Search Results (106)

Heavy metal contamination in agricultural soils poses serious threats to food safety, ecosystem integrity, and public health. This study investigates the concentrations, ecological risks, and human health impacts of nine heavy metals Cr, Mn, Co, Ni, Cu, Zn, Pb, As, and V in homestead agricultural soils collected from two depths, surface (0–20 cm) and subsurface (21–50 cm), across industrial and non-industrial regions of Bangladesh, using inductively coupled plasma mass spectrometry (ICP-MS). Results revealed that surface soils from industrial areas exhibited the highest metal concentrations in order of Mn > Zn > Cr > Pb > V > Ni > Cu > As > Co. However, maximum As levels were detected in non-industrial areas, suggesting combined influences of local geology, intensive pesticide application, and prolonged irrigation with As-contaminated groundwater. Elevated concentrations in surface soils indicate recent contamination with limited downward migration. Multivariate statistical analyses indicated that industrial and urban activities are the major sources of contamination, whereas Mn remains primarily geogenic, controlled by natural soil forming processes. Contamination factor (CF) and pollution load index (PLI) analyses identified Pb and As as the principal pollutants, with hotspots in Nairadi, Majhipara (Savar), Gazipur sadar, and Chorkhai (Mymensingh). Ecological risk (ER) assessment highlighted As and Pb as the dominant environmental stressors, though overall risk remained low. Human health risk analysis showed that ingestion is the primary exposure pathway, with children being more susceptible than adults. Although the hazard index (HI) values were within the acceptable safety limits, the estimated carcinogenic risks for As and Cr exceeded the USEPA thresholds, indicating potential long term health concerns. Therefore, the cumulative carcinogenic risk (CCR) results demonstrate that As is the primary driver of lifetime carcinogenic risk in homestead soils, followed by Cr, while contributions from other metals are minimal. These findings emphasize the urgent need for continuous monitoring, improved industrial waste management, and targeted mitigation strategies to ensure safe food production, a cleaner environment, and better public health. Full article

Optimization algorithms play a crucial role in solving complex problems in reservoir geology and engineering, particularly those involving highly non-linear, multi-parameter, and high-dimensional systems. In the context of reservoir development, accurate optimization is essential for enhancing hydrocarbon recovery, improving production efficiency, and managing subsurface uncertainties. The Moss Growth Optimization (MGO) algorithm emulates the adaptive growth and reproductive strategies of moss. It provides a robust bio-inspired framework for global optimization. However, MGO often suffers from slow convergence and difficulty in escaping local optima in highly multimodal landscapes. To address these limitations, this paper proposes a novel algorithm called Strategic Moss Growth Optimization (SMGO). SMGO integrates two enhancements: an Outpost Mechanism (OM) and an Early Stopping Strategy (ESS). The OM improves exploitation by guiding individuals through multi-stage local search with Gaussian-distributed exploration around promising regions. This helps refine the search and prevents stagnation in sub-optimal areas. In parallel, the ESS periodically reinitializes the population using a run-and-reset procedure. This diversification allows the algorithm to escape local minima and maintain population diversity. Together, these strategies enable SMGO to accelerate convergence while ensuring solution quality. Its performance is rigorously evaluated on a suite of global optimization benchmarks and compared with state-of-the-art metaheuristics. The results show that SMGO achieves superior or highly competitive outcomes, with clear improvements in accuracy and stability. To demonstrate real-world applicability, SMGO is applied to production optimization in tight reservoirs. The algorithm identifies superior production strategies, leading to significant improvements in projected economic returns. This successful application highlights the robustness and practical value of SMGO. It offers a powerful and reliable optimization tool for complex engineering problems, particularly in strategic resource management for tight reservoir development. Full article

