil-forming factors (climate, lithology, and relief). These land units were used to derive key land qualities, subsequently classified into constraint levels. Following FAO land evaluation principles, four land suitability levels for agriculture and forestry were assigned to 125 land units across three representative areas in southern Portugal. Relief and lithology emerged as main drivers of land suitability. Marginal agricultural lands are largely dominant (65.1–78.0%), followed by non-suitable lands (14.8–28.3%). Forestry suitability is mostly confined to moderate (61.5–69.4%) and marginal (30.6–37.4%) classes, reflecting the higher adaptability of forestry systems. High consistency was observed between the derived suitability classes and the latest land use/land cover map of Portugal. The framework enables decision-makers to identify areas suitable for intensive production while safeguarding lands vulnerable to degradation. It also provides a transferable tool for adaptive landscape management and sustainable land allocation, supporting policy development under changing environmental conditions in Mediterranean regions. Full article

The McMurdo Dry Valleys (DVs) of South Victoria Land, Antarctica, constitute the largest ice-free region on the continent and one of Earth’s most Mars-analog environments. Their hyper-arid polar desert conditions offer a unique setting for investigating surface weathering and mineralogical processes under extreme climates. This study presents the first regional-scale mapping of alteration and crystalline weathering minerals across the McMurdo DVs. It uses Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral data; visible and near-infrared (VNIR) and shortwave infrared (SWIR) bands were analyzed through a Spectral Hourglass Workflow, endmember extraction, and spectral unmixing with Matched Filtering (MF) and Constrained Energy Minimization (CEM). Inter-algorithm consistency analysis between MF and CEM yielded 78.83% overall agreement with a Kappa coefficient of 0.75, indicating strong methodological consistency in mineral discrimination using ASTER VNIR+SWIR data. It should be noted that this agreement reflects internal algorithmic robustness rather than independent geological validation. Geological reliability is instead supported by documented field observations, lithological map comparisons, and spectral correspondence with the USGS spectral library. Validation employed documented field observations, lithological maps, and the USGS spectral library. Results reveal distinct spatial distributions of hematite-limonite/goethite, jarosite, kaolinite/smectite-illite-pyrophyllite-alunite, muscovite, hydrous silica/sericite/jarosite/hematite, epidote/chlorite, and calcite, closely associated with lithological units and unconsolidated deposits in Taylor, Wright, Victoria, and McKelvey Valleys. An inter-algorithm consistency check achieved 78.83% overall accuracy with a Kappa coefficient of 0.75, underscoring the robustness of ASTER VNIR+SWIR data for Antarctic mineral discrimination despite localized spectral mixing. Beyond refining the geological understanding of the McMurdo DVs, these results establish ASTER as an effective tool for regional mineralogical mapping in inaccessible polar terrains. The findings further strengthen the role of the Dry Valleys as a terrestrial analog for Mars, where similar mineralogical assemblages and spectral ambiguities have been observed, thereby contributing to both Antarctic geoscience and planetary exploration frameworks. Full article

Floods are natural disasters that cause significant socioeconomic and environmental losses in both urban and rural areas. Within the framework of spatial planning, precautionary measures against flood hazards can be developed using analytical approaches based on different modeling techniques. In this study, flood-prone areas in the Melen Basin, Türkiye, were identified and mapped using five statistical methods, namely Frequency Ratio (FR), Shannon Entropy (SE), Evidential Belief Function (EBF), and the hybrid models EBF–SE and EBF–FR. The analysis was conducted using a flood inventory and environmental datasets covering the period 2019–2024, including elevation, slope, aspect, land use, plan and profile curvature, drainage density, distance to river, curve number, long-term average precipitation, geological formation, soil depth, topographic wetness index, sediment transport, and stream power index. Model performances were evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC). The results indicate that the SE method achieved the highest predictive performance (AUC = 0.979), followed by FR (0.974), EBF–SE (0.972), EBF–FR (0.968), and EBF (0.966). According to the FR and SE models, elevation, lithology, and slope were identified as the most influential factors in flood occurrence. In the evaluation of the success index of the models, the following values were determined according to their size: EBF–SE (96.0), SE (94.4), EBF (91.8), FR (81.9), and EBF–FR (79.4). In the classification of flood sensitivity maps, Natural Breaks (Jenks) is the most successful method according to the success index. The findings demonstrate that data-driven and hybrid models can effectively support flood risk assessment and provide valuable input for land-use planning and flood risk management. Full article

