Search Results (557)

Magnetic parameters of river sediments are commonly used as end-members for source tracing in the coasts and shelves. The eastern continental shelf area of China, with multiple sources of input, is a key region for discussing sediment sources. However, magnetic parameters are influenced by grain size, and the nature of this influence remains unclear. In this study, the Jiulong River was selected as a case to analyze the magnetic parameters and mineral characteristics for both the coarse (>63 μm) and fine-grained (<63 μm) fractions. Results show that the magnetic minerals mainly contain detrital-sourced magnetite and hematite. In the North River, a tributary of the Jiulong River, the content of coarse-grained magnetic minerals increases from upstream to downstream, contrary to fine-grained magnetic minerals, suggesting the influence of hydrodynamic forces. Some samples with abnormally high magnetic susceptibility may result from the combined influence of the parent rock and human activities. In the scatter diagrams of magnetic parameters for provenance tracing, samples of the <63 μm fractions have a more concentrated distribution than that of the >63 μm fractions. Hence, magnetic parameters for the <63 μm fraction are more useful in provenance identification. Full article

Rock bolts are crucial components in the subterranean support systems in underground mines that provide adequate structural reinforcement to the rock mass to prevent unforeseen hazards like rockfalls. This makes frequent assessments of such bolts critical for maintaining rock mass stability and minimising risks in underground mining operations. Where manual surveying of rock bolts is challenging due to the low-light conditions in the underground mines and the time-intensive nature of the process, automated detection of rock bolts serves as a plausible solution. To that end, this study focuses on the automatic identification of rock bolts within medium- to large-scale 3D point clouds obtained from underground mines using mobile laser scanners. Existing techniques for automated rock bolt identification primarily rely on feature engineering and traditional machine learning approaches. However, such techniques lack robustness as these point clouds present several challenges due to data noise, varying environments, and complex surrounding structures. Moreover, the target rock bolts are extremely small objects within large-scale point clouds and are often partially obscured due to the application of reinforcement shotcrete. Addressing these challenges, this paper proposes an approach termed DeepBolt, which employs a novel two-stage deep learning architecture specifically designed for handling severe class imbalance for the automatic and efficient identification of rock bolts in complex 3D point clouds. The proposed method surpasses state-of-the-art semantic segmentation models by up to 42.5% in Intersection over Union (IoU) for rock bolt points. Additionally, it outperforms existing rock bolt identification techniques, achieving a 96.41% precision and 96.96% recall in classifying rock bolts, demonstrating its robustness and effectiveness in complex underground environments. Full article

China is one of the countries severely affected by earthquakes, making precise and timely identification of earthquake precursors essential for reducing casualties and property damage. A novel method is proposed that combines a rock acoustic emission (AE) detection technique with deep learning methods to facilitate real-time monitoring and advance earthquake precursor detection. The AE equipment and seismometers were installed in a granite tunnel 150 m deep in the mountains of eastern Guangdong, China, allowing for the collection of experimental data on the correlation between rock AE and seismic activity. The deep learning model uses features from rock AE time series, including AE events, rate, frequency, and amplitude, as inputs, and estimates the likelihood of seismic events as the output. Precursor features are extracted to create the AE and seismic dataset, and three deep learning models are trained using neural networks, with validation and testing. The results show that after 1000 training cycles, the deep learning model achieves an accuracy of 98.7% on the validation set. On the test set, it reaches a recognition accuracy of 97.6%, with a recall rate of 99.6% and an F₁ score of 0.975. Additionally, it successfully identified the two biggest seismic events during the monitoring period, confirming its effectiveness in practical applications. Compared to traditional analysis methods, the deep learning model can automatically process and analyse recorded massive AE data, enabling real-time monitoring of seismic events and timely earthquake warning in the future. This study serves as a valuable reference for earthquake disaster prevention and intelligent early warning. Full article

