Natural Resources Research最新文献

The surface movement law induced by continuous mining across multiple working faces is distinct compared to that of a single working face. It is essential to understand and analyze this law to ensure the safety of coal mining operations. This study employed a research method that integrates numerical simulation and theoretical analysis to define, for the first time, the concepts of the repeated mining subsidence ratio and seemingly full mining. The analysis of ground surface movement in multiple mine working faces revealed that: The ground surface in multiple mine working faces within the Loess Plateau coal mines experienced multiple movements, with the center of subsidence deviating from the center of the working face. In the direction of surface inclination, the subsidence followed a cyclic pattern as it approached full mining, with the center of subsidence shifting away from the center of the mining area and positioning itself atop the spacer coal pillar. Multiple mine working faces intensify surface deformation and prolong surface movement. Spacer coal pillars between adjacent mine working faces provide structural support to surface subsidence deformation. Surface movement deformation results from the combined effects of slope slippage and mining-induced subsidence. The findings of this study establish a foundation for further research on surface movement and deformation in multiple mine working faces in the Loess Plateau coal mines.

A pivotal aspect in the evaluation of mining projects is the classification of mineral resources, which directly influences the definition of mineral reserves and significantly impacts mine planning and operational stages. However, the current classification methodologies often need specificity regarding the methods and parameters employed and heavily rely on the qualified/competent person’s judgment. This study addresses these gaps by proposing a pioneering fuzzy approach to assess grade and tonnage uncertainties. By allowing for overlapping resource categories and directly incorporating economic, geological, metallurgical, environmental, and operational criteria, we aim to provide tools for decision-making and for the final classification and public disclosure of mineral resources and reserves. We demonstrate the potential of our proposed methodology through an application to an iron ore deposit case study and through a detailed discussion on its uses, contributions, strengths, weaknesses, and on whether it complies with international reporting codes.

Gas hydrate is anticipated to serve as a viable substitute for traditional fossil fuels in the near future. Unfortunately, some geomechanical issues may arise during its development, threatening its efficient development and the marine ecology. It is regrettable that research in this area remains inadequate. In the present work, a coupled mathematical model was used to analyze sediment stability during the prolonged extraction of natural gas from hydrate-bearing sediments. Moreover, the applicability of this model was verified by comparison. Based on this, the factors influencing sediment stability were then explored, and the corresponding mechanisms were thoroughly discussed. The comparison results showed that the results obtained by the mathematical model used were more accurate, as it included more physical fields and factors. Therefore, it was more suitable for numerical simulation of sediment stability during the long-term development of gas hydrates. Moreover, it was demonstrated that the strength weakening caused by hydrate dissociation and the stress change due to depressurization were two main mechanisms for sediment deformation or instability. Although gas production increased with increasing depressurization amplitude, permeability and hydrate saturation, as well as shallower reservoir depth, the sediment stability deteriorated accordingly. Interestingly, both sediment stability and gas production were unaffected by the heating amplitude during the prolonged development operation. This study offers a fresh perspective on mitigating the risk of sediment instability while ensuring the efficient development of marine hydrates.

The influence of diagenesis on the heterogeneity of pore structures in tight sandstone reservoirs is essential for accurately assessing hydrocarbon potential. This study employed polarized light microscopy and X-ray diffraction to characterize lithofacies in tight sandstones from the Jurassic Badaowan Formation in the Mo-Yong region of the central Junggar Basin. Additionally, the study integrated low-temperature nitrogen gas adsorption and mercury injection capillary pressure to comprehensively analyze pore structure attributes across various lithofacies within a braided river delta setting. Finally, the incorporation of a multifractal model allowed for a quantitative assessment of pore structure heterogeneity, examining the implications of diagenetic processes on the enrichment and distribution of tight oil. The investigations revealed significant variability in pore structures among lithofacies of the Badaowan Formation in the Mo-Yong region. Medium-coarse lithic sandstone (M-CLS) and medium feldspar–lithic sandstone (MFLS) predominantly feature meso-pores and macro-pores, playing a crucial role in hydrocarbon accumulation. In contrast, fine-medium feldspar–lithic sandstone (F-MFLS) and fine-medium calcareous sandstone (F-MCS) are characterized by the predominance of micro-pores, exhibiting weak connectivity. Feldspar dissolution markedly altered the pore architecture, notably enhancing the connectivity while reducing the heterogeneity of meso-pores and macro-pores. Secondary enlargement of quartz augmented the heterogeneity and reduced the connectivity of meso-pores and macro-pores, whereas the presence of micro-fractures in quartz could decrease this heterogeneity and enhance connectivity. Conversely, an increase in clay minerals and calcite reduced the volume and connectivity of meso-pores and macro-pores, thereby augmenting the heterogeneity of the pore structure. Multifractal analysis demonstrated the profound impact of diagenetic processes on the scale-dependent heterogeneity of pore structure, providing essential insights into the adsorption and flow mechanisms of tight oil within complex pore matrices. The analyses clearly identified variations in pore volume and heterogeneity across lithofacies as pivotal in governing the distribution of tight oil. Particularly, the well-developed meso-pores and their lower heterogeneities in MFLS designate it as the most prospective reservoir lithofacies. These findings offer new perspectives and solid theoretical support for the exploration and development strategies of deep tight sandstone reservoirs in braided river delta environment.