The direct current resistivity method holds advantages such as rapid, efficient, and automatic data acquisition. It is an important geophysical exploration technology for monitoring dynamic changes in subsurface geology. However, this method has such issues as volume effect and non-uniqueness in inversion. To meet the demand for high-resolution direct current resistivity inversion of dynamic geological models characterized by discontinuous changes, this study proposed a cross-gradient constrained time-lapse inversion method, thereby enhancing inversion imaging accuracy. A cross-gradient constraint term between models was incorporated into the objective function of time-lapse inversion to constrain the structural consistency and highlight local resistivity changes. This method avoided excessively smooth imaging as often caused by over-reliance on a reference model in time-lapse inversion, thereby significantly improving both the spatial resolution and quantitative accuracy of direct current resistivity monitoring inversion images. Numerical examples confirmed that the proposed method delivers higher inversion imaging accuracy in identifying dynamic resistivity changes, evidenced by a substantially lower normalized mean-square error (MSE). Furthermore, physical model experiments and a case study confirmed the stability of this method under actual monitoring conditions. The proposed method provides a more precise and effective inversion imaging technique for refined monitoring of dynamic changes in subsurface geologic bodies. Full article

A managed aquifer recharge (MAR) project utilizing reclaimed water has been operated for over 10 years in northeastern Beijing, China, with the goal of restoring the long-dried Chaobai River and replenishing the region’s depleted groundwater resources. To ensure the safe implementation of the project, we quantitatively assessed the impact of river water recharge on the multi-layered groundwater system by investigating the hydrochemical compositions of the reclaimed water, river water, and groundwater. Results show that river water is characterized by higher concentrations of Na⁺, Cl⁻, and SO₄²⁻ than found in groundwater, and that river water recharge has altered the groundwater types in the 30 m-depth unconfined layer, changing them from Ca-Mg-HCO₃ and Ca-HCO₃ types to Na-Ca-HCO₃-Cl and Ca-Mg-Na-HCO₃ types. End-member mixing analyses of river water samples indicate that three end-members are needed to represent the seasonal and spatial variations in river water. A five-end-member mixing model is then developed to quantify fractions of river water (f_R) in different aquifer layers. The estimated f_R values vary from 18.4% to 100%, with an average of 67.6% in the 30 m-depth layer, while f_R values in the 80 m-depth confined layer are mainly below 30%, with an average of 13.3%, which corresponds well to the known site geology. Overall, combining hydrochemical analysis with the end-member mixing approach is useful for assessing the impact of river recharge on groundwater. This study also highlights the need for high-resolution characterization of subsurface heterogeneity in MAR sites. Full article

This study provides a detailed geochronological paragenesis of fracture systems from the Upper Jurassic petroleum source formation in NE Iraq, utilizing U-Pb dating, integrated with microprobe analyses and petrographic studies. Five fracturing stages are recognized (FI–FV), indicating significant tectonic and temperature changes from the Late Jurassic to Pliocene times (approximately 5.2–5.5 Ma). The burial history curve shows continuous subsidence events, starting with initial burial of the Barsarin Formation reaching depths of 1000–1200 m by 110 Ma, this depth interval coincides with the first fracturing stage (FI). The buffered system of FI by pristine facies and geometrical cross-cutting of FI with early stylolite formation show a prior formation of stylolite. Subsequent fracturing stages FII (28.6 ± 2 Ma, Oligocene) and FIII (19.83 ± 0.43 Ma, Early Miocene) were contemporaneous with tectonic deformation phases and hydrocarbon generation times. Microprobe and optical analyses demonstrate variations in mineralogical composition, particularly in FIV/FV-filled calcite and dolomite cements (12.2 ± 1.5 Ma and 5.5 Ma), highlighting the periods of conduit formation for the hydrocarbon migration. Backscattered electron (BSE) imaging reveals a textural alteration of these cements, especially those associated with fluorite precipitation, which further support the hydrothermal entrapment associated with the hydrocarbon migration. The hydrocarbon entrapment appeared in at least two episodes under subsurface setting under temperatures exceeding 100 °C. In summary, the significant meaningful ages and compositional analyses obtained from this study reveal crucial insights into the dynamics of fracture-filling cements and hydrocarbon entrapment mechanisms within the petroleum source rock formation. The novelty of these data would enhance our understanding of the complex relationship between structural geology and migration conduits, highlighting the influence of fracture-filling cements on hydrocarbon accumulation and reservoir quality as a main target for hydrocarbon field development. Full article

