ABSTRACT The research employs advanced Geographic Information Systems (GIS) and Remote Sensing (RS) techniques to extract, analyze, and interpret critical environmental data, providing a comprehensive understanding of the basin’s physical and hydrological dynamics. These tools are indispensable for modeling watershed processes and managing reservoir storage, supporting the development of practical and scientifically sound management scenarios. Using a 30 m resolution Digital Elevation Model (DEM) from the United States Geological Survey (USGS), the basin was delineated and analyzed in ArcGIS. Elevation classification was performed using the Jenks Natural Breaks algorithm, which divides the elevation data into nine classes by minimizing variance within classes and maximizing differences between them. The results indicate that the highest elevations occur in the Silveh and Sardasht sub-basins, reaching 3596 and 3529 m, while the lowest elevation of 407 m is found in the Dukan sub-basin. These elevation differences create diverse climatic and hydrological regimes, where upstream high-altitude areas receive mixed precipitation and accumulate snow, contributing gradual snowmelt, while lower basins generate rapid runoff with limited infiltration. The study evaluates the hydrological impacts of upstream dam construction, particularly the Sardasht Dam, on inflows to the Dukan Dam, revealing notable changes that underscore the need for adaptive water management.

Abstract This study introduces a novel end-to-end framework for a reliable dehazing of images obtained from an operational flow monitoring network operated by the U.S. Geological Survey (USGS), comprising more than 800 nationwide cameras. A two-stage process that consists of a classifier and a dehazing models is proposed. The classifier acts as a filtering step to detect hazy images and avoid unnecessary operations on haze-free images. The classifier is composed of a frozen ResNet50 feature extractor followed by a custom three-layer fully connected head. The hazy images are then restored using a novel one-step image-to-image translation generative adversarial network model (CycleGAN-Turbo), which consists of two generators and two discriminators. The model leverages diffusion-based architecture. CycleGAN-Turbo was trained on unpaired sets of real hazy and haze-free images. Results show the efficiency of the proposed framework in identifying and restoring hazy images when applied to a network of cameras monitoring river flow conditions. The classifier achieved an overall accuracy of 99.28% with 100% recall in the hazy class. The dehazing model scored 153.29 in FID, 0.73 in CMMD, and 0.0142 in DINO in restoring real-hazy images. A comparison with other state-of-the-art dehazing models shows the superiority of the proposed framework in dehazing real-world hazy images.

Abstract. Australian commonwealth, state and territory geological surveys possess information on over 3 million drillhole logs. In addition to mineral exploration drilling, extensive drillhole datasets exist from oil and gas exploration and hydrogeological studies. Other countries no doubt have similar data holdings. Together these legacy drillhole datasets have the potential to significantly enhance constraints on regional 3D geological models and improve our understanding of subsurface architecture, but have limited use in their current form as many if not most drill logs lack stratigraphic information, containing only lithological descriptions. This study develops open-source codes and methodologies for stratigraphy recovery (determining the ordered sequence of stratigraphic units) from drillhole lithological data by introducing a search algorithm that systematically explores all geologically plausible stratigraphic orderings for individual drillholes, combined with a solution correlation algorithm that compares the topological relationships of stratigraphic units across multiple drillholes to identify geologically consistent solutions and reduce uncertainty. The algorithms combine constraints from lithological descriptions with stratigraphic relationships automatically derived from regional maps. In addition, the method quantifies uncertainty by generating multiple plausible stratigraphic interpretations, providing critical insights for resource estimation, scenario analysis, and data acquisition strategies. The application of our method to a dataset of 52 drillholes from South Australia demonstrated its ability to make useful predictions of stratigraphic solutions and quantifying associated uncertainties. These results not only validate our approach but also highlight opportunities to refine current stratigraphic descriptions and provide a valuable new source for regional 3D geological modelling.