Several coals and coal by-products around the world have been identified as important alternative sources for rare earth elements and yttrium (REY) recovery, as these are considered crucial. However, many pre-existing coal chemical data and coal samples do not contain REY data, and in many cases, it is not possible to re-determine the REY concentrations in these samples. In this investigation, 528 coal samples collected from 36 coal mines of China were used to train a self-organizing map (SOM) model and the trained model was subsequently used to predict the REY concentrations in coal. The results were compared with the results of three other existing machine leaning methods, and the SOM model exhibited the highest accuracy in predicting REY concentrations. The trained SOM model was successfully used to predict REY concentrations in coal from the Fuqiang Mine, Hunchun Coalfield, northeastern China. The results were mostly consistent with those determined by an analytical technique. This work not only allows geologists to predict large-scale analysis of REY potential in coals but also improves our understanding to predict geochemical data using machine learning methods.

The grade of iron content in ore was measured using X-ray fluorescence, and three iron ore grades (i.e., 28%, 34%, and 40%) were selected to prepare disk specimens. The Brazilian splitting test was performed, and acoustic emission and digital image correlation methods were used to capture the surface strain distribution and crack propagation behavior. The microscopic morphology of the fracture surfaces of specimens was analyzed using scanning electron microscopy, and the PFC (particle flow code) simulation was used to analyze the type of discrete fracture network in the specimens. The results showed that as the grade increased, the fracture zone shifted from the center to both sides, along with specimen tensile strength. This occurred because the iron oxide enrichment strength increases microscopically and is affected by the gradual increase in shear cracks and decrease in tensile cracks with increasing grade. Moreover, both the strain value of specimens and the speed of crack propagation increased with higher grades. Scanning electron microscopy revealed that microcracks on the fracture surface gradually change from pulse failure to transgranular failure, with the latter primarily comprising microcracks. By extending numerical simulations to 22% and 46% grades, it was found that the fracture surface became more prone to bilateral damage as the grade increased. The proportion of transgranular cracks increased from 8.9% to 33.8%. Additionally, the increase in the number of cracks accelerated microcrack propagation, leading to more severe fracture of the specimens.

In lithium mineral exploration, rapid and accurate identification of lithium-related rock lithologies is critical. Traditional manual methods are time-consuming and have limited accuracy, whereas some deep learning models, despite offering high precision, suffer from high computational complexity and low inference speeds, limiting their practical application. To address these issues, this study proposes a lightweight deep learning method based on a transfer learning-based Fourier-space mixed sample data augmentation mobile vision transformer (TL-FMix-MobileViT) to efficiently identify six types of lithium-related rock lithologies. Data from Dahongliutan (Xinjiang, China), Portugal, and Spain were used for model training. The model integrates the inverted residual blocks of MobileNetV2, reducing computational cost and accelerating inference with depth-wise separable convolutions, along with a lightweight vision transformer that extracts both local and global features while lowering complexity. Transfer learning with pretrained models reduces the training time and resource usage, while the FMix data augmentation method improves the generalization ability and accelerates convergence. Among three TL-FMix-MobileViT variants (extra-extra small, extra small, and small), the small version performed best, with strong stability and generalization ability, although all variants offer benefits for different scenarios. Compared with seven classic models, TL-FMix-MobileViT achieved the highest classification performance, with over 99% accuracy and reliable inference. Visual comparisons showed that the model effectively captured features at rock boundaries, thereby providing superior classification of mixed rock features compared with other models. This lightweight model provides an efficient and accurate method for lithium-related rock lithology identification, demonstrating its potential for lithium exploration.

