Search Results (64)

This study presents a comprehensive analysis of CO₂ trapping mechanisms in subsurface reservoirs by integrating numerical reservoir simulations, geochemical modeling, and machine learning techniques to enhance the design and evaluation of carbon capture and storage (CCS) strategies. A two-dimensional reservoir model was developed to simulate CO₂ injection dynamics under realistic geomechanical and geochemical conditions, incorporating four primary trapping mechanisms: residual, solubility, mineralization, and structural trapping. To improve computational efficiency without compromising accuracy, advanced machine learning models, including random forest, gradient boosting, and decision trees, were deployed as smart proxy models for rapid prediction of trapping behavior across multiple scenarios. Simulation outcomes highlight the critical role of hysteresis, aquifer dynamics, and producer well placement in enhancing CO₂ trapping efficiency and maintaining long-term storage stability. To support the credibility of the model, a qualitative validation framework was implemented by comparing simulation results with benchmarked field studies and peer-reviewed numerical models. These comparisons confirm that the modeled mechanisms and trends align with established CCS behavior in real-world systems. Overall, the study demonstrates the value of combining traditional reservoir engineering with data-driven approaches to optimize CCS performance, offering scalable, reliable, and secure solutions for long-term carbon sequestration. Full article

The Jurassic Tamulangou Formation in the Hongqi Depression has favorable hydrocarbon generation conditions and great resource potential. This study systematically analyzes the geochemical characteristics and thermal evolution history of the source rocks using data from multiple key wells. The dark mudstone of the Tamulangou Formation has a thickness ranging from 50 to 200 m, with an average total organic carbon (TOC) content of 0.14–2.91%, an average chloroform bitumen “A” content of 0.168%, and an average hydrocarbon generation potential of 0.13–3.71 mg/g. The organic matter is primarily Type II and Type III kerogen, with an average vitrinite reflectance of 0.71–1.36%, indicating that the source rocks have generally reached the mature hydrocarbon generation stage and are classified as medium-quality source rocks. Thermal history simulation results show that the source rocks have undergone two major thermal evolution stages: a rapid heating phase from the Late Jurassic to Early Cretaceous and a slow cooling phase from the Late Cretaceous to the present. There are differences in the thermal evolution history of different parts of the Hongqi Depression. In the southern part, the Tamulangou Formation entered the hydrocarbon generation threshold at 138 Ma, reached the hydrocarbon generation peak at approximately 119 Ma, and is currently in a highly mature hydrocarbon generation stage. In contrast, the central part entered the hydrocarbon generation threshold at 128 Ma, reached a moderately mature stage around 74 Ma, and has remained at this stage to the present. Thermal history simulations indicate that the Hongqi Depression reached its maximum paleotemperature at 100 Ma in the Late Early Cretaceous. The temperature evolution pattern is characterized by an initial increase followed by a gradual decrease. During the Late Jurassic to Early Cretaceous, the Hongqi Depression experienced significant fault-controlled subsidence and sedimentation, with a maximum sedimentation rate of 340 m/Ma, accompanied by intense volcanic activity that created a high-temperature geothermal gradient of 40–65 °C/km, with paleotemperatures exceeding 140 °C and a heating rate of 1.38–2.02 °C/Ma. This thermal background is consistent with the relatively high thermal regime observed in northern Chinese basins during the Late Early Cretaceous. Subsequently, the basin underwent uplift and cooling, reducing subsidence and gradually lowering formation temperatures. Full article

