Search Results (541)

This study evaluates the landslide susceptibility of Zonguldak Province, Türkiye, by integrating the Analytical Hierarchy Process (AHP), artificial intelligence (AI) algorithms, and SBAS-InSAR deformation data. Eight environmental and geological parameters—elevation, slope, aspect, lithology, hydrogeology, land use, and distances to rivers and roads—were weighted using AHP and analyzed through 25 AI models. Among them, the Ensemble Bagged Trees (EBT) algorithm achieved the highest predictive accuracy (84%), demonstrating strong adaptability to complex geological datasets. The resulting susceptibility maps were validated using both traditional landslide inventories and InSAR-derived deformation maps, achieving an overall agreement of 83.05%. This dual-validation approach allows for the identification of unrecorded or active slope movements not captured in existing inventories. The combined use of AHP and AI significantly improves model reliability by incorporating both expert judgment and data-driven learning. The study introduces a novel hybrid framework for landslide susceptibility mapping and provides a valuable reference for disaster risk management and spatial planning in regions with complex topography. This study also contributes to sustainability by supporting risk-informed land-use planning, reducing potential economic losses, and enhancing environmental resilience in landslide-prone regions. The proposed framework aligns with sustainable development goals by integrating geospatial technologies and data-driven approaches for long-term hazard mitigation. Full article

Fuzzy logic-based mineral potential mapping was applied to the Tisová–Klingenthal Cu–Co VMS deposit (Erzgebirge) in the Czech–German border region. The study area is characterized by heterogeneous geological and geochemical datasets derived from differing national surveys and historical mining. Using the Exploration Information System (EIS) toolkit, a knowledge-driven fuzzy logic approach integrated key spatial datasets, including copper and zinc soil and stream sediment anomalies and metabasalt lithology, relevant to Besshi-type VMS deposits. Three prospective anomalies were identified: the historic Tisová mine and two additional targets aligned along the same stratigraphic horizon. Artificial Neural Network (ANN) modelling was limited by insufficient training data, resulting in overfitting and reduced predictive reliability. Follow-up soil geochemical surveys conducted over the largest anomaly returned locally elevated copper values but did not conclusively confirm mineralisation. The results demonstrate that fuzzy logic provides a flexible and interpretable framework for mineral potential mapping in complex, data-scarce environments and highlight the need for iterative modelling and targeted exploration. Full article

Remote sensing offers distinct advantages for lithological mapping, but its ability to detect underlying bedrock is limited in covered areas, whereas geochemical data are constrained by sparse sampling and low spatial resolution. To address these challenges, this study proposes a texture-guided adaptive data fusion framework combined with a Multi-scale Convolutional Neural Network (MCNN) for lithological mapping, using the Guyang area in Inner Mongolia as a case study. First, the non-linear relationships between geochemical components and remote sensing spatial textures are modeled to achieve complementary integration of heterogeneous multi-source data. Second, an MCNN model is constructed to extract multi-scale geological features, enabling improved discrimination of lithological units and more effective inference of concealed bedrock beneath Quaternary cover. Experimental results show that the proposed method overcomes the limitations of single data sources and achieves an overall accuracy (OA) of 0.95 on the fused dataset. Ablation experiments further demonstrate that the texture-guided fusion strategy significantly improves lithological identification performance. This study provides an effective framework for intelligent geological mapping and confirms the feasibility of inferring underlying bedrock in covered areas using multi-source surface information. Full article