Real-time lithology identification during drilling faces challenges such as indistinct boundaries and difficulties in feature extraction. To address these, this study proposes the MSC-Transformer, a novel model integrating time-frequency features with a deep neural network. A series of drilling experiments were conducted using an intelligent drilling platform, during which triaxial vibration signals were collected from five types of rock specimens: anthracite, granite, bituminous coal, sandstone, and shale. Short-time Fourier Transform (STFT) was applied to generate multi-channel power spectral density (PSD) maps, which were then fused into a three-channel tensor to preserve directional frequency information and used as inputs to the model. The proposed MSC-Transformer combines a multi-scale convolutional (MSC) module with a lightweight Transformer encoder to jointly capture local texture patterns and global dependency features, thereby enabling accurate classification of complex lithologies. Experimental results demonstrate that the model achieves an average accuracy of 98.21 ± 0.49% on the test set, outperforming convolutional neural networks (CNNs), visual geometry group (VGG), residual network (ResNet), and bidirectional long short-term memory (Bi-LSTM) by 5.93 ± 0.90%, 2.54 ± 1.11%, 6.38 ± 2.63%, and 10.56 ± 3.11%, respectively, with statistically significant improvements (p < 0.05). Ablation studies and visualization analyses further validate the effectiveness and interpretability of the model architecture. These findings indicate that lithology recognition based on time-frequency representations of vibration signals is both stable and generalizable, offering technical support for real-time intelligent lithology identification during drilling operations. Full article

Groundwater, a vital resource for drinking water supply, must be managed sustainably to ensure its availability and quality. In France, the SISE-Eaux database on water intended for human consumption, archived by the Regional Health Agencies (ARS) since 1990, constitutes a rich source of information. This study focused on the groundwater of the Nouvelle-Aquitaine region, the largest administrative region in metropolitan France, covering 84,061 km² with 6 million inhabitants. It is based on a 22-year data extraction, resulting in a matrix of 121,649 observations and 51 physico-chemical and bacteriological parameters. Following logarithmic transformation of the data and fitting of variograms using the mean value of each parameter for each sampling point, the spatial distribution of numerous parameters across the region is presented. From this initial sparse matrix, a dense matrix of 23,319 samples (rows) and 15 key parameters (columns) was selected for a multivariate approach. A Principal Component Analysis (PCA) was used to condense the information and create summary maps capturing over 68% of the information contained in the dense matrix. The combined results of the multivariate analysis (dense matrix) and the distribution of individual parameters (sparse matrix) highlight the diversity of sources contributing to the spatial variability of groundwater, such as the role of lithology, the origin and pathways of fecal contamination, and the influence of redox processes. Neither the large size of the study area nor the high number of parameters proved to be an obstacle to the analysis. The understanding of ongoing processes and the factorial axis distribution maps, which enable the spatial representation of these mechanisms, can be used to facilitate groundwater monitoring and protection. Full article