Laser-induced breakdown spectroscopy (LIBS) has played a critical role in Mars exploration missions, substantially contributing to the geochemical analysis of Martian surface substances. However, the complex nonlinearity of LIBS processes can considerably limit the quantification accuracy of conventional LIBS chemometric methods. Hence chemometrics based on artificial neural network (ANN) algorithms have become increasingly popular in LIBS analysis due to their extraordinary ability in nonlinear feature modeling. The hidden layer activation functions are key to ANN model performance, yet common activation functions usually suffer from problems such as gradient vanishing (e.g., Sigmoid and Tanh) and dying neurons (e.g., ReLU). In this study, we propose a novel LIBS quantification method, named the Bayesian optimization-based tunable Softplus backpropagation neural network (BOTS-BPNN). Based on a dataset comprising 1800 LIBS spectra collected by a laboratory duplicate of the MarSCoDe instrument onboard the Zhurong Mars rover, we have revealed that a BPNN model adopting a tunable Softplus activation function can achieve higher prediction accuracy than BPNN models adopting other common activation functions if the tunable Softplus parameter β is properly selected. Moreover, the way to find the proper β value has also been investigated. We demonstrate that the Bayesian optimization method surpasses the traditional grid search method regarding both performance and efficiency. The BOTS-BPNN model also shows superior performance over other common machine learning models like random forest (RF). This work indicates the potential of BOTS-BPNN as an effective chemometric method for analyzing Mars in situ LIBS data and sheds light on the use of chemometrics for data analysis in future planetary explorations. Full article

Atmospheric pressure gradients determine the dynamics of the southwest monsoon (SWM) and northeast monsoon (NEM), resulting in rainfall in the Indian subcontinent. Consequently, the surface salinity, mixed layer, and thermocline are impacted by the seasonal freshwater outflow and direct rainfall. Moreover, seasonally reversing monsoon gyre and associated currents govern the northern Indian Ocean surface oceanography. This study provides an overview of the impact of these dynamic changes on sea surface temperature, salinity, and productivity by integrating more than 3000 planktonic foraminiferal censuses and bulk sediment geochemical data from sediment core tops, plankton tows, and nets between 25° N and 10° S and 40° E and 110° E of the past six decades. These data were used to construct spatial maps of the five most dominant planktonic foraminifers and illuminate their underlying environmental factors. Moreover, the cured foraminiferal censuses and the modern oceanographic data were used to test the newly developed artificial neural network (ANN) algorithm to calculate the relationship with modern water column temperatures (WCTs). Furthermore, the tested relationship between the ANN derived models was applied to two foraminiferal censuses from the northern Bay of Bengal core MGS29-GC02 (13°31′59″ N; 91°48′21″ E) and the southern Bay of Bengal Ocean Drilling Program (ODP) Site 758 (5°23.05′ N; 90°21.67′ E) to reconstruct the WCTs of the past 890 ka. The reconstructed WCTs at the 10 m water depth of core GC02 suggest dramatic changes in the sea surface during the deglacial periods (i.e., Bolling–Allerǿd and Younger Dryas) compared to the Holocene. The WCTs at Site 758 indicate a shift in the mixed-layer summer temperature during the past 890 ka at the ODP Site, in which the post-Mid-Brunhes period (at 425 ka) was overall warmer than during the prior time. However, the regional alkenone-derived sea-surface temperatures (SSTs) do not show such a shift in the mixed layer. Therefore, this study hypothesizes that the divergence in regional SSTs is most likely due to differences in seasonality and depth habitats in the paleo-proxies. Full article

Despite the increasing adoption of machine learning and data-driven models for predicting regional groundwater potential (GWP), exploration geoscientists have recognized that these models still face various challenges in their predictive precision. For instance, the stochastic uncertainty associated with incomplete groundwater investigation inventories and the inherent non-transparency characteristic of machine learning models, which lack transparency regarding how input features influence outcomes, pose significant challenges. This research constructs a bagging-based learning framework that integrates Positive–Unlabeled samples (BPUL), along with ex-post interpretability, to map the GWP of the Lijiang River Basin in China, a renowned karst region. For this purpose, we first aggregated various topographic, hydrological, geological, meteorological, and land conditional factors. The training samples were enhanced with data from the subterranean stream investigated in the study area, in addition to conventional groundwater inventories such as wells, boreholes, and karst springs. We employed the BPUL algorithm with four different base learners—Logistic Regression (LR), k-nearest neighbor (KNN), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM)—and model validation was conducted to map the GWP in karst regions. The findings indicate that all models exhibit satisfactory performance in GWP mapping, with the hybrid ensemble models (RF-BPUL and LightGBM-BPUL) achieving higher validation scores. The model interpretation of the aggregated SHAP values revealed the contribution patterns of various conditional factors to groundwater distribution in karst zones, emphasizing that lithology, the multiresolution index of valley bottom flatness (MRVBF), and the geochemical element calcium oxide (CaO) have the most significant impact on groundwater enrichment in karst zones. These findings offer new approaches and methodologies for the in-depth exploration and scientific prediction of groundwater potential. Full article