The Arctic region is an intersection of strategic competition and climate change. With the melting of glaciers in the Arctic, previously inaccessible natural resources and new sea trade routes have become accessible. Furthermore, according to data from the U.S. Geological Survey, a significant portion of the world's oil and natural gas reserves are located in the Arctic. This situation has made the region not only an area of environmental vulnerability but also a strategic area. Indeed, the United States (US), particularly during Donald Trump's presidency from 2017 to 2021, has significantly increased its interest in the Arctic. During this period, environmental regulations were pushed into the background, and environmental diplomacy was weakened through policies such as withdrawal from the Paris Climate Agreement. The main objective of this study is to examine the effects of climate change on US foreign policy in the context of the Donald Trump era and to reveal how these policies can be understood within the framework of the theory of offensive realism. Offensive realism is a theory developed by international relations theorist John Mershaimer. According to this theory, states do not merely seek to ensure their security; they are constantly engaged in the pursuit of power maximization and hegemony. In line with this, the study examined strategy documents published between 2019 and 2021 using content analysis and developed policy options for Trump's second term through scenario analysis. According to the findings of the analysis, the strategy documents are consistent with the basic assumptions of offensive realism. The study concluded that the perspective of offensive realism would be dominant during Trump's second term, that a balanced foreign policy was a limited possibility, and that a neighborhood-first policy was unlikely.

The purpose of this study was to evaluate the potential safety hazards pertaining to the overall inclination of existing masonry structures. Taking a six-story masonry residential building in Tongling as the research subject, we established a systematic safety assessment framework. Through structural entity testing, settlement monitoring, and geological surveys, uneven foundation settlement was identified as the primary cause of the building’s inclination. A finite element model was established via SAP2000 (Version 14) software to examine and verify the bearing capacity and seismic performance of the inclined structure, and a 1:4 scale shaking table test was designed to validate the seismic performance of the structure following inclination correction. The findings indicate that the primary bearing capacity of the building’s superstructure complied with the relevant code provisions. Time–history analysis under rare six-degree earthquake conditions showed that the maximum inter-story drift angle—defined as the ratio of the maximum inter-floor horizontal displacement to the floor height under the action of the standard seismic value—was 1/2018, which is lower than the limit value of 1/900 specified for the “moderate damage” performance level of masonry structures. During the shaking table test, the natural vibration frequency of the structure remained unchanged under earthquake actions ranging from frequent to rare six-degree events, with no visible cracks or significant damage detected. This assessment system provides a technical reference for the safety performance evaluation and subsequent inclination correction of similar inclined masonry structures.

Accurate classification of thin-section rock images is critical for geological surveys, resource exploration, and automated petrographic analysis. However, existing deep learning models struggle to balance local texture representation and global semantic context, often leading to suboptimal performance in fine-grained rock type recognition. In this study, we propose HFANet, a novel hybrid local-global feature attention network that integrates a DenseNet-based local branch and a Swin Transformer-based global branch to simultaneously capture detailed textures and contextual semantics. A multi-head self-attention module enhances local feature interactions, while a bidirectional cross-attention mechanism enables dynamic guidance between local and global representations. To further improve discriminative capability, we introduce an ensemble classification framework comprising three independent heads with adaptive fusion, and incorporate handcrafted geological features-such as color, texture, and luminance-via multimodal fusion. Extensive experiments on a comprehensive rock thin-section dataset demonstrate that HFANet outperforms a wide range of state-of-the-art models across multiple tasks, achieving up to 99.03% accuracy and perfect AUC and AUPR scores on the sedimentary subset. Ablation studies and interpretability analyses confirm the effectiveness of each component and the model's ability to focus on geologically meaningful regions. The results highlight HFANet's strong potential for advancing intelligent geoscientific image analysis and facilitating practical applications in lithological classification.