An important aspect of geoscience and energy research is the analysis of microscopic images, where the assessment of rock properties combines imaging methods with numerical analysis. Given the significant advancements in generative artificial intelligence technologies in recent years, which have enabled the creation of realistic images, a need arises to assess the authenticity of synthetic visual data compared to authentic geological data images. This article evaluates the potential for identifying artificially generated microscopic rock images. Synthetic images were generated using a widely accessible diffusion model, based on real training data. Expert evaluation noted high realism, though some structural and rock-type differences remained detectable. In the study, image descriptors were analyzed to assess their usefulness in distinguishing synthetic data from real data. Discriminative feature selection was conducted, and the effectiveness of various classification models based on the selected parameter sets was compared. The study also proposes a heuristic coefficient demonstrating discriminative potential for the analyzed images. The results confirm the feasibility of building classifiers for synthetic images that could aid in detecting generated visual data in geological and petrographic research. They also serve as a foundation for further exploration of the importance of individual features in such applications. Full article

Analyzing the spatiotemporal characteristics of microseismic monitoring data is crucial for the monitoring and early prediction of coal–rock dynamic disasters during deep mining. Aiming to address the challenges hampering the early prediction of coal–rock dynamic disasters in deep mining, in this paper, we propose a method for analyzing the spatiotemporal characteristics of microseismic events in deep mining based on the ST-DBSCAN algorithm. First, a spatiotemporal distance metric model integrating temporal and spatial distances was constructed to accurately describe the correlations between microseismic events in spatiotemporal dimensions. Second, along with the spatiotemporal distribution characteristics of microseismic data, we determined the spatiotemporal neighborhood parameters suitable for deep-mining environments. Finally, we conducted clustering analysis of 14 sets of actual microseismic monitoring data from the Xinjulong Coal Mine. The results demonstrate the precise identification of two characteristic clusters, namely middle-layer mining disturbances and deep-seated activities, along with isolated high-magnitude events posing significant risks. Full article

This study addresses the limitations of conventional evaluation methods caused by low porosity, strong heterogeneity, and complex pore structures in tight sandstone reservoirs. Through integrated rock physics experiments and multi-physical field modeling, the research systematically investigates the coupled response mechanisms between electrical and elastic parameters. The experimental approach includes pore structure characterization, quantitative mineral composition analysis, resistivity and polarizability measurements under various saturation conditions, P- and S-wave velocity testing, and scanning electron microscopy (SEM) imaging. The key findings show that increasing porosity leads to significant reductions in resistivity and elastic wave velocities, while also increasing surface conductivity. Specifically, clay minerals enhance surface conductivity through interfacial polarization effects and decrease rock stiffness, which exacerbates wave velocity attenuation. Furthermore, resistivity exhibits a nonlinear negative correlation with water saturation, with sharp increases at low saturation levels due to the disruption of conductive pathways. By integrating the Modified Generalized Effective Medium Theory of Induced Polarization (MGEMTIP) and Kuster–Toksöz models, this study establishes quantitative relationships between porosity, saturation, and electrical/elastic parameters, and constructs cross-plot templates that correlate elastic wave velocities with resistivity and surface conductivity. These analyses reveal that high-porosity, high-saturation zones are characterized by lower resistivity and wave velocities, coupled with significantly higher surface conductivity. The proposed methodology significantly improves the accuracy of reservoir evaluation and enhances fluid identification capabilities, providing a solid theoretical foundation for the efficient exploration and development of tight sandstone reservoirs. Full article

Alkaline carbonatite complexes are formed by magmatic, hydrothermal, and weathering geological events, which modify the minerals present in the rocks, resulting in ores with varied metallurgical behavior. To better spatially distinguish ores with distinct plant responses, creating a 3D geometallurgical block model was necessary. To establish the clusters, four different algorithms were tested: K-Means, Hierarchical Agglomerative Clustering, dual-space clustering (DSC), and clustering by autocorrelation statistics. The chosen method was DSC, which can consider the multivariate and spatial aspects of data simultaneously. To better understand each cluster’s mineralogy, an XRD analysis was conducted, shedding light on why each cluster performs differently in the plant: cluster 0 contains high magnetite content, explaining its strong magnetic yield; cluster 3 has low pyrochlore, resulting in reduced flotation yield; cluster 2 shows high pyrochlore and low gangue minerals, leading to the best overall performance; cluster 1 contains significant quartz and monazite, indicating relevance for rare earth elements. A hierarchical indicator kriging workflow incorporating a stochastic partial differential equation (SPDE) trend model was applied to spatially map these domains. This improved the deposit’s circular geometry reproduction and better represented the lithological distribution. The elaborated model allowed the identification of four geometallurgical zones with distinct mineralogical profiles and processing behaviors, leading to a more robust model for operational decision-making. Full article