The İspir–Ahlatlı region in northeastern Türkiye, situated within the Eastern Pontides, hosts significant Miocene trachy-andesite volcanic rock exposures. This work seeks to elucidate their petrographic, geochemical, and isotopic compositions to enhance comprehension of their genesis and tectonic significance. Geochemistry reveals a transitional affinity, an enrichment in large-ion lithophile elements (LILEs), and a decrease in high-field-strength elements (HFSEs), suggesting a subduction-modified mantle source. Geochemical variations and fractional crystallisation trends indicate that the parental magma underwent significant differentiation, likely involving the fractionation of amphibole, clinopyroxene, and plagioclase. As supported by recent thermal modelling studies, the presence of intermediate volcanic rocks without associated bimodal suites in the study area may reflect elevated geothermal gradients and lithospheric delamination during post-collisional extension. The signatures indicated that the trachy-andesites originated in a post-collisional extensional environment after the closing of the Neo-Tethys Ocean and the ensuing tectonic reconfiguration of the Eastern Pontides. The reported geochemical traits correspond with post-collisional volcanic phases documented in various sectors of the Alpine–Himalayan orogenic system, such as the Eastern Pontides, the Iranian Plateau, and the Himalayan Belt, reinforcing the notion of a subduction-influenced mantle source. These findings increase the comprehension of magma formation in post-collisional settings and offer novel insights into the geodynamic context of the area. This research improves the understanding of post-collisional volcanic systems, their petrogenetic evolution, and their role in regional tectonic processes. Full article

This study applies machine learning (ML) techniques to classify fertile [for porphyry Cu and (or) Au systems] and barren adakites using geochemical data from New Brunswick, Canada. It emphasizes that not all intrusive units, including adakites, are inherently fertile and should not be directly used as the heat source evidence layer in mineral prospectivity mapping without prior analysis. Adakites play a crucial role in mineral exploration by helping distinguish between fertile and barren intrusive units, which significantly influence ore-forming processes. A dataset of 99 fertile and 66 barren adakites was analyzed using seven ML models: support vector machine (SVM), neural network, random forest (RF), decision tree, AdaBoost, gradient boosting, and logistic regression. These models were applied to classify 829 adakite samples from around the world into fertile and barren categories, with performance evaluated using area under the curve (AUC), classification accuracy, F1 score, precision, recall, and Matthews correlation coefficient (MCC). SVM achieved the highest performance (AUC = 0.91), followed by gradient boosting (0.90) and RF (0.89). For model validation, 160 globally recognized fertile adakites were selected from the dataset based on well-documented fertility characteristics. Among the tested models, SVM demonstrated the highest classification accuracy (93.75%), underscoring its effectiveness in distinguishing fertile from barren adakites for mineral prospectivity mapping. Statistical analysis and feature selection identified middle rare earth elements (REEs), including Gd and Dy, with Hf, as key indicators of fertility. A comprehensive analysis of 1596 scatter plots, generated from 57 geochemical variables, was conducted using linear discriminant analysis (LDA) to determine the most effective variable pairs for distinguishing fertile and barren adakites. The most informative scatter plots featured element vs. element combinations (e.g., Ga vs. Dy, Ga vs. Gd, and Pr vs. Gd), followed by element vs. major oxide (e.g., Fe₂O₃T vs. Gd and Al₂O₃ vs. Hf) and ratio vs. element (e.g., La/Sm vs. Gd, Rb/Sr vs. Hf) plots, whereas major oxide vs. major oxide, ratio vs. ratio, and major oxide vs. ratio plots had limited discriminatory power. Full article

