Search Results (5,462)

To investigate deep Earth information, researchers often utilize geomagnetic observatories and satellite data to obtain the conversion function of geomagnetic sounding, C-response data, and employ traditional inversion techniques to reconstruct subsurface structures. However, the traditional gradient-based inversion produces geophysical models with artificial structure constraint enforced subjectively to guarantee a unique solution. This method typically requires the model parameterization knowledge a priori (e.g., based on personal preference) without uncertainty estimation. In this paper, we apply an efficient trans-dimensional (trans-D) Bayesian algorithm to invert C-response data from observatory and satellite geomagnetic data for the electrical conductivity structure of the Earth’s mantle, with the model parameterization treated as unknown and determined by the data. In trans-D Bayesian inversion, the posterior probability density (PPD) represents a complete inversion solution, based on which useful inversion inferences about the model can be made with the requirement of high-dimensional integration of PPD. This is realized by an efficient reversible-jump Markov-chain Monte Carlo (rjMcMC) sampling algorithm based on the birth/death scheme. Within the trans-D Bayesian algorithm, the model parameter is perturbated in the principal-component parameter space to minimize the effect of inter-parameter correlations and improve the sampling efficiency. A parallel tempering scheme is applied to guarantee the complete sampling of the multiple model space. Firstly, the trans-D Bayesian inversion is applied to invert C-response data from two synthetic models to examine the resolution of the model structure constrained by the data. Then, C-response data from geomagnetic satellites and observatories are inverted to recover the global averaged mantle conductivity structure and the local mantle structure with quantitative uncertainty estimation, which is consistent with the data. Full article

This study investigates the influence of cooling rates on the microstructure and phase transformations of the high-entropy alloy Cr₁₆Mn₁₆Fe₁₆Co₁₆Ni₁₆P₂₀. The alloy was synthesized via arc melting and subjected to three cooling conditions: slow cooling (52 K/s), accelerated cooling after a short electric arc pulse (3018 K/s), and rapid quenching (10⁵–10⁶ K/s) using the melt-spinning method. The microstructures were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Mössbauer spectroscopy. The thermal properties and phase transformations were analyzed using differential scanning calorimetry (DSC) and thermography. Slow cooling produced a complex crystalline microstructure, while accelerated cooling resulted in fewer phases. Rapid cooling yielded an amorphous structure, demonstrating that phosphorus and high mixing entropy enhance glass-forming ability. Phase transformations exhibited significant undercooling under accelerated cooling, with FCC phase crystallization shifting from 1706 K (slow cooling) to 1341 K, and eutectic crystallization from 1206 K to 960 K. These findings provide a foundation for optimizing cooling processes in high-entropy alloys for advanced structural and functional applications. Full article

The Kalatag mineralization belt is an important metallogenic belt of polymetallic mineral deposits in the northern part of eastern Tianshan, and its age and tectonic setting are still controversial. We identified a set of Devonian volcanic rocks hosted in the Early Palaeozoic package of dominantly marine sediments with a small amount of terrestrial rocks. This study presents petrological, U–Pb geochronology, and geochemical data for the volcanic rocks. The ages of the rhyolite (407.2 ± 1.9 Ma) and basaltic andesite (380.4 ± 2.8 Ma) suggests that the Kalatag belt is a Devonian volcanic succession. These rocks consist mainly of marine calc–alkaline lava, tuff, pyroclastic rocks, and minor terrestrial basaltic andesite. The lavas are characterized by the enrichment of light rare earth elements and strongly depleted in Nb and Ta, typical of island arc magmatic rocks. The volcanic rocks probably originated from the partial melting of the mafic lower crust which was modified by subducted slab-related fluids. During their ascent through the crust, these volcanic rocks underwent variable extents of fractional crystallization (rhyolites) and crustal contamination (basaltic andesites). Combined with the results of previous studies, we suggest that the Devonian rocks formed in an island arc related to the northward subduction of the Northern Tianshan Ocean with a crustal thickness of ~35–40 km. Full article

Oil spills over land and water bodies are some of the most relevant hazards that should be considered when implementing oil production and transport projects. However, the development of robust, versatile, and efficient tools for carrying out this type of hazard assessment is a challenge for geophysical modellers due to the complexity of the oil flow over hybrid terrain–water surfaces. This work presents a versatile Eulerian approach to simulating the transport of an oil layer flowing over steep terrain that may also be dragged by an underlying water flow, i.e., rivers, lakes, oceans, etc., if it exists. The model allows for the seamless simulation of spills that start on land and eventually impact a water body in a single simulation step. The focus here is paid to the integration of the drag shear stresses between the layers, responsible for the oil spreading over a moving water surface. This drag term is solved using a non-iterative implicit method that allows for robust and efficient solutions even with high coupling between both layers. Two synthetic test cases are simulated to demonstrate the accuracy and robustness of the proposed model, obtaining results that validate the model’s behaviour in high-coupling cases. Finally, the spreading hazard for a realistic oil production project is assessed. The results obtained verify the capability of the model to become a useful tool for oil spill forecasting over hybrid terrain–water surfaces. Full article