Gravity and gravity gradient data serve as fundamental inputs for geophysical resource exploration and geological structure analysis. However, traditional denoising methods—including wavelet transforms, moving averages, and low-pass filtering—exhibit signal loss and limited adaptability under complex, non-stationary noise conditions. To address these challenges, this study proposes an improved U-Net deep learning framework that integrates multi-scale feature extraction and attention mechanisms. Furthermore, a Laplace consistency constraint is introduced into the loss function to enhance denoising performance and physical interpretability. Notably, the datasets used in this study are generated by the authors, involving simulations of subsurface prism distributions with realistic density perturbations (±20% of typical rock densities) and the addition of controlled Gaussian noise (5%, 10%, 15%, and 30%) to simulate field-like conditions, ensuring the diversity and physical relevance of training samples. Experimental validation on these synthetic datasets and real field datasets demonstrates the superiority of the proposed method over conventional techniques. For noise levels of 5%, 10%, 15%, and 30% in test sets, the improved U-Net achieves Peak Signal-to-Noise Ratios (PSNR) of 59.13 dB, 52.03 dB, 48.62 dB, and 48.81 dB, respectively, outperforming wavelet transforms, moving averages, and low-pass filtering by 10–30 dB. In multi-component gravity gradient denoising, our method excels in detail preservation and noise suppression, improving Structural Similarity Index (SSIM) by 15–25%. Field data tests further confirm enhanced identification of key geological anomalies and overall data quality improvement. In summary, the improved U-Net not only delivers quantitative advancements in gravity data denoising but also provides a novel approach for high-precision geophysical data preprocessing. Full article

This study presents a comprehensive methodology for the detection and characterization of fractures in geological samples using X-ray computed tomography (CT). By combining convolution-based image processing techniques with advanced neural network-based segmentation, the proposed approach achieves high precision in identifying complex fracture networks. The method was applied to a marly limestone sample from the Maiolica Formation, part of the Umbria–Marche stratigraphic succession (Northern Apennines, Italy), a geological context where fractures often vary in size and contrast and are frequently filled with minerals such as calcite or clays, making their detection challenging. A critical part of the work involved addressing multiple sources of uncertainty that can impact fracture identification and measurement. These included the inherent spatial resolution limit of the CT system (voxel size of 70.69 μm), low contrast between fractures and the surrounding matrix, artifacts introduced by the tomographic reconstruction process (specifically the Radon transform), and noise from both the imaging system and environmental factors. To mitigate these challenges, we employed a series of preprocessing steps such as Gaussian and median filtering to enhance image quality and reduce noise, scanning from multiple angles to improve data redundancy, and intensity normalization to compensate for shading artifacts. The neural network segmentation demonstrated superior capability in distinguishing fractures filled with various materials from the host rock, overcoming the limitations observed in traditional convolution-based methods. Overall, this integrated workflow significantly improves the reliability and accuracy of fracture quantification in CT data, providing a robust and reproducible framework for the analysis of discontinuities in heterogeneous and complex geological materials. Full article

Geological surveys have vital importance at the planning stage of dammed reservoir construction projects. The results of these surveys determine the majority of the technical solutions adopted in the construction design to ensure the proper safety and stability parameters of the structure during water damming. Where the ground type is found to be different from what is expected, the construction project may be delayed or even cancelled. This study analyses issues and design modifications caused by the identification of different soil conditions during the construction of four new flood control reservoirs in the Nysa Kłodzka River basin in south-western Poland. The key findings are as follows: (1) a higher density of exploratory boreholes in areas with potentially fractured rock mass is essential for selecting the appropriate anti-filtration protection; (2) when deciding to apply deep piles, it is reasonable to verify, at the planning stage, whether they can be installed using the given technology directly at the planned site; (3) inaccurate identification of foundation soils under the dam body can lead to significant design modifications—in contrast, a denser borehole grid helps to determine the precise elevation of the base layer, which is essential for reliably estimating the volume of material required for the embankment; (4) in order to correctly assess the soil deposits located, for instance, in the reservoir basin area, it is more effective to use test excavations rather than relying solely on borehole-based investigations—as a last resort, test excavations can be used to supplement the latter. Full article