Integrated workflows for mineral resource estimation from exploration to mining must be able to process typical geodata (e.g., borehole data), perform data engineering (e.g., geodomaining), and spatial modeling (e.g., block modeling). Several methods exist, however they can only handle individual subtasks, and are either semi or fully automatable. Thus, an integrated workflow has not been established, which is needed to handle bigger geodata sets, perform remote monitoring, or provide short-term operational feedback. Bigger (more voluminous, higher velocity and higher dimensional) geodata sets are both emerging and anticipated in future exploration and mining operations, necessitating a geodata science counterpart to traditional, segregated, and routinely manual geostatistical workflows for resource estimation. In this paper, we demonstrate a prototype that integrates various data processing, pointwise geodomaining, domain boundary delineation, combinatorics-based visualization, and geostatistical modeling methods to create a modern resource estimation workflow. For the purpose of geodomaining, we employed a fully semi-automated, machine learning-based workflow to perform spatially aware geodomaining. We demonstrate the effectiveness of the method using actual mining data. This workflow makes use of methods that are properly geodata science-based as opposed to merely data science-based (explicitly leverages the spatial aspects of data). The workflow achieves these benefits through the use of objective metrics and semi-automated modeling practices as part of geodata science (e.g., cross-validation), enabling high automation potential, practitioner-agnosticism, replicability, and objectivity. We also evaluate the integrated resource estimation workflow using a real dataset from the platiniferous Merensky Reef of the Bushveld Complex (South Africa) known for its high nugget effect.

This study aimed to predict the lithium resource potential in the Jiulong region of western Sichuan using a spectral residual attention convolutional neural network (SRACN) model, which integrates hyperspectral imagery from the GF-5B satellite with spectral measurement data from field rock core samples. By incorporating residual connections and a spectral attention mechanism, the SRACN model efficiently extracts critical spectral features, thereby enhancing mineral identification accuracy and predictive performance. The experimental results demonstrated that: (1) The SRACN model achieved a classification accuracy of 96.46% and an F1 score of 0.9645 for muscovite classification and mineral mapping, indicating superior performance; (2) utilizing hierarchical density-based spatial clustering of applications with noise (HDBSCAN), lithium and rare metal mineralization zones in the Jiulong region were delineated, with results closely aligned with field validation, revealing significant exploration potential in the northern Daqianggou mining area and the Baitaizi region. This study presents a novel scientific and technical approach to regional geological prospecting and demonstrates the effectiveness of integrating SRACN with density clustering analysis for evaluating regional mineral resource potential.

Groundwater is the primary source of drinking water in the world, but its contamination and reduction cause environmental problems. Traditional hydraulic and numerical models for assessing groundwater and land subsidence are time-consuming and expensive. Thus, this study used the long short-term memory (LSTM) neural network to predict groundwater level and employed linear regression analysis and the hybrid random forest linear regression to find the correlation between groundwater and land subsidence. The impact of groundwater level on groundwater quality was investigated by forecasting the fluoride in groundwater using the hybrid models of random forest and k-nearest neighbor (RF–KNN), random forest linear model (HRFLM), and gradient boosting support vector regression (GBR–SVR) for the prediction of groundwater fluoride. The LSTM model yielded an R² of 0.96 in forecasting groundwater level, and the time series results from 2018 to 2022 showed a variation in groundwater level, with a decline in 2022. The LSTM model suggested that from 2024 to 2040, the groundwater level would recover progressively. The regression analysis showed an R² of 0.99 and a p value of 0.01 for the correlation between groundwater level and land subsidence, and the HRFLM model yielded an R² of 0.94. For predicting groundwater fluoride contamination, the hybrid RF–KNN had the highest R² of 0.97 compared to HRFLM and GBR–SVR, with R² of 0.95 and 0.93, respectively. This research demonstrated that hybrid models and deep learning are advanced techniques that can be applied in Cangzhou to evaluate groundwater level and land subsidence and they can be applied in areas facing similar challenges.