Search Results (330)

To study hydrogeological characteristics after the occurrence of abnormal water bursts from the weak water-rich (permeable) aquifer of the Changxing Formation limestone overlying deep working faces during production in Guizhou karst landform mining areas, hydrogeological data covering the exploration and production periods of the Xinhua mining region in Jinsha County, Guizhou Province, were collected. On the basis of surface and underground drilling, geophysical exploration techniques, empirical equations, and indoor material simulation methods, the hydrogeological evolution characteristics of the Changxing Formation limestone in the mining region after mining damage to coalbed 9 were studied. The research results indicated that the ratio of the height of the roof failure fracture zone (as obtained via numerical simulation and ground borehole detection) to the mining height exceeded 25.78, which is far greater than the empirical model calculation values (from 13.0 to 15.8). After mining the underlying coalbed 9, an abnormal water-rich area developed in the Changxing Formation limestone, and mining damage fractures led to the connection of the original dissolution fissures and karst caves within the limestone, resulting in the weak water-rich (permeable) aquifer of the Changxing Formation limestone becoming a strong water-rich (permeable) aquifer, which served as the water source for mine water bursts. Over time, after mining damage occurrence, the voids in the Changxing Formation limestone were gradually filled with various substances, yielding water storage space and connectivity decreases. The specific yield decreased with an increasing water burst time and interval after the cessation of mining in the supply area, and the correlation coefficient R was 0.964, indicating a high degree of correlation between the two parameters. Full article

This study examines the use of a mass-abatement scalable system with managed aquifer recharge (MAR-MASS) as a sustainable solution for restoring salinized aquifers and improving water quality by removing dissolved salts. It offers a practical remediation approach for aquifers affected by salinization in coastal regions, agricultural areas, and contaminated sites, where variable-density flow poses a challenge. Numerical simulations assessed hydrogeological properties such as hydraulic conductivity, anisotropy, specific yield, mechanical dispersion, and molecular diffusion. A conceptual model integrated hydraulic conditions with spatial and temporal discretization using the FLOPY API for MODFLOW 6 and the IFM API for FEFLOW 10. Python algorithms were run within the high-performance computing (HPC) server, executing simulations in parallel to efficiently process a large number of scenarios, including both preprocessing input data and post-processing results. The study simulated 6950 scenarios, each modeling flow and transport processes over 3000 days of method implementation and focusing on mass extraction efficiency under different initial salinity conditions (3.5 to 35 kg/m³). The results show that the MAR-MASS effectively removed salts from aquifers, with higher hydraulic conductivity prolonging mass removal efficiency. Of the scenarios, 88% achieved potability (0.5 kg/m³) in under five years; among these, 79% achieved potability within two years, and 92% of cases with initial concentrations of 3.5–17.5 kg/m³ reached potability within 480 days. This study advances scientific knowledge by providing a robust model for optimizing managed aquifer recharge, with practical applications in rehabilitating salinized aquifers and improving water quality. Future research may explore MAR-MASS adaptation for diverse hydrogeological contexts and its long-term performance. Full article

A mass abatement scalable system through managed aquifer recharge (MAR-MASS) improves mass extraction from groundwater with a variable-density flow. This method is superior to conventional injection systems because it promotes uniform mass displacement, reduces density gradients, and increases mass extraction efficiency over time. Simulations of various scenarios involving hydrogeologic variables, including hydraulic conductivity, vertical anisotropy, specific yield, mechanical dispersion, molecular diffusion, and mass concentration in aquifers, have identified critical variables and parameters influencing mass transport interactions to optimize the system. MAR-MASS is adaptable across hydrogeologic conditions in aquifers that are 25–75 m thick, comprising unconsolidated materials with hydraulic conductivities between 5 and 100 m/d. It is effective in scenarios near coastal areas or in aquifers with variable-density flows within the continent, with mass concentrations of salts or solutes ranging from 3.5 to 35 kg/m³. This system employs a modular approach that offers scalable and adaptable solutions for mass extraction at specific locations. The integration of programming tools, such as Python 3.13.2, along with technological strategies utilizing parallelization techniques and high-performance computing, has facilitated the development and validation of MAR-MASS in mass extraction with remarkable efficiency. This study confirmed the utility of these tools for performing calculations, analyzing information, and managing databases in hydrogeologic models. Combining these technologies is critical for achieving precise and efficient results that would not be achievable without them, emphasizing the importance of an advanced technological approach in high-level hydrogeologic research. By enhancing groundwater quality within a comparatively short time frame, expanding freshwater availability, and supporting sustainable aquifer recharge practices, MAR-MASS is essential for improving water resource management. Full article