Archaeal lipids, defining a primordial life domain alongside Bacteria and Eukarya, are distinguished by their unique glycerol-1-phosphate backbone and ether-linked isoprenoid chains. Serving as critical geochemical biomarkers, archaeal lipids like glycerol dialkyl glycerol tetraethers (GDGTs) underpin paleoclimate proxies, while their phylum-specific distributions illuminate phylogenetic divergence. Despite the maturity of Mass Spectrometry-based quantitative biomarkers—predominantly those with established structures—becoming well-established in geochemical research, systematic investigation of archaeal lipids as natural products has notably lagged. This deficit manifests across three key dimensions: (1) Extraction methodology lacks universal protocols adapted to diverse archaeal taxa and sample matrices. While comparative studies exist, theoretical frameworks guiding method selection remain underexplored. (2) Purification challenges persist due to the unique structures and complex isomerization profiles of archaeal lipids, hindering standardized separation protocols. (3) Most critically, structural characterization predominantly depends on decades-old foundational studies. However, the existing reviews prioritize chemical structural, biosynthetic, and applied aspects of archaeal lipids over analytical workflows. This review addresses this gap by adopting a natural product chemistry perspective, integrating three key aspects: (1) the clarification of applicable objects, scopes, and methodological mechanisms of various extraction technologies for archaeal lipids, encompassing both cultured and environmental samples; (2) the elucidation of separation principles underlying polar-gradient lipid fractionation processes, leveraging advanced chromatographic technologies; (3) the detailed exploration of applications for NMR in resolving complex lipid structures, with specialized emphasis on determining the stereochemical configuration. By synthesizing six decades of methodological evolution, we establish a comprehensive analytical framework, from lipids extraction to structural identification. This integrated approach constructs a systematic methodological paradigm for archaeal lipid analysis, bridging theoretical principles with practical implementation. Full article

In cemented paste backfill (CPB), fly ash (FA) can reduce cement costs. However, the chemical compositions of FA and tailings used in the CPB can vary significantly, affecting the strength values of CPBs, which can be determined through laboratory tests and play a crucial role in design operations. Therefore, developing a predictive model would be advantageous in terms of time and cost. The most critical aspect of this study is that machine learning (ML) models demonstrate high accuracy in the performance of strength prediction in experimental studies, especially in nonlinear and complex data structures, and even in the presence of uncertainty in geochemical and geophysical parameters. Among the ML algorithms, random forest (RF), artificial neural network (ANN), linear regression (LR), voting, and extreme gradient boosting (XGBoost) algorithms were used in this study. According to the results obtained, the XGBoost model exhibited the most robust predictive performance, evidenced by the highest correlation coefficient (R) (0.922) and the lowest mean absolute error (0.666). XGBoost also demonstrated its durability and stability by achieving the lowest relative absolute error (18.81%) and root mean square error (41.10%). Therefore, it has been understood that significant time and resource savings can be achieved in important projects by eliminating the need for experimental tests. Full article

Anthropogenic increases in the salinity of surface waters are referred to as secondary salinization. In surface waters, salinity levels can vary significantly due to various natural and anthropogenic influences. This article presents multi-decadal observations of changes in surface water salinity in the highly industrialized region in southern Poland. The case study of the Przemsza River is an example of the significant impacts of industrial, mainly coal mining, activities that have changed the chemical and biological characteristics of water bodies. The presented research revealed that impacts on salinity and water body status due to mining discharges will be difficult or even impossible to restore, considering the process of transition of the coal sector. In the Przemsza river basin, almost 42% less mine water was discharged in 2023 than in 1991. Parallelly, the salinity of mine waters discharged from deeper levels of active coal mines has increased due to the geochemical gradient (the total load of chlorides and sulfates was 534.8 MgCl⁻+SO₄²⁻ per day in 1991, while in 2023 the total salinity load was 480.1 MgCl⁻+SO₄²⁻ per day). Moreover, of the 19 active mine water discharges in 1991, only 11 remain in 2023, while the observed salinity of surface water in the Przemsza watershed increased rapidly from an average of 2000 µS·cm⁻¹ to 6700 µS·cm⁻¹ due to the significant drought and adverse hydrological conditions, which represent low flows never observed before (three times lower flows in the mouth of the Przemsza River in the period 2021–2023 compared to the previous decades 1991–2020). Impacts on water bodies will continue to occur regardless of mining activities in the area—it should be noted that at the end of exploitation, mine water rebound and flooding do not automatically reduce long-lasting impacts on surface waters. Therefore, salinization is a growing threat that might be amplified by climate change. While industrial and urban impacts on surface water change its characteristics, the future challenge of proper water management with a holistic approach is necessary with proper monitoring data collection and river flow-dependent and surface water salinity-dependent discharge of wastewater in the river basin. Full article