This study addresses a critical knowledge gap in quantifying strategic mineral resources within hyper-arid, tectonically complex terrains by establishing a recursive framework that reconciles deterministic resource estimation with the nonlinear dynamics of tectonically mediated metamorphic systems. Using Libya’s Wadi El Shati as a case study, legacy lithological misclassifications are rectified through the fusion of Sentinel-1 Synthetic Aperture Radar, Sentinel-2 multispectral imagery, and Digital Elevation Model analytics within a unified geospatial workflow. The methodology synergizes atmospherically corrected optical data, processed via supervised Maximum Likelihood Classification, with calibrated radar-derived structural lineaments. Classified marble-bearing zones within the Al Mahruqah Formation are integrated with DEM data and field-validated thickness measurements using Triangulated Irregular Network models to resolve surface–subsurface dependencies and compute volumes. The results demonstrate a 91% lithological classification accuracy, rectifying a 22% error in legacy maps. Structural analysis of 1213 lineaments confirms a dominant NE–SW extensional regime (σ₃) that facilitated fluid conduits. The quantified marble-bearing horizon spans ~334 km² with a volume of 6.0 km³ (±9%). Spatial analysis reveals a causal link between high-grade marble clusters, basaltic intrusions, and NE–SW fault systems, refining models of contact metamorphism in rift-related settings. Full article

Accurate lithologic maps derived from satellite imagery underpin structural interpretation, mineral exploration, and geohazard assessment. However, automated mapping in complex terranes remains challenging because spectrally similar units, narrow anisotropic bodies, and ambiguous contacts can degrade boundary fidelity. In this study, we propose SegNeXt-HFCA, a hierarchical multiscale fusion network with coordinate attention for lithologic segmentation from a Sentinel-2/DEM feature stack. The model builds on SegNeXt and introduces a hierarchical multiscale encoder with coordinate attention to jointly capture fine textures and scene-level structure. It further adopts a class-frequency-aware hybrid loss that combines boundary-weighted online hard-example mining cross-entropy with Lovász-Softmax to better handle long-tailed classes and ambiguous contacts. In addition, we employ a robust training and inference scheme, including entropy-guided patch sampling, exponential moving average of parameters, test-time augmentation, and a DenseCRF-based post-refinement. Two study areas in the Beishan orogen, northwestern China (Huitongshan and Xingxingxia), are used to evaluate the method with a unified 10-channel Sentinel-2/DEM feature stack. Compared with U-NetFormer, PSPNet, DeepLabV3+, DANet, LGMSFNet, SegFormer, BiSeNetV2, and the SegNeXt backbone, SegNeXt-HFCA improves mean intersection-over-union (mIoU) by about 3.8% in Huitongshan and 2.6% in Xingxingxia, respectively, and increases mean pixel accuracy by approximately 3–4%. Qualitative analyses show that the proposed framework better preserves thin-unit continuity, clarifies lithologic contacts, and reduces salt-and-pepper noise, yielding geologically more plausible maps. These results demonstrate that hierarchical multiscale fusion with coordinate attention, together with class- and boundary-aware optimization, provides a practical route to robust lithologic mapping in structurally complex regions. Full article

This study utilizes Digital Elevation Models (DEMs) with different spatial resolutions (SRTM 90 m, ASTER DEM 30 m, and ALOS PALSAR 12.5 m), Landsat-8 satellite imagery, and the Bouguer WGM-12 gravity model to analyze morphotectonic features in the Upper Guajira region of Colombia, a desert area in northern South America, area that is composed by low-relief serranías of Cabo de la Vela, Carpintero, Cosinas, Simarua, Jarara, and Macuira. Three DEMs were used to extract and map morphotectonic lineaments, drainage networks, and morphological features. Lineaments were characterised by azimuth frequency, length, density, lithological distributions, and geological timeframes, with support from a digitized geological map from the Colombian Geological Service (SGC). The analysis of the east–west (E-W) Cuisa fault, using the Riedel shear model, suggests a transtensional/transpressional tectonic regime influenced by the Caribbean and South American plates, characterised by NE-SW and E-W fault orientations. Lineaments were grouped into five geochronological categories based on the geological map, revealing a shift from NE-SW to E-W orientations from the Cretaceous period onward, reflecting the ongoing movement of the Caribbean plate. Folds and faults from this tectonic activity were enhanced using Landsat-8 band combinations. The WGM-12 model was separated into regional and residual signals, with the latter highlighting the serranías subregions. Residual gravity analysis revealed significant negative anomalies, suggesting lower-density lithologies surrounded by higher-density blocks. This pattern aligns with the regional geological framework and may reflect a crustal root or terrain dragging linked to the tectonic processes that shaped the serranías. Derivative residual gravity data also revealed lineaments oriented NE–SW, whose distribution extends beyond the morphometric boundaries of the subregions. The study found a strong correlation between structural and drainage patterns, demonstrating structural control over geomorphology. This study establishes a solid morphotectonic and geophysical framework for the Upper Guajira region, demonstrating how multi-resolution DEM analysis combined with gravity data can resolve regional deformation patterns, crustal architecture, and tectonic development along the Caribbean–South American plate boundary. Full article