Groundwater contamination by nitrates (NO₃⁻) and nitrites (NO₂⁻) is an urgent problem in rural areas of Eastern Europe, with profound public health and sustainability implications. This paper presents an integrated assessment of groundwater vulnerability and water quality in rural wells in the Ceanu Mare commune, Cluj County, Romania—a representative area of the Northern Transylvania Basin, characterized by diverse geological structures, intensive agricultural activities, and incomplete public water infrastructure. This study combines detailed hydrochemical analyses, household-level studies, and geological context to identify and quantify key factors influencing nitrate and microbial contamination in rural wells, providing a comprehensive perspective on water quality challenges in the central part of Romania. This study adopts a multidisciplinary approach, integrating detailed geotechnical investigations conducted through four strategically located boreholes. These are complemented by extensive hydrogeological and lithological characterization, as well as rigorous chemical and microbiological analyses of nearby wells. The results reveal persistently elevated concentrations of NO₃⁻ and NO₂⁻, commonly associated with inadequate livestock waste management and the proximity of manure storage areas. Microbiological contamination was also frequent. In this study, the NO₃⁻ levels in well water ranged from 39.7 to 48 mg/L, reaching up to 96% of the EU/WHO threshold (50 mg/L), while the NO₂⁻ concentrations varied from 0.50 to 0.69 mg/L, exceeding the legal limit (0.5 mg/L) in 87% of the sampled wells. Ammonium (NH₄⁺) was detected (0.25–0.34 mg/L) in all the wells, below the maximum allowed limit (0.5 mg/L) but indicative of ongoing organic pollution. All the well water samples were non-compliant for microbiological parameters, with E. coli detected in 100% of cases (5–13 CFU/100 mL). The regional clay–marl substrate offers only limited natural protection against pollutant infiltration, primarily due to lithological heterogeneity and discontinuities observed within the clay–marl layers in the study area. This research delivers a replicable model for rural groundwater assessment and addresses a critical gap in regional and European water safety studies. It also provides actionable recommendations for sustainable groundwater management, infrastructure development, and community risk reduction in line with EU water directives. Full article

Karst mountain areas, as complex geological systems formed by carbonate rock development, possess unique three-dimensional spatial structures and hydrogeological processes that fundamentally influence regional ecosystem evolution, land resource assessment, and sustainable development strategy formulation. In recent years, through the implementation of systematic ecological restoration projects, the ecological degradation of karst mountain areas in Southwest China has been significantly curbed. However, the research on the fine-grained land use mapping and quantitative characterization of spatial heterogeneity in karst mountain areas is still insufficient. This knowledge gap impedes scientific decision-making and precise policy formulation for regional ecological environment management. Hence, this paper proposes a novel methodology for land use mapping in karst mountain areas using very high resolution (VHR) remote sensing (RS) images. The innovation of this method lies in the introduction of strategies of geographical zoning and stratified object extraction. The former divides the complex mountain areas into manageable subregions to provide computational units and introduces a priori data for providing constraint boundaries, while the latter implements a processing mechanism with a deep learning (DL) of hierarchical semantic boundary-guided network (HBGNet) for different geographic objects of building, water, cropland, orchard, forest-grassland, and other land use features. Guanling and Zhenfeng counties in the Huajiang section of the Beipanjiang River Basin, China, are selected to conduct the experimental validation. The proposed method achieved notable accuracy metrics with an overall accuracy (OA) of 0.815 and a mean intersection over union (mIoU) of 0.688. Comparative analysis demonstrated the superior performance of advanced DL networks when augmented with priori knowledge in geographical zoning and stratified object extraction. The approach provides a robust mapping framework for generating fine-grained land use data in karst landscapes, which is beneficial for supporting academic research, governmental analysis, and related applications. Full article