ABSTRACT Tunnel construction, particularly hard rock tunnelling using TBM, involves complex geological conditions. Traditional risk assessment methods primarily rely on engineers’ experience, which is often affected by inaccurate geological surveys, insufficient site investigations, and subjective expert knowledge, potentially leading to insufficient risk control and accidents. Moreover, purely data-driven approaches often fail to accurately assess risk severity or support effective risk decision-making. This paper proposes an innovative knowledge–data dual-driven framework that integrates machine learning with expert systems and cloud modelling. Machine learning algorithms are employed to accurately predict the likelihood of TBM collapse, while expert systems combined with cloud model theory are used to quantify accident severity. In the data-driven component, among the three algorithms tested, the Random Forest (RF) model achieved the best performance with a prediction accuracy of 98%. In the knowledge-driven component, the expert system together with cloud models quantifies the uncertainty in expert knowledge. Finally, a fuzzy-cloud probabilistic model is proposed to couple the results of data-driven and knowledge-driven approaches, quantify the probabilities of risk levels, and enable intelligent risk decision-making. The framework is supported effectively through four specific scenarios from a real-world tunnel project, and the results demonstrate its feasibility and effectiveness in supporting TBM collapse risk decision-making.

This study presents an airborne radiometric assessment of natural gamma radiation across selected areas in Ondo and Osun States, Nigeria, utilising GIS gridded airborne geological map data from Sheets 263 and 264 obtained from the Nigerian Geological Survey Agency. The data, organised in a gridded format, were analysed using Oasis Montaj software to quantify the concentrations of naturally occurring radioelements, 238U, 232Th, and 40K, and to estimate the corresponding terrestrial gamma dose rates. Results indicate that, in Sheet 264, concentrations of 238U, 232Th, and 40K range from 4.7 to Bq/kg, 20.6 to 172.7Bq/kg, and 96.6 to 1560.50Bq/kg, with mean values of 48.2, 81.3, and 609.0 (Bq/kg) respectively. For Sheet 263, 238U concentrations span 0.1-94.0Bq/kg, 232Th from 5.90 to 162.70Bq/kg, and 40K from 59.50 to 943.00Bq/kg with the average values of 37.2, 56.9, and 331.70 (Bq/kg) accordingly. The calculated absorbed dose rates in both sheets spanned between 37.8 and 191.6 and 14.3 and 161.8 for Sheet 264 and Sheet 263 with mean values of 100.5 nGy/h and 67.8 nGy/h. The absorbed dose rate for most of the locations in the study area exceeds the global average of 59 nGy/h, with generally higher values observed in Sheet 264. The high values of standard deviation and coefficient of variation, particularly for 40K, indicate strong spatial heterogeneity in radioelement distribution, suggesting varying lithological and mineralogical compositions within the study area. The elevated levels of primordial radioelements and associated radiation doses in several locations suggest a potential public health concern due to chronic exposure to ionising radiation. Given the geological context and ongoing anthropogenic activities in the region, routine environmental monitoring is recommended to mitigate potential radiological risks to local populations.

Around the turn of the 20th century, the first state geologist at the Geological Survey of Norway, located in Kristiania (Oslo) at that time, Johan (also sometimes named John) Bernhard Rekstad established a series of marks, mostly crosses carved into boulders and bedrock slabs. These marks formed the basis for frontal measurements of outlet glaciers from Jostedalsbreen Ice Cap in western Norway. In total, he carved 29 marks in front of 15 outlet glaciers around the ice cap. For only a few of these marks, the exact locations have been known until recently. Therefore, the authors started a search for these marks some years ago, as they may provide valuable information on glacier frontal changes since their time of establishment. At present (2025), 18 marks in front of 12 different outlet glaciers from Jostedalsbreen Ice Cap have been rediscovered and documented by exact locations (coordinates and altitude) and photographs.