As an important unconventional oil and gas resource, tight oil exploration and development is of great significance to ensure energy supply under the background of continuous growth of global energy demand. Low-porosity and low-permeability reservoirs are characterized by tight rock properties, poor physical properties, and complex pore structure, and as a result the fine calculation of logging reservoir parameters faces great challenges. In addition, the crude oil in this area has high viscosity, the formation water salinity is low, and the oil reservoir resistivity shows significant spatial variability in the horizontal direction, which further increases the difficulty of oil and water reservoir identification and affects the accuracy of oil saturation calculation. Targeting the above problems, the Nutcracker Optimization Algorithm (NOA) was used to optimize the hyperparameters of the random forest classification model, and then the optimal hyperparameters were input into the random forest model, and the conventional logging curve and oil test data were combined to identify and classify the reservoir fluids, with the final accuracy reaching 94.92%. Compared with the traditional Hingle map intersection method, the accuracy of this method is improved by 14.92%, which verifies the reliability of the model for fluid identification of low-porosity and low-permeability reservoirs in the research block and provides reference significance for the next oil test and production test layer in this block. Full article

The Aghbar-Bou Azzer East mining district (ABED) is located between the Bou Azzer East and Aghbar deposits. It is an area of approximately 7 km long towards ENE–WSW and 2 km wide towards N–S. In this barren area, volcano-sedimentary rocks are attributed to the Ouarzazate group outcrop (Ediacarian age): they are composed of volcanic rocks (ignimbrite, andesite, rhyolite, dacite, etc.) covered by the Adoudou detritic formation in angular unconformity. Given the absence of serpentinite outcrops, exploration investigation in this area has been very limited. This paper aims to use ionic leach geochemistry (on samples of soil) to detect the presence of Co-bearing arsenides above hidden ore deposits in this unexplored area of the Bou Azzer inlier. In addition, a detailed structural analysis allowed the identification of four families of faults and fractures with or without filling. Three directional major fault systems of several kilometers in length and variable orientation in both the Cryogenian basement and the Ediacaran cover have been identified: (i) ENE–WSW, (ii) NE–SW, and (iii) NW–SE. Several geochemical anomalies for Co, As, Ni, Ag, and Cu are aligned along three main directions, including NE–SW, NW–SE, and ENE–WSW. They are particularly well-defined in the western zone but are only minor in the central and eastern zones. Some of these anomalies correlate with the primary structural features observed in the studied area. These trends are consistent with those known under mining exploitation in nearby ore deposits, supporting the potential for similar mineralization in the ABED. Based on structural analysis and ionic leach geochemistry, drilling programs were conducted in the study area, confirming the continuity of serpentinites at depth beneath the Ediacaran cover and the presence of Co–Fe-bearing arsenide ores. This validates the ionic geochemistry technique as a reliable method for exploring buried ore deposits. Full article

This study systematically analyzes reservoir formation mechanisms under deep burial conditions, integrating macroscopic observations from representative ultra-deep clastic reservoirs in four major sedimentary basins in central and western China. Developing effective clastic reservoirs in ultra-deep strata (6000–8000 m) remains a critical yet debated topic in petroleum geology. Recent advances in exploration techniques and geological understanding have challenged conventional views, confirming the presence of viable clastic reservoirs at such depths. Findings reveal that reservoir quality in ultra-deep strata is preserved and enhanced through the interplay of sedimentary, diagenetic, and tectonic processes. Key controlling factors include (1) high-energy depositional environments promoting primary porosity development, (2) proximity to hydrocarbon source rocks enabling multi-phase hydrocarbon charging, (3) overpressure and low geothermal gradients reducing cementation and compaction, and (4) late-stage tectonic fracturing that significantly improves permeability. Additionally, dissolution porosity and fracture networks formed during diagenetic and tectonic evolution collectively enhance reservoir potential. The identification of favorable reservoir zones under the sedimentation–diagenesis-tectonics model provides critical insights for future hydrocarbon exploration in ultra-deep clastic sequences. Full article