The mineral-mapping capability of three spaceborne sensors with different spatial and spectral resolutions, the Environmental Mapping and Analysis Program (EnMap), Sentinel-2, and World View-3 (WV3), is assessed regarding bauxite mining wastes in Amphissa, Greece, with validation based on ground samples. We applied the well-established Linear Spectral Unmixing (LSU) and Spectral Angle Mapping (SAM) classification techniques utilizing endmembers of two established spectral libraries and incorporated ground data through geochemical and mineralogical analyses, X-ray fluorescence (XRF), Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS), and X-ray Diffraction (XRD), to assess classification performance. The main lithologies in this area are bauxites and limestones; therefore, aluminum oxyhydroxides, calcite, and iron oxide minerals were the dominant phases as indicated by the XRF/XRD results. Almost all target minerals were mapped with the three sensors and both methods. The performance of EnMap is affected by its coarser spatial resolution despite its higher spectral resolution using these methods. Sentinel-2 is most effective for mapping iron-bearing minerals, particularly hematite, due to its higher spatial resolution and the presence of diagnostic iron oxide absorption features in the VNIR. World View 3 Shortwave Infrared (WV3-SWIR) performs better when mapping calcite, benefiting from its eight SWIR spectral bands and very high spatial resolution (3.7 m). Hematite and calcite yield the highest accuracy, especially with SAM, indicating 0.80 for Sentinel-2 (10 m) for hematite and 0.87 for WV3-SWIR (3.7 m) for calcite. AlOOH shows higher accuracy with SAM, ranging from 0.57 to 0.80 across the sensors, while LSU shows lower accuracy, ranging from 0.20 to 0.73 across the sensors. This study showcases each sensor’s ability to map minerals while also demonstrating that spectral coverage and the spatial and spectral resolution, as well as the characteristics of the selected endmembers, exert a critical influence on the accuracy of mineral mapping in mine waste. Full article

Ataúro Island, located in the inner Banda Arc, provides a natural laboratory to investigate the interplay between magmatic evolution, hydrothermal circulation, and near-surface weathering in an active arc–continent collision setting. This study presents the first systematic island-wide geochemical baseline for Ataúro Island, based on multi-element analyses of stream sediments integrated with updated geological, structural, and hydromorphological information. Compositional Data Analysis (CoDA–CLR–PCA), combined with anomaly mapping and spatial overlays, defines a coherent three-tier geochemical framework comprising: (i) a lithogenic component dominated by Fe–Ti–Mg–Ni–Co–Cr, reflecting the geochemical signature of basaltic to andesitic volcanic rocks; (ii) a hydrothermal component characterized by Ag–As–Sb–S–Au associations spatially linked to structurally controlled zones; and (iii) an oxidative–supergene component marked by Fe–V–Zn redistribution along drainage convergence areas. These domains are defined strictly on geochemical criteria and represent geochemical process domains rather than proven metallogenic provinces. Rare earth element (REE) systematics further constrain the geotectonic setting and indicate that the primary geochemical patterns are largely controlled by lithological and magmatic differentiation processes. Spatial integration of geochemical patterns with fault architecture highlights the importance of NW–SE and NE–SW structural corridors in focusing hydrothermal fluid circulation and associated metal dispersion. The identified Ag–As–Sb–Au associations are interpreted as epithermal-style hydrothermal geochemical enrichment and exploration-relevant geochemical footprints, rather than as evidence of confirmed or economic mineralization. Overall, Ataúro Island emerges as a compact natural analogue of post-arc geochemical system evolution in the eastern Banda Arc, where lithogenic background, hydrothermal fluid–rock interaction, and early supergene processes are superimposed. The integrated geochemical framework presented here provides a robust baseline for future targeted investigations aimed at distinguishing lithogenic from hydrothermal contributions and evaluating the potential significance of the identified geochemical enrichments. Full article