Due to the ecosystem services they provide, forest resources are essential for the development of human society. The protection of forests is inevitable because of their importance in ensuring sustainability. This study seeks to examine community perception on forest protection by integrating it with remote sensing data and investigates socio-economic and environmental implications of protection to the surrounding communities. This study used a case study design and was guided by Elinor Ostrom's socio-ecological theory. The study used a sample of 301 respondents for quantitative data and 12 respondents for qualitative data, making a total of 313 respondents. Respondents were selected from two wards of Kazimzumbwi and Kisarawe using simple random and purposeful sampling techniques. Various tools were used to collect the information, including the use of semi-structured questionnaires, key informants’ interviews, satellite images that were downloaded from the United States Geological Survey (USGS) archives, and reviews of literature. The quantitative data were analysed using descriptive statistics with the statistical Package of Social Sciences 12 while quantitative data were analysed using thematic analysis. The satellite data was analysed using the random forest (RF) algorithm and mapped in ArcGIS 10 software to examine the forest cover changes in Kazimzumbwi Forest Reserve from 1994 to 2024. The results showed that the majority of respondents were aware of forest protection, which was identified using several local knowledge indicators that indicated an increase in trees. Such an indicator was proved by a satellite image that shows the increase in bushland and open woodland and the decrease in the area occupied by grassland in the area. In a similar vein, respondents acknowledged the existence of social, economic, and environmental impacts in the area that were mainly reflected by economic hardship and a decrease in farmland. The increase in energy prices, control of soil erosion, climate regulation, improvement in biodiversity, fear and insecurity, rise in crime, lack of compensation, and enhancement of social services were all noted as significant issues. The study recommends the need to strengthen the governance system by engaging local communities in decision-making and establishing sustainable livelihood support to the community surrounded by the protected forest. It also recommends for implementing continuous environmental education provision among the key stakeholders to ensure sustainable commitment to the protection.

The shark †Otodus megalodon is one of the main marine predators of the Cenozoic, with fossil records ranging from 15.9 to 3.6 Ma and a distribution considered cosmopolitan. However, some regions of the planet still lack unequivocal records of the species, including the South Atlantic Ocean. Although there are records of teeth on continental South America, this work presents the first record of a †O. megalodon tooth in the South Atlantic Ocean (offshore). The tooth was collected by the Geological Survey of Brazil (SGB), in 2012, on the Rio Grande Rise (RGR) from a dredge haul between 664–667 m depth at station 4340-IV-150-HAS and is embedded in a phosphatized ferromanganese crust. The sample exhibits a porous, botryoidal surface, with internal lamination alternating dark Fe-Mn-rich layers and light calcium- and phosphate-rich layers. The morphological description indicates a triangular tooth, with TH = 57.1 mm, CH = 45.2 mm, and CW = 36.2 mm. The cutting edges are worn due to marine abrasion. The predominant color is black (N2–N3, Munsell), reflecting Fe-Mn coating. Strontium isotope analyses performed on equivalent materials indicate ages between 21.7 and 14.5 Ma (Early–Middle Miocene). This interval coincides with the period of greatest diversity and broad distribution of †O. megalodon, supporting the interpretation that the tooth corresponds to this geological interval. The presence of the tooth in deep-sea settings and associated with Fe-Mn crusts is unusual, as most findings occur in coastal environments. The results presented suggest that the RGR may have served as a migratory route or feeding area for large pelagic predators during the Miocene, possibly associated with local productivity and regional paleoceanographic conditions. The record reinforces the scientific and strategic importance of the RGR and highlights the need to expand paleontological and paleoceanographic investigations in the deep waters of the South Atlantic Ocean.

In this study, Wavelet Long Short-Term Memory (WLSTM) and Wavelet Convolutional Neural Network Long Short-Term Memory (WCNNLSTM) are two Artificial Intelligence (AI) methods that are applied for the subseasonal-to-seasonal (S2S) streamflow forecasting in seven rivers in Colorado. Daily and monthly meteorological variables from 1992 to 2022 were collected from Colorado’s Mesonet (CoAgMet) as input data. Daily and monthly discharge data were acquired from the United States Geological Survey (USGS) as output data. In addition, precipitation data were gathered from the National Aeronautics and Space Administration (NASA) global precipitation network, including the Tropical Rainfall Measuring Mission (TRMM) and the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG). WCNNLSTM-IMERG had the best accuracy in forecasting streamflow in Colorado due to its finer spatial resolution. The findings of this study will be beneficial for modelers and dataset developers in evaluating the performance of NASA products in practice for S2S streamflow forecasting.

Shri Indu Bhushan Chhibber, a legend of the Engineering Geology (EG) stream, was associated with this domain for six decades, and was one among the doyens connected with the formative years of Engineering Geology Division in the Geological Survey of India (GSI). His impactful career spanned six decades and reads like an illustrious testament.