Phosphatic deposits from the Lower Cambrian Pedroche Formation (Sierra de Córdoba, Spain) provide key insights into early diagenetic mineralization processes during the Cambrian radiation. This study applies an integrated multi-analytical approach combining Raman spectroscopy, SEM–EDS, LA-ICP-MS, and ToF-SIMS to investigate mineralogical, elemental, and molecular signatures of phosphatized bioclastic carbonates and associated siliciclastic facies from the Los Lagares-1 borehole. Results reveal a systematic phosphatization gradient from carbonate-dominated skeletal rims to phosphate-rich interiors composed of carbonate fluorapatite with variable carbonate and hydroxyl substitution. Trace-element systematics and REE patterns indicate seawater-influenced phosphogenesis under suboxic porewater conditions, coupled to iron reduction and early diagenetic clay mineral formation. In contrast, the siliciclastic siltstone facies preserves poorly crystalline phosphate phases associated with detrital aluminosilicates and chlorite, reflecting distinct porewater chemistry and crystallization kinetics. ToF-SIMS mapping demonstrates spatial coupling between fluorine and phosphate within fossil structures, confirming fluorapatite formation and localized organic matter entombment. These results highlight the strong control of host lithology on phosphate crystallization pathways and trace-element redistribution, and provide new constraints on microbially mediated phosphogenesis in restricted Early Cambrian reef–lagoon systems along the northern Gondwanan margin. Full article

A structurally complex Paleozoic succession in southwestern Sinai hosts uranium and associated metals, and brittle deformation controls fluid flow and ore localization. The study integrates structural mapping with mineralogical, geochemical, and radiometric data to evaluate how fault architecture controls uranium and polymetallic mineral occurrences in the east Abu Zeneima area. Eleven representative samples were collected from major fault zones and host lithofacies, and 652 ground gamma-ray spectrometric measurements were acquired across mineralized localities and Paleozoic stratigraphic units. Heavy mineral separation, SEM–BSE/EDX, X-ray diffraction, and whole-rock geochemistry were used to identify ore and accessory phases and quantify their elemental composition. The middle carbonate member of the Um Bogma Formation is the primary host lithology and contains primary U dispersed within carbonaceous sandy dolostone and locally abundant secondary U phases coexisting with Cu–Fe–Mn phases and REE-bearing silicates and phosphates. Uranium enrichment (locally >2900 ppm eU) in the targeted anomalous samples shows a positive association with P₂O₅ and a weaker positive association with ΣREEs. Together with SEM–BSE/EDX and XRD identification of uranyl phosphates and REE-bearing accessory minerals, these observations suggest that phosphate-bearing secondary phases and REE-rich accessories locally contributed to uranium hosting. Seventy-four radioactive anomalies are predominantly associated with normal faults and are concentrated along fault cores and highly fractured downthrown blocks, especially along a NW–SE trend that forms the main mineralized corridor. The study findings emphasize the importance of fault zone architecture for targeting new uranium resources in Paleozoic basins. Full article

Deep-seated landslide complexes are widespread in soft-rock hill-country landscapes, yet their regional morphometric organisation and controlling factors remain insufficiently quantified. This study uses high-resolution (1 m) airborne LiDAR-derived terrain data integrated with geological and drainage-network datasets to investigate landslide complexes in the eastern Tararua District, New Zealand. A relative, unit-based morphometric framework is applied to compare terrain derivatives (including slope, aspect, and multi-scale relative relief) between mapped landslides and their host geological units. To isolate intrinsic lithological controls from geomorphic influences, the analysis is restricted to landslides occurring entirely within a single geological unit. The results indicate that lithology exerts first-order control on landslide morphometry, while fluvial incision and valley confinement regulate landslide initiation and persistence. Landslides are preferentially associated with low- to mid-order channels, indicating strong hillslope–channel coupling within a young, actively uplifting landscape. A conceptual threshold framework is proposed, showing that landslides develop where lithological susceptibility and relief amplification jointly exceed stability thresholds. By integrating geological information with LiDAR-based morphometric analysis, this study provides a transferable framework for distinguishing instability regimes and improving understanding of sediment dynamics and landscape evolution in soft-rock terrains. 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