This study presents an integrated approach for lunar subsurface excavation by combining Tunnel Boring Machine (TBM) technology with microwave-assisted rock weakening and machine learning-based strength prediction methods. Through comprehensive analysis of lunar environmental conditions and geological characteristics, we address the key challenges of subsurface construction on the Moon. Our machine learning models, trained on terrestrial rock data and calibrated with Apollo mission samples, provide reliable predictions of lunar rock strength. Laboratory experiments demonstrate that microwave irradiation can reduce rock strength by 19% within three minutes, significantly enhancing excavation efficiency. The integration of these techniques with TBM technology offers practical solutions for developing lunar habitats while effectively managing challenges posed by extreme temperatures, vacuum conditions, and abrasive regolith. The demonstrated 19% reduction in rock strength through microwave treatment indicates significant potential for enhancing lunar excavation efficiency, though operational implementation requires further development. Our findings indicate that this combined approach of rock weakening and strength prediction methods can substantially improve the technical and economic feasibility of lunar subsurface construction. Full article

This study investigates ground deformations in the southeastern Po Plain (northern Italy), focusing on the Bologna area—a densely populated region affected by natural and anthropogenic subsidence. Ground deformations in the area result from geological processes (e.g., sediment compaction and tectonic activity) and human activities (e.g., ground water production and underground gas storage—UGS). We apply a multidisciplinary approach integrating subsurface geology, ground water production, advanced differential interferometry synthetic aperture radar—DInSAR, gas storage data, and land use information to characterize and analyze the spatial and temporal variations in vertical ground deformations. Seasonal and trend decomposition using loess (STL) and cluster analysis techniques are applied to historical DInSAR vertical time series, targeting three representatives areas close to the city of Bologna. The main contribution of the study is the attempt to correlate the lateral extension of ground water bodies with seasonal ground deformations and water production data; the results are validated via knowledge of the geological characteristics of the uppermost part of the Po Plain area. Distinct seasonal patterns are identified and correlated with ground water production withdrawal and UGS operations. The results highlight the influence of superficial aquifer characteristics—particularly the geometry, lateral extent, and hydraulic properties of sedimentary bodies—on the ground movements behavior. This case study outlines an effective multidisciplinary approach for subsidence characterization providing critical insights for risk assessment and mitigation strategies, relevant for the future development of CO₂ and hydrogen storage in depleted reservoirs and saline aquifers. 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

Probabilistic models are used to describe random processes and quantify prediction uncertainties in a principled way. Examples include geotechnical and geological investigations that seek to model subsurface hydrostratigraphic properties or mineral deposits. In mining geology, model validation efforts have generally lagged behind the development and deployment of computational models. One problem is the lack of industry guidelines for evaluating the uncertainty and predictive performance of probabilistic ore grade models. This paper aims to bridge this gap by developing a holistic approach that is autonomous, scalable and transferable across domains. The proposed model assessment targets three objectives. First, we aim to ensure that the predictions are reasonably calibrated with probabilities. Second, statistics are viewed as images to help facilitate large-scale simultaneous comparisons for multiple models across space and time, spanning multiple regions and inference periods. Third, variogram ratios are used to objectively measure the spatial fidelity of models. In this study, we examine models created by ordinary kriging and the Gaussian process in conjunction with sequential or random field simulations. The assessments are underpinned by statistics that evaluate the model’s predictive distributions relative to the ground truth. These statistics are standardised, interpretable and amenable to significance testing. The proposed methods are demonstrated using extensive data from a real copper mine in a grade estimation task and are accompanied by an open-source implementation. The experiments are designed to emphasise data diversity and convey insights, such as the increased difficulty of future-bench prediction (extrapolation) relative to in situ regression (interpolation). This work enables competing models to be evaluated consistently and the robustness and validity of probabilistic predictions to be tested, and it makes cross-study comparison possible irrespective of site conditions. Full article