ABSTRACT Marine protected areas are essential for the conservation of marine environment, biodiversity and geodiversity. Ecosystems are closely linked to the variability of different geological settings and it is important to integrate different data for effective management. To this purpose, geophysical, geological and biological surveys have been carried out in the Miramare Marine Protected Area (north-eastern Adriatic Sea) since 2003 to characterize the seabed and the subsurface. We present seven thematic maps: subsurface maps, morpho-bathymetric maps, sediment texture, bionomic maps and the synthesis in the geo-biological map, which combines and integrates data interpretations from both the geological and biological domains to illustrate the marine environment of Miramare.

ABSTRACT The rate of earthquakes in a region is a fundamental input to Probabilistic Seismic Hazard Analysis. We present a Monte Carlo method for computing that rate from seismicity catalogs while including a range of data and analysis uncertainties. This method is applied to regions for which the b value is assumed to be spatially invariant. Each region is broken down into epochs for which each epoch is estimated to have a uniform magnitude of completeness (Mc). The distribution of earthquake rates for M≥Mc is determined for each epoch by considering the Poisson likelihood of rates given the number of observed earthquakes with M≥Mc. We use a Monte Carlo process to include the uncertainty in b, Mc, and individual event magnitudes. The result for each epoch is the joint distribution of the Poisson rate of earthquakes with magnitudes larger than the minimum value used to calculate hazard (M1) and the Gutenberg–Richter b values, which control the extrapolation to other magnitudes. The rate for each region is either the duration-weighted average over the epochs or, to better capture temporal variations, we also consider mixture models. The mixture models also provide an avenue to allow temporal variations in b values. To implement this joint distribution in a logic tree, we use the mean and 95% confidence branches, each of which is parameterized with an M≥M1 rate and b value. We explore different ways of defining those branches, as well as non-Gutenberg–Richter branches, and their impact on hazard estimates. The mean hazard, but not the fractiles, is robust with respect to these choices. To illustrate these new methods, we use synthetic data and catalogs from recent U.S. Geological Survey National Seismic Hazard Models for the Central and Eastern United States and for Puerto Rico and the U.S. Virgin Islands.

This study examined the potentials, challenges and prospect of solid minerals sector of the Nigerian economy; with emphasis to factors responsible for the low performance of the sector in terms of contribution to GDP, export and employment generation. This sector has prospect to promote economic diversification drive of the Federal Government of Nigeria if well developed and managed. Despite government efforts over the years to reposition and revitalize it through legislation, regulation and investment; its optimum output in terms of wealth creation, government revenue, employment generation have been hampered by a number of factors that did not allow the sector to prosper as planned. The study employed Documentary Research Method (DRM) as a source of data; and document analysis in interpreting and presenting research findings. From 1981 – 2022, solid mineral had a positive but statistically insignificant impact on the Nigerian economic growth including export. Nigeria’s Mining and Quarrying sector contributed 7.72% to the overall GDP in the third quarter of 2024, while it recorded 8.32% in the third quarter of 2023 according to the NBS report, 2024. Some of the challenges of the sector include access to high capital and technology; insecurity; poor private sector investment, energy intensive, local human capital, artisanal mining, illegal mining in many solid minerals’ sites, value addition, developing and building investors’ confidence as well as regulatory framework that can guarantee fair fiscal regime among others. In view of this, there is the need for the Federal Government of Nigeria through Nigerian Geological Survey Agency (NGSA) to provide data about locations of solid minerals in commercial quantities in the country for attracting local and foreign investment. Next is to ensure political stability and security in the country; promote forward and backward linkages in the solid minerals sector; provide basic infrastructure and address illegal mining activities across the mining sites in the country.