This paper presents the results of an examination of an automatic biomass boiler identifying its strengths and weaknesses and computing its seasonal energy and emission parameters. The boiler was found to meet the energy and emission requirements for distribution in Poland. The boiler is characterised by good heating efficiency and low dust and carbon monoxide emissions. The aim of this paper is to provide and analyse these parameters, and by doing so classify it in the context of its competitors. The average heating output is 26.86 kW and the thermal efficiency is 87.97%. Carbon monoxide emissions are very low (22.71 mg/m³). However, nitrogen oxide emissions (187.6 mg/m³) can be a problem. Filters made out of metalworking waste, i.e., machining shavings, significantly improve the boiler performance, contributing to an increased heat output and efficiency and reduced dust emissions. Compared with other solutions available in the market, the boiler compares favourably in terms of dust and carbon monoxide emissions and is also characterised by similar efficiency, especially with the filters in place. Regarding the context of thermal energy management, the appliance under investigation demonstrates not only favourable energy and emission parameters, but also the potential for the efficient use of thermal energy, which can bring additional economic and environmental benefits. Full article

Sharp-crested weirs are commonly used in hydraulic engineering for flow measurement and control. Despite extensive research on sharp-crested weirs, particularly regarding their discharge coefficients, more information is needed via research on their energy dissipation downstream. This study conducted experimental tests to assess the influence of contraction ratio (b/B) of rectangular sharp-crested weirs (RSCWs) on energy dissipation downstream under free flow conditions. Five RSCWs with different b/B equals 6/24, 7/24, 8/24, 9/24, and 10/24 were used. The results showed a consistent decrease in relative energy dissipation (Δ𝐸_𝑟) with an increase in the head over the weir. Furthermore, as the discharge per unit width (q) increased, the relative energy dissipation (Δ𝐸_𝑟) decreased, indicating more efficient discharge over the weir. A higher b/B further reduces Δ𝐸_𝑟, suggesting that wider weirs are more effective in minimizing energy losses. The maximum relative residual energy (E₁/E₀) and relative energy dissipation (Δ𝐸_𝑟) occurred at b/B = 10/24 and 6/24, with values of 0.825 and 0.613, respectively. Additionally, the maximum discharge coefficient (C_d) of RSCWs is found at b/B = 6/24, with an average value of 0.623. The results support the accuracy of the proposed equation with R² = 0.988, RMSE = 0.0083, and MAPE = 1.43%. Full article

Slope surface deformation monitoring plays an important role in landslide risk assessment and early warning. Currently, the mainstream GNSS, as a point-measurement technique, is expensive to deploy, resulting in information on only a few points of displacement being obtained on a target slope in practical applications. In contrast, optical images can contain more information on slope displacement at a much lower cost. Therefore, a low-cost, high-spatial-resolution and easy-to-implement landslide surface deformation monitoring system based on close-range photogrammetry is developed in this paper. The proposed system leverages multiple image processing methods and monocular visual localization, combined with machine learning, to ensure accurate monitoring under time series. The results of several laboratory landslide experiments show that the proposed system achieved millimeter-level monitoring accuracy in laboratory landslide experiments. Moreover, the proposed system could capture slow displacement precursors of 5 mm to 10 mm before significant landslide failure occurred, which provides favorable surface deformation evidence for landslide monitoring and early warning. In addition, the system was deployed on a natural slope in Lanzhou, yielding preliminary effective monitoring results. The laboratory experimental results demonstrated the system’s effectiveness and high accuracy in monitoring landslide surface deformation, particularly its significant application value in early warning. The field deployment results indicated that the system could also effectively provide data support in natural environments, offering practical evidence for landslide monitoring and warning. Full article