Accurate permeability predictions remain pivotal for understanding fluid flow in porous media, influencing crucial operations across petroleum engineering, hydrogeology, and related fields. Traditional approaches, while robust, often grapple with the inherent heterogeneity of reservoir rocks. With the advent of deep learning, convolutional neural networks (CNNs) have emerged as potent tools in image-based permeability estimation, capitalizing on micro-CT scans and digital rock imagery. This paper introduces a novel paradigm, employing vision transformers (ViTs)—a recent advancement in computer vision—for this crucial task. ViTs, which segment images into fixed-sized patches and process them through transformer architectures, present a promising alternative to CNNs. We present a methodology for implementing ViTs for permeability prediction, its results on diverse rock samples, and a comparison against conventional CNNs. The prediction results suggest that, with adequate training data, ViTs can match or surpass the predictive accuracy of CNNs, especially in rocks exhibiting significant heterogeneity. This study underscores the potential of ViTs as an innovative tool in permeability prediction, paving the way for further research and integration into mainstream reservoir characterization workflows. Full article

To ensure the green, safe, and efficient extraction of mineral resources and promote sustainability, the stability of mined-out areas has become a critical factor affecting safe production and ecological restoration in underground metal mines. The instability of underground goafs poses a significant threat to mine safety, especially when irregular excavation patterns interact with high ground stress, exacerbating instability risks. Most existing studies lack a systematic and multidisciplinary integrated framework for comprehensive evaluation and management. This paper proposes a trinity research system of “assessment–optimization–governance”, integrating theoretical analysis, three-dimensional fluid–solid coupling numerical simulation, and a filling sequence optimization method based on genetic algorithms. An analysis of data measured from 243 pillars and 49 goafs indicates that approximately 20–30% of the pillars have a factor of safety (FoS) below 1.0, signaling immediate instability risks; additionally, 58% do not meet the threshold for long-term stability (FoS ≥ 1.5). Statistical and spatial analyses highlight that pillar width-to-height ratio (W/H) and cross-sectional area significantly influence stability; when W/H exceeds 1.5, FoS typically surpasses 2.0. Numerical simulations reveal pore water pressures of 1.4–1.8 MPa in deeper goafs, substantially reducing effective stress and accelerating plastic zone expansion. Stability classification categorizes the 49 goafs into 7 “poor”, 37 “moderate”, and 5 “good” zones. A genetic algorithm-optimized filling sequence prioritizes high-risk area remediation, reducing maximum principal stress by 60.96% and pore pressure by 28.6%. Cemented waste rock filling applied in high-risk areas, complemented by general waste rock filling in moderate-risk areas, significantly enhances overall stability. This integrated method provides a scientific foundation for stability assessment and dynamic remediation planning under complex hydrogeological conditions, offering a risk-informed and scenario-specific application of existing tools that improves engineering applicability. Full article

Shear wave velocity (V_s) is a key geomechanical variable in subsurface exploration, essential for hydrocarbon reservoirs, geothermal reserves, aquifers, and emerging use cases, like carbon capture and storage (CCS), offshore geohazard assessment, and deep Earth exploration. Despite its broad significance, no comprehensive multidisciplinary review has evaluated the latest applications, estimation methods, and challenges in V_s prediction. This study provides a critical review of these aspects, focusing on energy-efficient prediction techniques, including geophysical surveys, remote sensing, and artificial intelligence (AI). AI-driven models, particularly machine learning (ML) and deep learning (DL), have demonstrated superior accuracy by capturing complex subsurface relationships and integrating diverse datasets. While AI offers automation and reduces reliance on extensive field data, challenges remain, including data availability, model interpretability, and generalization across geological settings. Findings indicate that integrating AI with geophysical and remote sensing methods has the potential to enhance V_s prediction, providing a cost-effective and sustainable alternative to conventional approaches. Additionally, key challenges in V_s estimation are identified, with recommendations for future research. This review offers valuable insights for geoscientists and engineers in petroleum engineering, mining, geophysics, geology, hydrogeology, and geotechnics. Full article

