Search Results (150)

Active continental margins, generally characterized by narrow shelves incised by canyons, are pervasively shaped by submarine landslides that can occur near coastal areas. In this context, creating landslide susceptibility maps is the first step in landslide geohazard assessment. This paper focuses on shallow-water submarine landslides along the Capo d’Orlando continental margin and presents a related susceptibility map using the Weight of Evidence method. This method quantifies the strength of the association between a landslide inventory and predisposing factors. A geomorphological analysis of the continental shelf and upper slope yielded a landslide inventory of 450 initiation points, which were combined with five specifically selected preconditioning factors. The results revealed that the most favourable conditions for shallow-water landslides include slopes between 5° and 15°, proximity to faults (<1 km), proximity to river mouths (<2 km), the presence of consolidated lithologies and sandy terraces, and slopes facing NE and E. The landslide susceptibility map indicates that susceptible areas are in canyon heads and flanks, as well as in undisturbed slope portions near canyon heads where retrogressive landslides are likely. The model results are robust (AUC = 0.88), demonstrating that this method can be effectively applied in areas with limited geological data for preliminary susceptibility assessments. Full article

Urban expansion in coastal areas involves infrastructure development, industrial growth, and mining activities. These coastal environments face various environmental and geological hazards that require geo-engineers to devise solutions. An integrated geophysical approach aims to address such complex challenges as sea level rise, sea water intrusion, shoreline erosion, landslides and previous anthropogenic activity in coastal settings. In this study, the proposed methodology involves the systematic application of geophysical methods (FDEM, 3D GPR, 3D ERT, seismic), starting with a broad-scale survey and then proceeding to a localized exploration, in order to identify lithostratigraphy, bedrock depth, sea water intrusion and detect anthropogenic buried features. The critical aspect is to leverage the unique strengths and limitations of each method within the coastal environment, so as to derive valuable insights for survey design (extension and orientation of measurements) and data interpretation. The coastal zone of Throrikos valley, Attica, Greece, serves as the test site of our geophysical investigation methodology. The planning of the geophysical survey included three phases: The application of frequency-domain electromagnetic (FDEM) and 3D ground penetrating radar (GPR) methods followed by a 3D electrical resistivity tomography (ERT) survey and finally, using the seismic refraction tomography (SRT) and multichannel analysis of surface waves (MASW). The FDEM method confirmed the geomorphological study findings by revealing the paleo-coastline, superficial layers of coarse material deposits and sea water preferential flow due to the presence of anthropogenic buried features. Subsequently, the 3D GPR survey was able to offer greater detail in detecting the remains of an old marble pier inland and top layer relief of coarse material deposits. The 3D ERT measurements, deployed in a U-shaped grid, successfully identified the anthropogenic feature, mapped sea water intrusion, and revealed possible impermeable formation connected to the bedrock. ERT results cannot clearly discriminate between limestone or deposits, as sea water intrusion lowers resistivity values in both formations. Finally, SRT, in combination with MASW, clearly resolves this dilemma identifying the lithostratigraphy and bedrock top relief. The findings provide critical input for engineering decisions related to foundation planning, construction feasibility, and preservation of coastal infrastructure. The methodology supports risk-informed design and sustainable development in areas with both natural and cultural heritage sensitivity. The applied approach aims to provide a complete information package to the modern engineer when faced with specific challenges in coastal settings. Full article

This investigation presents the current status of seismic sedimentology, along with seismic geomorphology, as applied to the high-resolution (<5 m) mapping of sedimentary facies and hydrocarbon reservoirs in the subsurface. Seismic sedimentology involves the joint investigation of seismic lithology and seismic geomorphology. The high-resolution (as thin as one meter) interpretation of depositional units on lithofacies and paleo-landforms can be achieved by following a comprehensive workflow focusing on three mandatory steps (evaluating and improving data quality; selecting right attributes, preferably 90° seismic trace with frequency fusion; and making use of horizontal resolution on stratal slices) and two optional steps (guiding interpretation with seismic models and applying machine learning techniques). Seismic sedimentology is set to improve through enhanced calibration using well and outcrop data, along with regional and local geological models. Furthermore, there will be a deeper integration between geological and geophysical disciplines, as well as advancements in high-resolution geophysical acquisition and processing techniques. Full article