This research provides an extensive evaluation of liquefaction induced by earthquakes in the Ionian Islands, specifically Lefkada, Cephalonia, Ithaki, and Zakynthos, through the compilation of a liquefaction inventory and GIS-based liquefaction susceptibility index (LiSI) maps. A total of 49 liquefaction sites from 20 causative earthquakes confirm that liquefaction is a recurrent geohazard in the area, primarily affecting coastal and low-lying areas with unconsolidated post-alpine deposits. The relationship between earthquake magnitude and maximum epicentral distance of observed liquefaction is consistent with global empirical datasets, indicating that moderate to strong earthquakes (M_w = 5.9–7.4) can induce liquefaction at considerable distances. The susceptibility model integrates eleven conditioning variables, classified as geological and geomorphological variables, hydrological indices and optical satellite imagery-derived data, within an analytic hierarchy process (AHP) framework. Lithology, age, and geomorphological unit emerged as the dominant conditioning variables. The LiSI maps confirm the zones previously identified in the inventory. Model validation and sensitivity analysis including confusion matrix components, key performance metrics and ROC analysis in coarser grid sizes demonstrate performance ranging from excellent (Zakynthos) to moderate (Lefkada and Cephalonia), while remaining inconclusive for Ithaki due to data limitations. The model exhibits generally conservative behavior, characterized by high precision and specificity but variable sensitivity, while it is largely stable across spatial resolutions in most cases. Full article

Rainfall-induced landslides represent one of the most recurrent geohazards affecting the transportation network of southwestern Calabria (Italy). This study provides an integrated assessment of landslide occurrence and road damage along the Costa Viola by combining detailed geomorphological mapping, multi-temporal analyses, historical documentation (1950–2025), and GIS-based spatial data processing. A total of 261 landslides were mapped, affecting approximately 19% of the study area. Slides constitute the dominant movement type (66.7%), followed by complex landslides, flows, and falls. Landslide distribution is strongly controlled by geological and morphometric factors: more than 80% of mapped phenomena occur in highly fractured granitic and gneissic rocks, over 70% lie within 500 m of faults, and more than 90% are located within 300 m of streams. Slope gradient (25–55°) and local relief (350–550 m) further contribute to slope instability patterns. The historical dataset documents 237 landslide-induced road damage events over 75 years, with a marked increase in occurrence since the early 2000s. Most damage events affected the SS18 road and frequently corresponded to reactivations of pre-existing landslides, highlighting the long-term persistence of slope instability and the seasonal influence of intense autumn–winter precipitation. Overall, the results demonstrate that landslide hazard in the Costa Viola is governed by the interplay between structural, lithological, geomorphic, and climatic factors, compounded by anthropogenic modifications along road corridors. The combined landslide inventory and historical database provide a robust basis for risk mitigation, identification of critical road sectors, and future susceptibility and predictive modelling to support effective territorial planning. Full article

Given the increasing frequency and intensity of floods, which are mostly caused by continuous climate change and growing human pressures on the environment, accurately identifying areas that are susceptible to flooding is a crucial priority for risk reduction and long-term land use planning. Thus, this research examines multilevel flood susceptibility mapping across North Macedonia, using 328 past flood occurrences, 14 conditioning variables derived from a digital elevation model, simplified lithology, and calculated direct runoff. The methodology integrates fuzzy set theory (Fuzzy), analytic hierarchy process (AHP), weighted linear combination (WLC), and random forest (RF) approaches. The two-stage process employs distinct sets of conditioning factors in sequential flood susceptibility mapping: first, generating Fuzzy/AHP/WLC predictions and pseudo-absence data, and second, producing five RF predictions by varying pseudo-absences and binary cutoffs. Validation results indicate that the very high susceptibility class (0.8–1.0) of the Fuzzy/AHP/WLC model predicted 46.6% of flood pixels within 31.6% of the total area. In comparison, the very high susceptibility class of the RF models predicted 88.5%, 78.3%, 60.6%, 48.5%, and 28.3% of flood pixels within 54.7%, 42.2%, 30.5%, 27.0%, and 25.1% of the total area, respectively. The RF models achieved area under the curve (AUC) values exceeding 0.850, with a maximum of 0.966. Additionally, areas of high and low uncertainty were highlighted using a standard deviation map created from the RF models, highlighting agreement/disagreement and potential locations for methodological improvement and focused sampling. The findings also highlight the potential of the multilevel technique for mapping flood susceptibility and call for more research into its potential for future studies and practical uses. Full article