Large-scale and multi-sample datasets have revealed that microbial diversity and geographic distribution patterns are distinct across various habitats, particularly between hydrothermal vent and cold seep ecosystems. To date, our understanding of the effects of spatial and geochemical gradients on marine microbial communities remains limited. Here, we report the microbial diversity and metabolic versatility of a remote seafloor sediment ecosystem at different sites (GC-2, -4, -5, -6, -8) in the Mid-Okinawa Trough (Mid-OT) using high-throughput metagenomic sequencing technology. Desulfobacteraceae (3.1%) were detected in a high abundance at GC-2 with intense methane concentrations (353 μL/L), which showed a clear correlation with cold seeping. Whereas Candidatus Brocadiaceae (1.7%), Rhodobacteraceae (0.9%), and Rhodospirillaceae (0.7%), which are commonly involved in denitrification and sulfur oxidation, were enriched at GC-8. Concurrently investigating the potential of deep-sea microbial metabolism, we gained insights into the adaptive capabilities and metabolic mechanisms of microorganisms within seafloor environments. Utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, the analysis of functional modules revealed a significant enrichment (71–74%) of genes associated with metabolic pathways. These results expand our knowledge of the relationship between microbial biodiversity and metabolic versatility in deep-sea extreme environments. Full article

Sediment transport to the sea by rivers is crucial for the stability of estuaries and coasts. The Yangtze River, the largest river in China, like many large rivers worldwide, is experiencing a decrease in sediment load reaching the coast. However, the tidal flat around Qidong Cape, located at the entrance of the North Branch of the Yangtze Estuary, is undergoing extensive siltation. The source of this sediment is unclear. In this study, a sediment core was collected and the geochemical characteristics of rare earth elements (REE) were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). The results indicate the following: (1) The average content of REE is 178.57 μg/g, and the average ratio between LREE and HREE is 8.66, which is comparable to sediments from the South Yellow Sea. The chondrite-normalized and UCC-normalized patterns resemble those of the Yangtze River and the South Yellow Sea, indicating a negative gradient, a weak Ce-negative anomaly, and a distinct Eu-negative anomaly. (2) The continental shelf deposits in eastern China are primarily derived from sediment flux delivered by rivers. The sediments in the South Yellow Sea mainly originate from the Yangtze River and the Yellow River, exhibiting characteristics of a mixed source due to long-term geological processes, namely geochemical processes. The REEs in the tidal flat around Qidong Cape inherit the source area’s characteristics and originate from the weathering of upper continental rock in mainland China. Moreover, the tidal flat around Qidong Cape is influenced by both runoff and tidal actions, leading to strong land–sea interactions and reducing the environment, explaining the Eu-negative anomaly. (3) Hydrodynamic forces in the North Branch of the Yangtze River have shifted from runoff to tidal dominance since the 1930s. However, marine hydrodynamics outside the estuary have remained unchanged. Consequently, the Subei coastal current plays a key role in sediment transport and diffusion. Sediments from the south wing of the Radiative Sand Ridge in the South Yellow Sea are transported southward by the Subei coastal current, and under tidal influence, suspended sediment is deposited in the tidal flat around Qidong Cape. Therefore, the sediment source has gradually shifted from the Yangtze River to the South Yellow Sea. Full article