Understanding the relationship between geological and geomorphological processes is essential for reconstructing landscape evolution. This study examines how geology and geomorphology shape landscape development in central Brazil, focusing on the Natividade Group area. Sentinel-2 and SRTM data were integrated with geospatial analyses to produce two key maps: (i) a pedo-geomorphological map, classifying landforms and soil–landscape relationships, and (ii) a predictive geological–geomorphological map, based on a machine learning-based prediction of geomorphic units, which employed a Random Forest classifier trained with 15 environmental predictors from remote sensing datasets. The predictive model classified the landscape into six classes, revealing the ongoing interactions between geology, geomorphology, and surface processes. The pedo-geomorphological map identified nine pedoforms, grouped into three slope classes, each reflecting distinct lithology–relief–soil relationships. Resistant lithologies, such as quartzite-rich metasedimentary rocks, are associated with shallow, poorly developed soils, particularly in the Natividade Group. In contrast, phyllite, schist, and Paleoproterozoic basement rocks from the Almas and Aurumina Terranes support deeper, more weathered soils. These findings highlight soil formation as a critical indicator of landscape evolution in tropical climates. Although the model captured geological and geomorphological patterns, its moderate accuracy suggests that incorporating geophysical data could enhance the results. The landscape bears the imprint of several tectonic events, including the Rhyacian amalgamation (~2.2 Ga), Statherian taphrogenesis (~1.6 Ga), Neoproterozoic orogeny (~600 Ma), and the development of the Sanfranciscana Basin (~100 Ma). The results confirm that the interplay between geology and geomorphology significantly influences landscape evolution, though other factors, such as climate and vegetation, also play crucial roles in landscape development. Overall, the integration of remote sensing, geospatial analysis, and machine learning offers a robust framework for interpreting landscape evolution. These insights are valuable for applications in land-use planning, environmental management, and geohazard assessment in geologically complex regions. Full article

Floods and landslides are two distinct natural phenomena influenced by different conditioning factors, though some environmental triggers may overlap. This study applied eXtreme Gradient Boosting (XGBoost) to develop susceptibility maps for both phenomena, using a unified approach based on the same geospatial predictors. The approach integrated topographical, geological, and remote sensing datasets. Flood event data were collected from institutional sources using multi-source and high-resolution remotely sensed data. The landslide inventory was compiled based on historical records and geomorphological analysis. Key conditioning factors such as elevation, slope, lithology, and land cover were analyzed to identify areas prone to floods and landslides. The methodology was applied to the Basento River basin in Southern Italy, a region frequently impacted by both hazards, to assess its vulnerability and inform risk management strategies. While flood susceptibility is primarily associated with low-lying areas near river networks, landslides are more influenced by steep slopes and geological instability. The XGBoost model achieved a classification accuracy close to 1 for flood-prone areas and 0.92 for landslide-prone areas. Results showed that flood susceptibility was primarily associated with low Elevation and Relative Elevation, and high Drainage Density, whereas landslide susceptibility was more influenced by a broader and balanced set of factors, including Elevation, Drainage Density, Relative Elevation, Distance and Lithology. The resulting susceptibility maps offered critical approaches for land use planning, emergency management, and risk mitigation. Overall, the results demonstrated the effectiveness of XGBoost in multi-hazard assessments, offering a scalable and transferable approach for similar at-risk regions worldwide. Full article

The Pandameru River Basin, South India, is affected by high levels of contamination from human activities and the over-exploitation of groundwater for agriculture, both of which pose significant threats to water quality and its availability for drinking and irrigation. To explore sustainable groundwater management, this study presents a machine learning-driven approach to basin-scale groundwater potential zone (GWPZ) mapping by integrating remote sensing (RS), a geographic information system (GIS), and the random forest (RF) algorithm. The research leverages ten thematic layers—including lithology, geomorphology, soil type, lineament density, slope, drainage density, land use/land cover (LULC), NDVI, SAVI, and rainfall—to assess groundwater availability. The RF model, trained with well-distributed groundwater data, provides an optimized classification of GWPZs into five categories: very good (5.84%), good (15.21%), moderate (27.25%), poor (27.22%), and very poor (24.47%). The results indicate that excellent groundwater zones are predominantly located along highly permeable alluvial deposits, whereas low-potential zones coincide with impermeable geological formations and steep terrains. Field validation using piezometric readings and well data confirmed significant variations in water table depths, ranging from 5 m to over 150 m. The groundwater potential map achieved an accuracy of 86%, underscoring the effectiveness of the RF model in predicting groundwater availability. This high-precision mapping technique enhances decision-making for sustainable groundwater management, supporting long-term water conservation, equitable resource allocation, and climate-resilient water strategies. By providing reliable insights into groundwater distribution, this study contributes to the sustainable utilization of groundwater resources in semiarid regions, aiding policymakers and planners in mitigating water scarcity challenges and ensuring water security for future generations. Full article