The Santa Elena Peninsula has experienced local subduction earthquakes in 1901 (7.7 Mw) and 1933 (6.9 Mw), during which local ground conditions, including deposits of longshore-current sediments, paleo-lagoon or marsh, sandspit, and ancient tidal channel sediments, exhibited various coseismic deformation behaviors in Quaternary soils of inferior geotechnical quality. This study shows that geophysical profiles from seismic refraction and shear-wave velocities are correlated with stratigraphic data from sedimentary sequences obtained from slope cutting and geotechnical drilling. This database is used to create a comprehensive map to describe the lithological units of Salinas’ urban geology. The thickness of the Tertiary–Quaternary sedimentary sequences and the depth to the bedrock of the Piñon and Cayo geological formations determine the periods of sites in these stratigraphic sequences, which range from 0.3 to 1.5 s. This study provides the first geotechnical zoning map for the city of Salinas at a scale of 1:25,000, which is a technical requirement of the Ecuadorian construction standard. This geotechnical zoning information is essential for appropriate land management in Salinas and its neighboring cities, La Libertad and Santa Elena, as well as for outlining municipal restrictions on future construction. Full article

Gabal (G.) Suwayqat El-Arsha contains two distinct phases of granitoids: I-type granodiorite and A-type monzogranite. Both of them experienced intense fractional crystallization that affected plagioclase, alkali feldspar, quartz, and, to a lesser degree, ferromagnesian minerals. EnMAP hyperspectral data were used to discriminate between the different granitoid types through spectral analysis, using various techniques, including the Sequential Maximum Angle Convex Cone (SMACC) method. Granodiorite has high SiO₂ (68.21–71.44 wt%), Al₂O₃ (14.29–14.92 wt%), Fe₂O₃ (1.99–3.32 wt%), and CaO (2.34–3.87 wt%), whereas monzogranite has even higher SiO₂ (73.58–75.87 wt%) and K₂O (4.28–4.88 wt%). Both granodiorite and monzogranite exhibit calc-alkaline, peraluminous to metaluminous, and medium- to high-K characteristics, with attendant enrichment of light REE and LILE and depletion of heavy REE and HFSE. A negative Eu anomaly may indicate early plagioclase fractionation, especially in the monzogranite. The I-type granodiorite is likely derived from a high-K, mafic protolith that partially melted during lithospheric delamination, leading to severe fractional crystallization in the upper crust in a post-collisional environment. In contrast, the monzogranite exhibits A-type characteristics and was likely emplaced in an anorogenic setting. Both granites were affected by several episodes of hydrothermal alteration, resulting in silicification, kaolinitization, sericitization, and chloritization. The intrusions studied here exhibit key similarities with those in the Wadi El-Hima area, including tectonic setting, petrogenetic type, Neoproterozoic age (Stage I collisional: ca. 650–620 Ma; Stage II post-collisional: ca. 630–590 Ma), and mineralogical assemblages (notably two-mica granites). These correlations suggest that both suites form part of a regionally extensive batholith composed of I- and A-type granites, stretching from north of the Marsa Alam Road (Umm Salatit–Homrit Waggat) southward to at least Wadi El-Hima. Full article