During the large-scale vegetation restoration on the Loess Plateau, the introduction of exotic species with high water consumption, such as Robinia pseudoacacia L., led to widespread soil desiccation, and resulted in severe drought stress and increasing risk of forest degradation and mortality. Accurate assessment of drought-induced mortality risk in plantation forests is essential for evaluating and enhancing the sustainability of ecological restoration, yet quantitative research at the regional scale on the Loess Plateau is lacking. With a focus on Robinia pseudoacacia L. plantations, we utilized a coupled model of the Biome BioGeochemical Cycles model and plant supply–demand hydraulic model (BBGC-SPERRY model) to simulate the dynamics of the annual average percentage loss of whole-plant hydraulic conductance (APLK) at 124 meteorological stations over an extended period (1961–2020) to examine changes in plant hydraulic safety in Robinia pseudoacacia L. plantations. Based on the probability distribution of APLK at each site, the drought-induced mortality risk probability (DMRP) in Robinia pseudoacacia L. was determined. The results indicate the BBGC-SPERRY model could effectively simulate the spatiotemporal variations in transpiration and evapotranspiration in Robinia pseudoacacia L. stands on the Loess Plateau. The mean APLK and DMRP exhibited increasing trends from southeast to northwest along a precipitation gradient, with their spatial patterns on the Loess Plateau mainly driven by mean annual precipitation and also significantly influenced by other climatic and soil factors. The low-risk (DMRP < 2%), moderate-risk (2% ≤ DMRP ≤ 5%), and high-risk (DMRP > 5%) zones for drought-induced mortality in Robinia pseudoacacia L. accounted for 60.0%, 30.7%, and 9.3% of the study area, respectively. These quantitative findings can provide an important basis for rational forestation and sustainable vegetation management on the Loess Plateau. Full article

The chemical distribution on the lunar surface results from the combined effects of both endogenic and exogenic geological processes. Exploring global maps of chemical composition helps to gain insights into the compositional variation among three major geological units, unraveling the geological evolution of the Moon. The existing oxide abundance maps were obtained from spectral images of remote sensing and geochemical data from samples returned by Apollo and Luna, missing the chemical characteristics of the Moon’s late critical period. In this study, by adding geochemical data from Chang’e (CE)-5 lunar samples, we construct inversion models between the Christiansen feature (CF) and oxide abundance of lunar samples using the particle swarm optimization–extreme gradient boosting (PSO-XGBoost) algorithm. Then, new global oxide maps (Al₂O₃, CaO, FeO, and MgO) and Mg# with the resolution of 32 pixels/degree (ppd) were produced, which reduced the space weathering effect to some extent. The PSO-XGBoost models were compared with partial least square regression (PLSR) models and four previous results, indicating that PSO-XGBoost models possess the capability to effectively describe nonlinear relationships between CF and oxide abundance. Furthermore, the average contents of our results and the Diviner results for 21 major maria demonstrate high correlations, with R² of 0.95, 0.82, 0.95, and 0.86, respectively. In addition, a new Mg# map was generated, which reveals different magmatic evolutionary processes in the three geologic units. Full article

Carbon steel and bentonite are materials selected as engineered barriers for high-level radioactive waste confinement. Their long-term interaction must be evaluated to confirm the barrier’s stability. Three laboratory experiments of the carbon steel—Mg-bentonite interaction were conducted for 1, 6, and 22 months under a hydrothermal gradient. Changes in bentonite’s water content, specific surface area, and cation exchange capacity were measured. Mineralogy was studied by X-ray diffraction and scanning electron microscopy. The redistribution of aqueous species and the redox state of iron were determined across the bentonite columns. Results indicated water saturation after 22 months. The specific surface area of bentonite was reduced near contact with the steel, while the cation exchange capacity mostly decreased at 3–6 mm from the steel interface. The corrosion rate decreased with time and bentonite enriched in Fe in the first 1.5 mm from the steel contact. The formation of new Fe-bearing minerals, such as di-tri ferri-sudoite, magnetite, hematite, maghemite, lepidocrocite, siderite and ankerite was observed. Aqueous species redistributed in the porewater of bentonite with decreasing concentrations of Fe and Cl as a function of time and increasing concentrations of Na, Ca and SO₄ after 22 months. This occurs under conditions where the bentonite is saturated with Mg, which conditioned the formation and nature of iron clay minerals with time. Full article