A revision of the submarine instability processes offshore the Campania region is presented herein based on the literature data and Multibeam bathymetric and seismic profiles previously acquired by the CNR ISMAR of Naples (Italy). Among others, the objectives and perspectives of this research include the following: the chrono-stratigraphic framework of the submarine instability events and their correlation with the trigger geological processes, including the seismicity, the volcanism and the tectonic activity; density reversal has not been detected as a control factor; the implementation of technologies and database for the acquisition and the processing of morpho-bathymetric, seismo-stratigraphic and sedimentological data in the submarine slopes of Campania, characterized by submarine gravitational instabilities. Other main tasks include producing thematic geomorphological maps of the submarine slopes associated with instability phenomena. The principles of slope stability have been revised to be independent of the slope height. In submarine slopes mainly composed of sand, the stability depends on the slope inclination angle concerning the horizontal (β), equal or minor to the internal friction angle of loose sand (ϕ). Based on this research, it can be outlined that the submarine instability processes offshore of Campania mainly occur along the flanks of volcanic edifices, both emerged (Ischia) and submerged (Pentapalummo, Nisida, Miseno, Procida Channel), on steep, tectonically-controlled sedimentary slopes, (southern slope of Sorrento Peninsula, slope of the Policastro Gulf), and on ramps with a low gradient that surround wide continental shelves (Gulf of Salerno). Full article

The environment is being affected by the great development of human activities, which is why, in recent years, the need to protect the environment has increased, through the carrying out of a Strategic Environmental Assessment (SEA). Within this assessment, environmental geology constitutes an instrument for territorial and urban planning based on the analysis of conservation and the integral analysis of risks, obtaining cartography that can be useful in territorial and regional planning strategies. The methodology carried out in this article consists of applying a multi-criteria analysis in territorial planning, combining vector and raster data. This novel, low-cost, and effective methodology assesses conservation areas and risks, using map algebra and network analysis to identify priority areas and facilitate decision-making in a precise and quantitative manner. This analysis has been carried out in the Arribes del Duero Natural Park, which stands out as a place where numerous environmental values coexist, i.e., geological, geomorphological, and edaphological, forming unique landscapes. With regard to the results obtained, the cartography of conservation quality classifies the territory into four categories according to its degree of conservation: very high, high, low, and very low quality. The integral risk cartography identifies the areas with the greatest geological risks, such as erosion and landslides, and establishes limitations for land use. Also, by integrating both cartographies, it is determined which activities are compatible with each zone, considering both conservation and risks. Finally, it can be concluded that the cartographies obtained are useful for efficient land management, protecting the environment, and allowing human development in a controlled manner. Full article

The rapid and effective extraction of fault entities is a fundamental process in constructing a fault knowledge graph. As a key method for recording and preserving fault data, a fault investigation report holds significant potential for extracting valuable information. This paper proposes a fault knowledge annotation system that incorporates geographic information, fault attribute, fault structure, fault activity, fault geomorphology, and fault hazard. The system is developed based on a comprehensive analysis of the textual characteristics of fault investigation reports. Additionally, we establish a fine-grained corpus tailored for this task and apply a combination of BERT and BiLSTM-CRF for named entity recognition in the fault domain. We compare the performance of our model with a non-pre-training baseline model. The experimental results demonstrate that (1) the F1 value of entity recognition based on the faulty corpus exceeds 80%, which validates the efficacy of the faulty corpus; (2) the BERT model can effectively utilize available information. The corpus to adjust the subsequent tasks, thus improving the model output; (3) the proposed BERT-BiLSTM-CRF model and ALBERT-BiLSTM-CRF models have superior extraction performance in comparison to the no-pre-training model. This study not only provides a theoretical basis for the effectiveness of the BERT-BiLSTM-CRF model in fault entity identification, but also establishes a solid data foundation for the subsequent construction of the fault knowledge map. In addition, it offers reliable technical support for practical application areas such as geological surveys, disaster early warning, and urban planning, thereby promoting the advancement of data-driven research in the field of geology. Full article