Background: Groundwater contamination with chlorinated hydrocarbons (CLHCs), particularly with tetrachloroethylene (PCE) and trichloroethylene (TCE), which are used in industry for degreasing and cleaning, can be considered a serious problem concerning the entire world. In addition to conventional groundwater monitoring from a network of wells, several screening methods have been proposed to identify and delineate groundwater contamination with volatile organic compounds (VOCs), such as soil gas measurement, bioindicators, direct-push technologies or geophysical techniques. The main objectives of this study were to confirm the feasibility of active soil gas screening for the characterisation of groundwater contamination with CLHCs under the wider area of the former refrigerator manufacturer (city of Zlaté Moravce, western Slovakia) and to evaluate the human health risks through exposure to CLHCs present in groundwater. Methods: a conventional site investigation based on concentration measurements using gas chromatography-mass spectrometry from monitoring wells and soil gas measurements using a portable photo-ionisation detector device were applied. Results: The chemical analyses showed the persistent contamination of groundwater, with PCE, TCE and other CLHCs, such as cis-1,2-dichloroethylene (cis-DCE) or 1,1,2-trichloroethane (TCA), being most severe in the zone of the former factory (up to 2690, 83,900, 6020 and 156 µg/L for PCE, TCE, cis-DCE and TCA, respectively), but also extended into the residential zone located 600 m along the groundwater flow line. Soil gas measurements of VOCs and other chemical parameters (methane (CH₄), total petroleum (TP), carbon dioxide (CO₂) and oxygen (O₂)) from a densely designed network of sampling points (n = 300) helped trace the current state of groundwater contamination. Spatial distribution maps of VOCs concentrations in soil gas clearly marked the areas of the highest CLHCs concentrations in groundwater. Principal component analysis (PCA) confirmed a significant correlation of VOCs and CLHCs with the first principal component, PC1, explaining up to 84% of the total variability of the concentration data, suggesting that VOCs in soil gas were a suitable marker of the extent of groundwater contamination with CLHCs. Despite severe groundwater contamination with CLHCs reaching residential areas, local residents were not exposed to non-carcinogenic risks, but a potential carcinogenic risk was present. Conclusions: based on the results, it could be confirmed that soil gas screening is an efficient and quick tool for identifying the sources of groundwater contamination with CLHCs as well as the level of this contamination. Full article

Accurate evaluation of permeability parameters is critical for the exploration and development of oil and gas fields. Among the available techniques, permeability assessment based on nuclear magnetic resonance (NMR) logging data is one of the most widely used and precise methods. However, the rapid biochemical variations in marine environments give rise to complex pore structures and strong reservoir heterogeneity, which diminish the effectiveness of traditional SDR and Timur–Coates models. To address these challenges in complex carbonate reservoirs, this study proposes a high-precision permeability evaluation method that integrates the Gaussian distribution model with the Thomeer model for more accurate permeability calculations using NMR logging data. Multimodal Gaussian distributions more accurately capture the size and distribution of multiscale pores. In this study, we innovatively employ the Gaussian distribution function to construct NMR-derived pseudo-pore size distribution curves. Subsequently, Thomeer model parameters are derived from Gaussian distribution parameters, enabling precise permeability calculation. The application of this method to the marine dolomite intervals of the Asmari Formation, Section A, within Oilfield A in southeastern Iraq, demonstrates its superior performance under both bimodal and unimodal pore size distributions. Compared to traditional models, this approach significantly reduces errors, providing crucial support for the accurate evaluation of complex reservoirs and the development of hydrocarbon resources. Full article

To achieve the regional goal of “double carbon”, it is necessary to map the carbon stock prediction for a wide area accurately and in a timely fashion. This paper introduces a long- and short-term memory network algorithm called the Self-Attention Convolutional Long and Short-Term Memory Network (SA-ConvLSTM). This paper takes the Wuhan urban circle of China as the research object, establishes a carbon stock AI prediction model, constructs a carbon stock change evaluation system, and investigates the correlation between carbon stock change and land use change during urban expansion. The results demonstrate that (1) the overall accuracy of the ConvLSTM and SA-ConvLSTM models improved by 4.68% and 4.70%, respectively, when compared to the traditional metacellular automata prediction methods (OS-CA, Open Space Cellular Automata Model), and for small sample categories such as barren land, shrubs, and grassland, the accuracy of SA-ConvLSTM increased by 17.15%, 43.12%, and 51.37%, respectively; (2) from 1999 to 2018, the carbon stock in the Wuhan urban area showed a decreasing trend, with an overall decrease of 6.49 × 10⁶ MgC. The encroachment of arable land due to rapid urbanization is the main reason for the decrease in carbon stock in the Wuhan urban area. From 2018 to 2023, the predicted value of carbon stock in the Wuhan urban area was expected to increase by 9.17 × 10⁴ MgC, mainly due to the conversion of water bodies into arable land, followed by the return of cropland to forest; (3) the historical spatial error model (SEM) indicates that for each unit decrease in carbon stock change, the Single Land Use Dynamic Degree (SLUDD) of water bodies and impervious surfaces will increase by 119 and 33 units, respectively. For forests, grasslands, and water bodies, the future spatial error model (SEM) indicated that for each unit increase in carbon stock change, the SLUDD would increase by 55, 7, and −305 units, respectively. This study demonstrates that we can use deep neural networks as a new method for predicting land use expansion, revealing the key impacts of land use change on carbon stock change from both historical and future perspectives and providing valuable insights for policymakers. Full article