Groundwater is a crucial source of water supply and an important ecological element globally. Research on the dynamic characteristics of groundwater and their causative mechanisms is fundamental to objectively evaluating groundwater resources and their sustainable utilization. Based on the large amount of hydrogeological data collected and analyzed in typical hilly plain areas, a multi-factor weighted comprehensive evaluation system (MFWCES) based on GIS was used to evaluate the response of groundwater dynamics to combined stress elements in Tangshan City. The study area is located in the plains and hilly regions of Tangshan City. The evaluation system was based on seven influencing factors, including hydraulic conductivity, soil media, aquifer thickness, depth of groundwater, land use type, extraction intensity of groundwater, and groundwater evaporation. The results of groundwater dynamics in the study area were obtained by weighted comprehensive evaluation, with their score size ranging from 2.4 to 12.7. The spatial distribution of groundwater dynamics was classified into four categories: rapid response (10.3–12.7), dual response to precipitation and anthropogenic extraction (9.6–10.3), delayed response (7.6–9.6), and strong superimposed response to human activities (2.4–7.6). The related conclusions will provide key references for regional water resource planning, ecological protection, and the development of differentiated groundwater management strategies under compound stress. Full article

Springs located in urban historic areas are important for groundwater management, the protection of green spaces, and the preservation of park functions and urban structure. This article presents the results of a study of selected Warsaw springs in the city center under conservation protection, focusing on their hydrogeological characteristics, hydrogeochemical analysis, and pressures associated with urban development. Field and laboratory analyses, as well as hydrodynamic modeling, made it possible to assess the quantity and quality of water from the springs. Hydrodynamic studies showed that the area of the spring recharge zone of 13.77 ha is characterized by an average time of water exchange of approx. 26 years and a low infiltration recharge, an average of 18 mm/year. Hydrogeochemical analyses showed that spring water has a complex, multi-ion hydrogeochemical type: Cl-SO₄-HCO₃-Ca-Na, Cl-HCO₃-SO₄-Ca-Na, Cl-HCO₃-Na-Ca, and NO₃-Cl-HCO₃-Ca-Na, including the occurrence of hazardous substances such as PAH and BTEX, PCBs, non-ionic detergents, and heavy metals. The results indicate that urbanization significantly affects groundwater levels and spring recharge areas, which can limit the availability of water in green and recreational areas. The results of the study indicate the need for action to increase groundwater resources through managed aquifer recharge for rainwater management in densely built-up areas. In terms of water quality measures, due to the unsatisfactory chemical water status, the use of spring water for irrigation of urban vegetation or its incorporation into the active recreational infrastructure of the park currently appears to be fraught with considerable risk, hence the need to take protective action in the spring recharge zone through the regular monitoring of groundwater quality, the legal designation of protection zones, and the implementation of policies that support urban water retention. It is necessary to implement pre-treatment solutions (aeration, desalination) or introduce appropriately resistant vegetation. Any type of activity that allows the use of water after treatment will certainly contribute to making the park more attractive as a place of recreation and leisure for residents. Findings from the research can support decisions on protecting green spaces and adapting cities to climate change. Full article

Climate change, marked by decreasing rainfall and increasing extreme events, represents a major challenge for water resources, particularly in semi-arid regions. To estimate aquifer recharge, it is essential to assess the fraction of precipitation contributing to groundwater recharge and to implement a water balance model. However, the limited number of rainfall stations has led researchers to rely on satellite and reanalysis rainfall products. The accuracy of these datasets is essential for reliable hydrological modeling. In this study, we evaluated five rainfall products—CHIRPS, ERA5_Ag, CFSR, GPM, and PERSIANN-CDR—by comparing them to ground measurements from gauging stations in the central Haouz region of Marrakech. The evaluation was conducted at three temporal scales: daily, monthly, and annual. Statistical metrics, including RMSE, MAE, NSE, Bias, and Pearson correlation, as well as classification metrics (accuracy, F1 score, recall, precision, and Cohen’s Kappa), and wavelet analysis, were applied to assess the accuracy of the products. The results identified ERA5_Ag and GPM as the most accurate products in capturing rainfall events. Nevertheless, ERA5_Ag showed a high bias. After applying the quantile mapping method to correct the bias, the product exhibited greater accuracy. The corrected datasets from these two products will be used to estimate recharge over the last 30 years, contributing to the development of a hydrogeological model for groundwater dynamics. Full article