The Nubia Sandstone in the Gulf of Suez, Egypt, is a well-known unclassified sediment. Palynology is considered the most effective tool for dealing with this problem. Miospore assemblages from the Lowermost Carboniferous (Tournaisian) have been discovered from the J62-86 and the J62-64 AST1 wells located in the July Field of the Gulf of Suez, Egypt. Spores are moderately to poorly preserved, suggesting a stratigraphical position within Lowermost Carboniferous ages. The studied sediments include poorly preserved conodont fragments and present significant identification challenges due to the drilling methodologies’ complexities. Spore assemblage consists of 31 genera with 56 species. The dominant spores include zonate genera Vallatisporites, Densosporites, and Archaeozonotriletes, camerate genera Grandispora, Geminospora, apiculate genera Apiculiretusispora, and laevigate trilete genus Punctatisporites and megaspores of the Lagenoisporites type are recorded. Marine microphytoplankton including Schizocystia bicornuta, Lophosphaeridium, Leiosphaerida, and some filamentous green algae of unknown affinity are recorded. The dispersed spore assemblage is associated with carbonized plant fragments. The palynological data have effectively dated the lower intervals of the Nubia Sandstone from the Nubia “B,” indicating a Lowermost Carboniferous (Tournaisian) age, i.e., Vallatisporites vallatus–Retustriletes incohatus palynozone (VI). The stratigraphic differentiation of the Nubia Sandstone is crucial for subsequent correlating subsurface wells in the Gulf of Suez within the context of geology and hydrocarbon exploration, particularly given the scarcity of other fossil groups. Full article

Shielings are seasonal settlements found in upland pastures across Scandinavia and the North Atlantic. New investigations in the county of Møre and Romsdal, Norway, demonstrate the existence of this transhumant system by the Viking Age and Early Middle Ages. Sub-terranean features in these pastoral mountain landscapes have been identified by remote sensing technologies, but non-invasive methods still face challenges in terms of practical applicability and in confirming the presence of archaeological sites. Generally, aerial surveys, such as LiDAR and image-based modelling, excel in documenting visual landscapes and may enhance detection of low-visibility features. Thermography may also detect shallow subsurface features but is limited by solar conditions and vegetation. Magnetic methods face challenges due to the heterogeneous moraine geology. Ground-penetrating radar has yielded better results but is highly impractical and inefficient in these remote and rough landscapes. Systematic soil coring or test-pitting remain the most reliable options for detecting these faint sites, yet non-invasive methods may offer a better understanding of the archaeological contexts—between the initial survey and the final excavation. Altogether, the study highlights the dependency on landscape, soil, and vegetation, emphasising the need to consider each method’s possibilities and limitations based on site environments and conditions. Full article

Bench-scale geological modeling is often uncertain due to limited exploration drilling and geophysical wireline measurements, reducing production efficiency. Measure-While-Drilling (MWD) systems collect drilling data to analyze mining blast hole drill rig performance. Early MWD studies focused on penetration rates to identify rock types. This paper investigates Artificial Intelligence (AI)-based regression models to predict geophysical signatures like density, gamma, magnetic susceptibility, resistivity, and hole diameter using MWD data. The machine learning (ML) models evaluated include Linear Regression (LR), Decision Trees (DTs), Support Vector Machines (SVMs), Random Forests (RFs), Gaussian Processes (GP), and Neural Networks (NNs). An analytical method was validated for accuracy, and a three-tier experimental method assessed the importance of MWD features, revealing no performance loss when excluding features with less than 2% importance. RF, DTs, and GPs outperformed other models, achieving R² values up to 0.98 with a low RMSE, while LR and SVMs showed lower accuracy. The NN’s performance improved with larger datasets. This study concludes that the DT, RF, and GP models excel in predicting geophysical signatures. While ML-based methods effectively model relationships in the data, their predictive performance remains inherently constrained by the underlying geological and physical mechanisms. Model selection depends on computational resources and application needs, offering valuable insights for real-time orebody analysis using AI. These findings could be invaluable to geologists who wish to utilize AI techniques for real-time orebody analysis and prediction. Full article