The identification of areas that are susceptible to damage due to earthquakes is of utmost importance in tectonically active regions like Northeast India. This may provide valuable inputs for seismic hazard analysis; however, it poses significant challenges. The present study emphasized the integration of Interferometric Synthetic Aperture Radar (InSAR) deformation rates with conventional geological and geophysical data to investigate earthquake damage susceptibility in the Barapani Shear Zone (BSZ) region of Northeast India. We used MintPy v1.5.1 (Miami INsar Timeseries software in PYthon) on the OpenSARLab platform to derive time series deformation using the Small Baseline Subset (SBAS) technique. We integrated geology, geomorphology, gravity, magnetic field, lineament density, slope, and historical earthquake records with InSAR deformation rates to derive earthquake damage susceptibility using the weighted overlay analysis technique. InSAR time series analysis revealed distinct patterns of ground deformation across the Barapani Shear Zone, with higher rates in the northern part and lower rates in the southern part. The deformation values ranged from 6 mm/yr to about 18 mm/yr in BSZ. Earthquake damage susceptibility mapping identified areas that are prone to damage in the event of earthquakes. The analysis indicated that about 46.4%, 51.2%, and 2.4% of the area were low, medium, and high-susceptibility zones for earthquake damage zone. The InSAR velocity rates were validated with Global Positioning System (GPS) velocity in the region, which indicated a good correlation (R² = 0.921; ANOVA p-value = 0.515). Additionally, a field survey in the region suggested evidence of intense deformation in the highly susceptible earthquake damage zone. This integrated approach enhances our scientific understanding of regional tectonic dynamics, mitigating earthquake risks and enhancing community resilience. Full article

The Falakra geosite is located at the northern shoreline of the island of Limnos, Greece, and exhibits an array of unusual geomorphological features developed in late Cenozoic sandstones. Deposition of the primary clastic sediments was overprinted by later, low-temperature hydrothermal fluid flow and interstitial secondary calcite formation associated with nearby volcanic activity. Associated sandstone cannonballs take center stage in a landscape built by joints, Liesengang rings and iron (hydr)oxide precipitates, constituting an intriguing site of high aesthetic value. The Falakra geosite is situated in an area with dynamic erosion processes occurring under humid weather conditions. These have evidently sculpted and shaped the sandstone landscape through a complex interaction of wave- and wind-induced erosional processes aided by salt spray wetting. This type of geosite captivates scientists and nature enthusiasts due to its unique geological and landscape features, making its sustainable conservation a significant concern and topic of debate. Here, we provide detailed geological and remote sensing mapping of the area to improve the understanding of geological processes and their overall impact. Given the significance of the Falakra geosite as a unique tourist destination, we emphasize the importance of developing it under sustainable management. We propose the segmentation of the geosite into four sectors based on the corresponding geological features observed on site. Sector A, located to the west, is occupied by a lander-like landscape; to the southeast, sector B contains clusters of cannonballs and concretions; sector C is characterized by intense jointing and complex iron (hydr)oxide precipitation patterns, dominated by Liesengang rings, while sector D displays cannonball or concretion casts. Finally, we propose a network of routes and platforms to highlight the geological heritage of the site while reducing the impact of direct human interaction with the outcrops. For constructing the routes and platforms, we propose the use of serrated steel grating. Full article

The main objective of the present study was to develop an integrated approach combining remote sensing techniques and U-Net-based deep learning models for lithology mapping. The methodology incorporates Landsat 8 imagery, ALOS PALSAR data, and field surveys, complemented by derived products such as False Color Composites (FCCs), Minimum Noise Fraction (MNF), and Principal Component Analysis (PCA). The Dissection Index, a morphological index, was calculated to characterize the geomorphological variability of the region. Three variations of the deep learning U-Net architecture, Dense U-Net, Residual U-Net, and Attention U-Net, were implemented to evaluate the performance in lithological classification. Validation was conducted using metrics such as the accuracy, precision, recall, F1-score, and mean intersection over union (mIoU). The results highlight the effectiveness of the Attention U-Net model, which provided the highest mapping accuracy and superior feature extraction for delineating flysch formations and associated lithological units. This study demonstrates the potential of integrating remote sensing data with advanced machine learning models to enhance geological mapping in challenging terrains. Full article