The hydrothermal-filling-type tremolite jade (nephrite) deposit in sanchakou, Qinghai Province is hosted in marine dolomite, and its ore-forming fluid sources and metallogenic mechanisms remain poorly constrained. Here, we conducted an integrated study involving field geological mapping, petrographic observations, and geochemical analyses (major and trace elements, REEs, Sr isotopes) to constrain material sources, fluid physicochemical features and mineralization processes of the deposit. Results show that the ore-forming fluids were derived from deep crust, with homogeneous initial ⁸⁷Sr/⁸⁶Sr ratios ranging from 0.70949 to 0.70959, distinctly higher than the host dolomite (~0.707683), indicating intensive water–rock interaction with Sr-radiogenic lithologies during fluid upwelling. The host dolomite provided the main Ca and Mg, while Si and partial Mg were sourced from deep Si-Mg rich hydrothermal fluids, with negligible contribution from coeval gabbro. The ore-forming fluids were rich in Si, Mg, large-ion lithophile elements and volatiles (e.g., F⁻), characterized by medium-high to medium-low temperature evolution and fluctuating oxidation states. Mineralization can be divided into four stages: deep fluid generation and migration, infiltration metasomatism and silicification, tremolite crystallization at peak oxidation, and open-space filling and jade precipitation. High-quality tremolite jade mainly formed via pulsed hydrothermal injection and direct crystallization in tectonic fractures. This study establishes a genetic model for hydrothermal-filling-type nephrite, enriching relevant metallogenic theories and supporting subsequent exploration. Full article

The increasing frequency of disasters caused by landslides, mainly due to climate change leading to more intense extreme events, requires reliable predictive models for risk mitigation. Italy, in particular, is a country at high risk of landslides, but the lack of an updated catalogue of landslide activation dates poses a significant challenge for defining reliable activation thresholds. This study develops a methodology for mapping landslide susceptibility based on events in a pilot area of central Italy, integrating a database of landslides with known activation dates with predisposing and triggering parameters. Two statistical techniques were compared to assess their predictive performance in discriminating landslide from non-landslide conditions during extreme precipitation events. A comparison between binary logistic regression (BLR) and decision trees (QUEST) revealed the clear superiority of the BLR model, which achieved excellent predictive accuracy (AUC = 0.913). The model identified clay-rich lithology, gentle slopes (0–16°) and maximum daily precipitation as the most significant controlling factors. This result led to the generation of three derivative products: a susceptibility map, a hazard map for an extreme precipitation scenario with a 100-year return period, and a spatially distributed map of activation thresholds. This threshold map quantifies the intensity of precipitation required to exceed a critical probability of landslide initiation (p > 0.7) at any point in the territory. The susceptibility map highlights critical areas within the study area, while the hazard map also includes the return period of the event. The threshold map is a direct and operational tool for early warning systems, transforming a statistical model into a guide for real-time risk management. The study area serves as a pilot area that could allow this methodology to be replicated. With the integration of real-time meteorological data, it could function as a real-time warning system. The proposed framework therefore provides a directly actionable tool for civil protection agencies, land-use planning authorities, and emergency managers, enabling location-specific rainfall alert thresholds to be issued rather than a single regional value, with the potential to reduce both false alarms and missed warnings. Full article