Groundwater nitrate (NO₃⁻) contamination has emerged as a critical global environmental issue, posing serious human health risks. This study systematically investigated the hydrochemical processes, sources of NO₃⁻ pollution, the impact of land use on NO₃⁻ pollution, and drinking water safety in an urban area of southwestern China. Thirty-one groundwater samples were collected and analyzed for major hydrochemical parameters and dual isotopic composition of NO₃⁻ (δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻). The groundwater samples were characterized by neutral to slightly alkaline nature, and were dominated by the Ca-HCO₃ type. Hydrochemical analysis revealed that water–rock interactions, including carbonate dissolution, silicate weathering, and cation exchange, were the primary natural processes controlling hydrochemistry. Additionally, anthropogenic influences have significantly altered NO₃⁻ concentration. A total of 19.35% of the samples exceeded the Chinese guideline limit of 20 mg/L for NO₃⁻. Isotopic evidence suggested that primary sources of NO₃⁻ in groundwater include NH₄⁺-based fertilizer, soil organic nitrogen, sewage, and manure. Spatial distribution maps indicated that the spatial distribution of NO₃⁻ concentration correlated strongly with land use types. Elevated NO₃⁻ levels were observed in areas dominated by agriculture and artificial surfaces, while lower concentrations were associated with grass-covered ridge areas. The unabsorbed NH₄⁺ from nitrogen fertilizer entered groundwater along with precipitation and irrigation water infiltration. The direct discharge of domestic sewage and improper disposal of livestock manure contributed substantially to NO₃⁻ pollution. The nitrogen fixation capacity of the grassland ecosystem led to a relatively low NO₃⁻ concentration in the ridge region. Despite elevated NO₃⁻ and F⁻ concentrations, the entropy weighted water quality index (EWQI) indicated that all groundwater samples were suitable for drinking. This study provides valuable insights into NO₃⁻ source identification and hydrochemical processes across varying land-use types. Full article

In recent years, significant exploration successes and research progress in volcanic hydrocarbon reservoirs across China’s offshore basins have highlighted their importance as key targets for deep hydrocarbon exploration. In the Shimentan Formation of the Xihu Sag, East China Sea Shelf Basin (ECSSB), low-yield gas flows have been encountered through exploratory drilling; however, no major reservoir breakthroughs have yet been achieved. Assessing the large-scale reservoir potential of volcanic sequences in the Shimentan Formation is thus critical for guiding future exploration strategies. Based on previous exploration studies of volcanic reservoirs in other Chinese basins, this study systematically evaluates the hydrocarbon potential of these volcanic units by microscopic thin section identification, major element analysis, integrates drilling data with seismic interpretation techniques—such as coherence cube slicing for identifying volcanic conduits, dip angle analysis for classifying volcanic edifices, and waveform classification for delineating volcanic lithofacies. The main findings are as follows: (1) The Shimentan Formation is primarily composed of intermediate to acidic pyroclastic rocks and lava flows. Volcanic facies are divided into three facies, four subfacies, and six microfacies. Volcanic edifices are categorized into four types: stratified, pseudostratified, pseudostratified-massive, and massive. (2) Extensive pseudostratified volcanic edifices are developed in the Hangzhou Slope Zone, where simple and compound lava flows of effusive facies are widely distributed. (3) Comparative analysis with prolific volcanic reservoirs in the Songliao and Bohai Bay basins indicates that productive reservoirs are typically associated with simple or compound lava flows within pseudostratified edifices. Furthermore, widespread Late Cretaceous rhyolites in adjacent areas of the study region suggest promising potential for rhyolitic reservoir development in the Hangzhou Slope Zone. These results provide a robust geological foundation for Mesozoic volcanic reservoir exploration in the Xihu Sag and offer a methodological framework for evaluating reservoir potential in underexplored volcanic regions. Full article