The Environmental Consulting Industry in the United States has historically prioritized engineering approaches over geologic science in addressing groundwater contamination. This engineering-centric bias has often resulted in oversimplified conceptual site models (CSMs) that fail to capture subsurface heterogeneity, limiting the effectiveness of groundwater remediation strategies. Recognizing the critical role of geology, the industry is increasingly adopting a Remediation Geology approach, which emphasizes the development of robust geologic models as the foundation for remediation programs. Geologic models optimize site lithologic data to define subsurface permeability architecture. The geologic model primarily serves as the structure to develop a Process-Based CSM, which is a holistic model that supports the entire remediation life cycle. A Process-Based CSM addresses the physical, chemical, and biological processes governing contaminant occurrence with the goal of modeling and predicting subsurface conditions for improved decision making with respect to monitoring programs and remediation design. Case studies highlight the transformative impact of Remediation Geology and Process-Based CSMs, demonstrating significant improvements in cleanup efficiency and resource optimization across diverse hydrogeologic settings. By addressing site complexities such as fine-grained units and fracture networks, Remediation Geology and Process-Based CSMs have proven effective for contaminants ranging from chlorinated solvents to per- and polyfluoroalkyl substances (PFASs) and radionuclides. Full article

This study focuses on the geometric and geophysical characterization of sedimentary landslides in the Laguna del Amor area, located in Chota-Cajamarca (Peru). The main objective was to identify key static factors related to landslide susceptibility, including slope angle, soil composition, and groundwater flow, prioritizing the areas affected by landslides. Electrical Resistivity Tomography (ERT) was the geophysical method selected because of its effectiveness in delineating subsurface geometries, detecting water content, and assessing mass movements. The methodology combined geophysical analysis (ERT), field geology, and photogrammetry to develop a detailed subsurface model. The results indicate a rotational landslide mainly composed of weathered shales and limestones, with highly saturated zones that increase the area’s hazard level. The investigation also identified significant variability in landslide depth throughout the study area, highlighting the importance of these factors in geotechnical risk assessment. This interdisciplinary approach not only contributes to geological knowledge of the area but also provides critical information for mitigation and risk management strategies in landslide-prone areas. Full article

This study aims to better harness the geothermal potential of Mount Meager in British Columbia, a premier reserve of geothermal resources in Canada. Numerical investigations explore the feasibility and optimization of an Enhanced Geothermal System to boost geothermal energy extraction capabilities. Utilizing COMSOL Multiphysics, the model simulates non-isothermal fluid flow and heat transfer through complex subsurface geology with discrete fracture planes. The sensitivity analyses assess the impact of various operational parameters, including injection strategies, reservoir characteristics, and wellbore configurations on heat extraction efficiency. These analyses indicate that a higher injection rate, lower injection temperatures, and optimized fracture areas significantly enhance system performance by maximizing thermal energy capture and minimizing thermal breakthrough. Additionally, specific wellbore configurations, particularly the triplet setup with deeper depth, significantly improve geothermal fluid circulation and heat extraction compared to doublet configurations at shallower depths. This study reveals that the base case scenario of the EGS could generate approximately $8.311\times {10}^9$ kWh over 30 years, while optimization strategies could elevate potential production to up to $16.68\times {10}^9$ kWh. These findings underscore the critical role of carefully designed operational strategies that leverage local geological and thermal characteristics to optimize geothermal systems, thereby enhancing efficiency and promoting sustainable energy development at Mount Meager. Full article