The safety and stability of underground coal mining are largely determined by the structural features of coal seams and surrounding rocks. Geological heterogeneities such as faults, fracture zones, and lithological variations strongly influence the distribution of rock pressure and the occurrence of geodynamic hazards. This highlights the need for reliable geophysical methods capable of identifying such zones under mining conditions. Electrical prospecting represents a promising diagnostic approach, as it is highly sensitive to changes in the physical properties of rocks. Unlike conventional geological mapping, it enables the detection of hidden structures and weakened zones often invisible to direct observation. Advances in instrumentation and data processing have further expanded the applicability of electrical methods in complex environments. This study introduces a methodology of electrical prospecting observations for the diagnosis of coal seams. The analysis focuses on conductivity anomalies that reflect tectonic disturbances, fracture systems, and lithological heterogeneities. Field investigations demonstrated the sensitivity of the method to local environmental variations. Comparison with geological records confirmed the validity of the approach: the identified anomalous zones correlated well with documented tectonic features. The methodology showed a stable performance and revealed potential for integration into mine monitoring systems. It allows the identification of areas associated with elevated rock pressure and possible geodynamic activity, thereby contributing to safer underground operations. In the longer term, electrical prospecting may be applied to other coal deposits, including those with a high gas content and complex structure. The development of automated interpretation tools and machine learning algorithms could further increase processing efficiency and improve predictive reliability. Overall, the results confirm that electrical prospecting in mining environments can become an effective instrument for enhancing safety and building more accurate geological–geophysical models of coal seams. Full article

The study presents new data on the distribution, mapping, and morphostratigraphic characteristics of pediment deposits in the Montefeltro region (Italian Apennines), within the Val Marecchia Nappe. The Montefeltro landscape represents a clear example of morphology controlled by lithostructural features, with reliefs emerging from the surrounding terrain due to selective erosion. Its evolution has also been strongly influenced by climatic variations during the Middle–Late Pleistocene and the Holocene. Broad, gently sloping surfaces at the base of structural reliefs, together with associated debris deposits, are interpreted as erosional–depositional pediments formed under cold-climate, periglacial conditions during major Pleistocene glacial phases. Stratigraphic data from boreholes allowed the identification of pediment boundaries, thicknesses, and spatial extent, enabling reconstruction of the relict paleotopography and correlation with fluvial terraces. Two distinct lithological assemblages indicate different sediment sources and slope evolution pathways. Over time, pediments became disconnected from the present topography and were progressively dissected and terraced by fluvial incision, while recent slope adjustment is limited to modern drainage systems. This evolution reflects the combined influence of tectonic structure, lithology, and Quaternary climate change, confirming a regional trend of intensified fluvial deepening in the Marche Apennines. The study focuses on three representative areas: San Marino, Montecopiolo and Sassi Simone and Simoncello. Full article

This study investigated sulphide textures and trace element chemistry from the Tomingley Gold Project (TGP) region of Central-West NSW, eastern Australia, using in situ techniques. In particular, the study focused on pyrite and arsenopyrite to gain insights into ore-forming processes and determine which trace elements within these minerals can be used as potential pathfinder elements for mineral exploration in the TGP. A total of 41 drill core samples from a variety of lithologies (volcaniclastic, monzodiorite, graphitic siltstone, dacite, andesite) were described and analysed using reflected light microscopy, high-resolution microscopy (via Scanning Electron Microscope or SEM), elemental mapping (via Electron Probe Micro Analysis or EPMA) and targeted trace element analysis of sulphide grains (via Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry or LA-ICP-MS). Findings show that pyrite and arsenopyrite are the major sulphides that host fracture-fill/inclusions of native gold and ‘invisible gold’. Pyrite rich in groundmass inclusions should be evaluated due to their characteristic high concentrations of both As and Au. Pyrite trace element chemistry (Sn, Bi, W, Sb, Au and Se) was able to delineate mineralised from unmineralised samples in volcaniclastics, graphitic siltstones and andesites but was much more challenging for lithologies like dacites and monzodiorites. The study also found that Au may have been introduced into the system earlier and existed as ‘invisible gold’ in earlier generations of pyrite. This study highlighted the utility of in situ techniques to discriminate mineralised signatures from unmineralised samples, and this has proven to be far more effective compared to whole-rock techniques, emphasising the benefits of such datasets in mineral exploration. Full article