Slope failures in overconsolidated hard clays present significant geotechnical challenges, particularly in stratified formations prone to pre-existing discontinuities. Despite extensive research on residual shear strength and fissuring in stiff clays, the role of undetected tension cracks and their interaction with hydrogeological conditions in temporary excavations remains underexplored. This study addresses this research gap through a detailed case study of a slope failure during an unsupported residential excavation on Corfu Island, Greece. The investigation aimed to identify the failure mechanism, assess the influence of geological discontinuities and groundwater conditions, and evaluate the contribution of residual shear strength to slope stability. The methodology combined field observations, laboratory testing (including unconfined compression and ring shear tests), and numerical modelling using both finite element (FEM) and limit equilibrium (LEM) approaches. The results revealed that a nearly vertical, pre-existing fissure—acting as a tension crack—and water infiltration along the clay–sandstone interface significantly reduced the factor of safety, triggering a planar slide. Both FEM and LEM analyses indicated that critical conditions for failure were reached with a residual friction angle of 19°, inclined sandstone layers at 15–17°, and hydrostatic pressure from groundwater accumulation. This study demonstrates the compounded destabilizing effects of undetected discontinuities and water pressures in stratified hard clays and underscores the necessity of comprehensive geotechnical assessments for temporary excavations, even in seemingly stable formations. Full article

This study addresses the limitations of machine learning in regional groundwater dynamics research, particularly the insufficient integration of the hydrogeological background and low simulation accuracy. Focusing on the shallow groundwater in the Hebei Plain south of Beijing and Tianjin, we integrate static data, including hydrogeological parameters, with the commonly used time-series data. A novel regionalization strategy based on depositional systems is proposed to enhance the model’s spatial adaptability. The Long Short-Term Memory (LSTM) model, augmented with an attention mechanism, adjusts the dynamic model weights using static data to reflect geological impacts on groundwater dynamics. Comparative results show that the refined regionalization and the inclusion of static data significantly improve the accuracy of the model. Based on the fitting results, the comparison of shallow groundwater level prediction between 2023 and 2040 under two mining conditions shows that the continuous implementation of the pressure mining policy has accelerated the recovery of water level, and the rise in groundwater level is obviously different between regions. The alluvial fan in the piedmont has the largest rise, and the marine sedimentary plain has the smallest rise. This study provides a new method for analyzing groundwater dynamics under complex hydrogeological conditions and provides a basis for regional groundwater management and sustainable utilization. Full article

Water–sediment evolution and attribution analysis in watersheds is one of the research focuses of hydrogeology. An in-depth investigation into the spatiotemporal variation of water and sediment at multiple spatial scales within the basin, along with a systematic assessment of the respective impacts of climate change and human activities, provides a scientific foundation for formulating effective soil and water conservation practices and integrated water resource management strategies. This research holds significant implications for the sustainable development and ecological management of the basin. In this study, the Mann–Kendall nonparametric test method, double cumulative curve method, cumulative anomaly method, and cumulative slope change rate analysis method were used to quantitatively study the effects of climate change and human activities on runoff and sediment load changes at different spatial scales in the Huangfuchuan River basin. The results show that (1) from 1966 to 2020, the annual runoff and annual sediment load discharge in the Huangfuchuan River basin showed a significant decreasing trend. Among them, the reduction in runoff and sediment in the control sub-basin of Shagedu Station in the upper reaches was more obvious than that in the whole basin. The mutation points of runoff and sediment load in the two basins were 1979 and 1998. The water–sediment relationship exhibits a power function pattern. (2) After the abrupt change, in the change period B (1980–1997), the contribution rates of climate change and human activities to runoff and sediment load reduction in the Huangfuchuan River basin were 24.12%, 75.88% and 20.05%, 79.95%, respectively. In the change period C (1998–2020), the contribution rates of the two factors to the runoff and sediment load reduction in the Huangfuchuan River basin were 18.91%, 81.09% and 15.61%, 84.39%, respectively. Among them, the influence of precipitation in the upper reaches of the Huangfuchuan River basin on the change in runoff and sediment load is higher than that of the whole basin, and the influence on the decrease of sediment load discharge is more significant before 1998. There are certain stage differences and spatial scale effects. (3) Human activities such as large-scale vegetation restoration and construction of silt dam engineering measures are the main reasons for the reduction in runoff and sediment load in the Huangfuchuan River basin and have played a greater role after 1998. Full article