Since the Industrial Revolution, underwater soundscapes have become more complex and contaminated due to increased cumulative human activities. Anthropogenic underwater sources have been growing in number, and shipping noise has become the primary source of chronic acoustic exposure. However, global data on current and historic noise levels is lacking. Here, using the Listening to the Deep-Ocean Environment network, we investigated the baseline shipping noise levels in thirteen observatories (eight stations from ONC Canada, four from the JAMSTEC network, and OBSEA in the Mediterranean Sea) and, in five of them, animal presence. Our main results show yearly noise variability in the studied locations that is not dominated by marine traffic but by natural and biological patterns. The halt in transportation due to COVID was insignificant when the data were recorded far from shipping routes. In order to better design a legislative framework for mitigating noise impacts, we highlight the importance of using tools that allow for long-term acoustic monitoring, automated detection of sounds, and big data handling and management. Full article

The aim of the research is to examine the biophysical, spatial, functional, and structural components of the pre-Hispanic terracing systems located in the ancient city of Pisaq, considering the impacts of tourism, geological instability, and cultural loss on the ecological and economic value of the terracing system. The methodology includes site analysis, climatology, and an examination of local flora and fauna, supported by digital tools such as QGIS 3.34, Google Earth Pro 2024, and Sun-Path. The results were primarily supported by the use of software tools such as QGIS, AutoCAD 2023, SketchUp 2022, 3D Sun-Path, D5 Render 2024, and Photoshop 2021. The findings include a biophysical analysis related to ecological and economic zoning (EEZ), which determines variables for preservation and reforestation; a spatial analysis measuring the cultivation terraces, with areas ranging from 4.89 ha to 110.20 ha; a functional analysis examining geophysical aspects such as seismic resistance and microclimate effects due to the greenhouse effect; and a constructive analysis that characterizes terrace typologies from an architectural perspective. In conclusion, this analysis evaluates the terracing system of the archaeological park to ensure its preservation and effective management. It also highlights that Inca culture has left a legacy of sustainable architecture, which aligns with the current Sustainable Development Goals (SDGs) (6, 11, 13, 15). Full article

Floods are one of the most devastating natural hazards that have intensified due to land use land cover (LULC) changes in recent years. Flood risk assessment is a crucial task for disaster management in flood-prone areas. In this study, we proposed a flood risk assessment framework that combines flood vulnerability, hazard, and damages under long-term LULC changes in the Tajan watershed, northern Iran. The research analyzed historical land use change trends and predicted changes up to 2040 by employing a Geographic Information System (GIS), remote sensing, and land change modeling. The flood vulnerability map was generated using the Random Forest model, incorporating historical data from 332 flooded locations and 12 geophysical and anthropogenic flood factors under LULC change scenarios. The potential flood damage costs in residential and agricultural areas, considering long-term LULC changes, were calculated using the HEC-RAS hydraulic model and a global damage function. The results revealed that unplanned urban growth, agricultural expansion, and deforestation near the river downstream amplify flood risk in 2040. High and very high flood vulnerability areas would increase by 43% in 2040 due to human activities and LULC changes. Estimated annual flood damage for agriculture and built-up areas was projected to surge from USD 162 million to USD 376 million and USD 91 million to USD 220 million, respectively, considering 2021 and 2040 land use change scenarios in the flood-prone region. This research highlights the importance of land use planning in mitigating flood-associated risks, both in the studied area and other flood-prone regions. Full article

Climate change threatens established agricultural systems and production, driving the need for adaptation and mitigation strategies. Vitiforestry, an alternative cultivation system combining trees and shrubs in the vineyard, promotes environmental sustainability and offers a possible adaptation strategy to climate change. This work scrutinizes the impact of shading on vineyards using an Integrated Model of Vineyard Shading and Climate Adaptation (IMVSCA), supported by a system dynamics approach. This model estimates solar radiation and computes daily and annual trends of insolation, air temperature, and relative humidity to shading and its influence on vineyard growth stages. It also assesses the effects of shading-related extreme weather events and the occurrence of grapevine disease development driven by daily weather conditions and zoning adaptations. The pilot results depict the effects of tree shading on vineyards, namely the impacts of solar radiation and air temperature on vine phenology, pollination, pollen germination, fungal diseases, and the complimentary indicators of grape production and quality. Our modeling framework and findings suggest that vitiforestry could be an interesting climate change adaptation technique, providing a starting point for further studies in this scope. Full article