This study presents the results of engineering and geological surveys conducted at an industrial facility in the northern region of Kazakhstan. The hydrogeological conditions of the site were analyzed, and field investigations were carried out to determine the physical and mechanical properties of the soil. The surveys were conducted in accordance with the requirements of current regulatory documents in the Republic of Kazakhstan. Engineering-geological investigations were performed to provide a comprehensive assessment of the site conditions, taking into account the characteristics of the planned facilities. The ranges of deformation modulus for various soil types were evaluated using plate load tests. Particular attention was given to the advantages of this approach, including the preservation of structural integrity, minimizing the impact on existing structures, and the use of strong and durable materials. Examples of successful application in industrial facilities are presented to demonstrate the practical significance of the methods. The importance of advanced engineering solutions for reducing soil deformation is emphasized. The study focuses on the analysis of soil layers and groundwater levels. Field results indicate a significant reduction in soil deformation, which enhances both the stability and the service life of industrial buildings and structures. Overall, engineering and geotechnical testing represent a promising component of modern industrial design, requiring an interdisciplinary approach that considers engineering, geological, and economic factors.

Vernadsky State Geological Museum of RAS keeps collections of Moscow Imperial University Geological Cabinet. In 1892, Geological Cabinet received collections from John Wesley Powell (1834-1902). J.W. Powell was one of the famous geologists in the United State. He was the second Director of U.S. Geological Survey and the first researcher of Colorado canyons. The article briefly describes his biography and gives information about collections he transferred.

Groundwater in Bangladesh exhibits significant spatial and temporal variability, driven by monsoon recharge, lithologic heterogeneity, and human extraction. Integrating long-term monitoring data with recent field and Geographic Information Systems analyses (2021–2023), this study quantifies groundwater responses across northwestern and west-central Bangladesh. Seasonal drawdowns in solar irrigation pump (SIP) wells averaged 4.38 ± 1.74 m, followed by full post-monsoon recovery, confirming a recharge–discharge balance. The Theis analytical model, applied with a pumping rate of 2,500 m3/day (representing the upper operational range of SIPs), an average aquifer material transmissivity of 1,800 m2/day, and a storage coefficient of 0.1, predicted maximum drawdowns of 0.68 m at a distance of 10 m from the pumping wells—well below the 5 m critical threshold for maintaining domestic water-supply sustainability. Data also reveal that transmissivity and storage coefficients vary by up to two orders of magnitude among physiographic regions, emphasizing the need for site-specific management. The relatively high transmissivity and storage coefficients of the aquifer materials enable the functioning of the Bengal Water Machine, in which irrigation-induced recharge enhances groundwater storage and helps maintain stable water levels. By applying the United States Geological Survey’s safe-yield concept alongside the Mandel–Shiftan sustainability framework, this study demonstrates that current SIP groundwater withdrawals remain well within sustainable limits. These findings support regionally adaptive groundwater governance that aligns pumping rates with recharge capacity. Such alignment is essential for maintaining irrigation viability and food security under changing climatic and hydrologic conditions. Overall, the results emphasize the importance of region-specific management strategies rather than generalized depletion narratives.

The complex geological conditions of hidden karst in the Dongting Lake region pose significant challenges for the siting and foundation treatment of solid waste landfills. This paper takes a typical landfill in this region as a case study, integrating three-dimensional (3D) geological modeling, karst vulnerability assessment (LEPT), the Analytic Hierarchy Process (AHP), and finite element numerical simulation to systematically conduct site suitability analysis and stability evaluation. First, based on geological survey data, a 3D geological model was constructed to identify the distribution characteristics of weak substrata and hidden karst features. Second, the LEPT method was employed to assess regional karst vulnerability, and the AHP was used to comprehensively grade site suitability based on multiple criteria encompassing natural, environmental, and infrastructural factors. Furthermore, the finite element method was applied to analyze foundation settlement and stability under landfill loading, proposing comprehensive treatment technologies based on deformation coordination and reinforcement. The research results indicate that landfill siting in hidden karst areas should prioritize avoiding high-vulnerability zones; settlement of weak substrata can be effectively controlled through composite foundation treatment and synergistic design with the lining system; the proposed integrated "assessment-design-treatment" framework can provide a theoretical basis and technical reference for the construction of similar landfills in karst regions.