The Yucatan Peninsula (YP) presents heterogeneous environments in a karstic landscape that has been formed from permeable sedimentary rocks dating from the Cretaceous period. Its aquifers now face significant pressure from tourism, agriculture, soil use changes and population growth. Aquifer delimitation typically relies on environmental and socioeconomic criteria, overlooking the subterranean fauna. Stygobiotic crustaceans are highly diverse in the YP’s subterranean karstic systems, expressing adaptations to extreme environments while often also displaying the primitive morphology of evolutionary relics. With distributions restricted to specific environments, they are potential markers of water reserves. A literature review recovered records of 75 species of crustaceans from 132 subterranean systems in the YP, together with geomorphological, hydrological, hydrogeochemical and historical precipitation data. Fourteen UPGMA clusters were informative for mapping species composition, whereby the “Ring of Cenotes”, “Caribbean Cave” and “Cozumel Island” regions were delineated as consolidated aquifers. These aquifers are distinguished by abiotic factors as well: freshwater species dominate the Ring of Cenotes, while marine-affinity species characterize the Caribbean Cave and Cozumel Island aquifers. Stygobiotic crustaceans, being linked to geologically ancient water reserves and having a restricted distribution, offer a complementary tool for aquifer delimitation. Their presence suggests long-term and stable water availability. The use of these unique organisms for integrative aquifer delimitation can provide a way to improve the monitoring networks of regional aquifers. Full article

Landslide susceptibility evaluation is an indispensable part of disaster prevention and mitigation work. Selecting effective evaluation methods and models for landslide susceptibility assessment is of significant importance. This study focuses on selected areas in Yunyang County, Chongqing City. By interpreting high-resolution satellite remote sensing images from before and after heavy rainfall on 31 August 2014, the distribution of rainfall-induced accumulation landslides was obtained. To evaluate the susceptibility of accumulation landslides, we have equated evaluation factors to accumulation distribution prediction factors. Eight evaluation factors were extracted using multi-source data, including lithology, elevation, slope, remote sensing image texture features, and the normalized difference vegetation index (NDVI). Various machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and BP Neural Network models, were employed to assess the susceptibility of rainfall-induced accumulation landslides in the study area. Subsequently, the accuracy of the evaluation models was compared and verified using the Receiver Operating Characteristic (ROC) curve, and the evaluation results were analyzed. Finally, the developed Random Forest model was applied to Gongping Town in Fengjie County to verify its applicability in other regions. The findings indicate that the complex geological conditions and the unique tectonic erosion landform patterns in the northeastern region of Chongqing not only make this area a center of heavy rainfall but also lead to frequent and recurrent rainfall-induced landslides. The Random Forest model effectively reflects the development characteristics of accumulation landslides in the study area. High and very high susceptibility zones are concentrated in the northern and central regions of the study area, while low and moderate susceptibility zones predominantly occupy the mountainous and riverside areas. Landslide susceptibility mapping in the study area shows that the Random Forest model yields reasonably graded results. Elevation, remote sensing image texture features, and lithology are highly significant factors in the evaluation system, indicating that the development factors of slope geological disasters in the study area are mainly related to topography, geomorphology, and lithology. The landslide susceptibility evaluation results in Gongping Town, Fengjie County, validate the applicability of the Random Forest model developed in this study to other regions. Full article

Identifying suitable sites for urban, industrial, and tourist development is important, especially in areas with increasing population and limited land availability. Kharga Oasis, Egypt, stands out as a promising area for such development, which can help reduce overcrowding in the Nile Valley and Delta. However, soil and various environmental factors can affect the suitability of civil engineering projects. This study used Geographic Information Systems (GISs) and a multi-criteria decision-making approach to assess the suitability of Kharga Oasis for construction activities. Geotechnical parameters were obtained from seismic velocity data, including Poisson’s ratio, stress ratio, concentration index, material index, N-value, and foundation-bearing capacity. A comprehensive analysis of in situ and laboratory-based geological and geotechnical data from 24 boreholes examined soil plasticity, water content, unconfined compressive strength, and consolidation parameters. By integrating geotechnical, geomorphological, geological, environmental, and field data, a detailed site suitability map was created using the analytic hierarchy process to develop a weighted GIS model that accounts for the numerous elements influencing civil project design and construction. The results highlight suitable sites within the study area, with high and very high suitability classes covering 56.87% of the land, moderate areas representing 27.61%, and unsuitable areas covering 15.53%. It should be noted that many settlements exist in highly vulnerable areas, emphasizing the importance of this study. This model identifies areas vulnerable to geotechnical and geoenvironmental hazards, allowing for early decision-making at the beginning of the planning process and reducing the waste of effort. The applied model does not only highlight suitable sites in the Kharga Oasis, Egypt, but, additionally, it provides a reproducible method for efficiently assessing land use suitability in other regions with similar geological and environmental conditions around the world. Full article