Natural Resources Research最新文献

The pore–fracture structure of deep coal deposits is highly important for the potential evaluation, investigation, and utilization of deep coalbed methane resources. This study used methods such as low-pressure CO₂ adsorption, low-temperature N₂ adsorption, high-pressure mercury intrusion porosimetry, scanning electron microscopy, and optical microscopy to describe the pore–fracture structure of deep coal reservoirs at multiple scales and to discuss the development features, complexity, and influence on permeability of the pore–fracture structure of coal reservoirs. The results showed that there were significant differences in the pore volume and specific surface area (SSA) of the coal specimens with respect to the distribution of pore diameters. The micropore volume and SSA accounted for the largest proportions (85.93% and 98.63%, respectively). The more moisture and fixed carbon content there were in coal, the larger the micropore volume was. The higher the yields of ash and volatile matter were, the smaller the micropore volume was. The larger the pore radius in coal was, the greater the fractal dimension was. Besides, within their respective pore size sections, as the fractal dimension increased, the pore volume gradually decreased. As the vitrinite content increased, the fracture aperture and surface density gradually increased. As the fracture aperture increased, the fracture fractal dimension decreased, while the fracture tortuosity increased. Compared with shallow coal seams, the fracture aperture of deep coal seams showed a decreasing trend, while the pore volume showed an increasing trend.

Witwatersrand-type gold deposits in South Africa are generally amenable to cyanidation due to their free-milling nature. However, the relatively easy-to-process gold ores have been mostly depleted, and the remaining ores are of low-grade combined with semi-refractory properties. Here, we use an integrated approach to understand the mineralogical and textural characteristics of the Witwatersrand-type gold ores and to explore the effectiveness of glycine-leaching gold recovery. Analysis of sulfide minerals using 3D micro-X-ray computed tomography data shows these minerals can be used as predictive indicators for feed gold grade as they either co-exist and/or encapsulate gold. Primary experimental results demonstrate that alkaline glycine can recover > 80% Au in 100 hours at ambient temperatures. Glycine thus holds promise for gold recovery of low-grade free-milling and semi-refractory Witwatersrand-type gold ores. We also note that the presence of carbonaceous matter in ores, such as in the Black Reef orebody, adversely affects gold recovery. Ore blending may therefore be a suitable option to remediate poor gold recovery. Lastly, we demonstrate that stochastic simulations and data analytics can help augment primary experimental data to estimate uncertainty, providing a better understanding of experimental results, and thus providing future research directions.

Lithological mapping is an effective tool for geological surveys and mineral exploration. However, it faces challenges in identifying complex rock types and improving classification accuracy. We mapped lithological units in the Karamaili ophiolite-mélange belt of Xinjiang using integrated machine learning algorithms, including artificial neural network (ANN), Mahalanobis distance (MD), support vector machine (SVM), and random forest (RF). These algorithms were utilized to process remote sensing datasets acquired by the Sustainable Development Science Satellite 1 Thermal Infrared Spectrometer (SDGSAT-1 TIS), Landsat-8 Operational Land Imager (OLI), and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER-GDEM). The results indicated that the overall accuracies of ANN, MD, SVM, and RF were 68.87%, 78.98%, 93.4%, and 98.36%, respectively. The SVM and RF effectively mapped the lithological units. The SDGSAT-1 TIS data helped to identify mafic–ultramafic and feldspar-rich rocks, while Landsat-8 OLI helped to successfully delineate granitoid and complex lithologies. The ASTER-GDEM data helped improve mapping accuracy by providing detailed topographic information. Thus, this study confirmed the efficacy of the implemented approaches to delineate mineralization zones and to discriminate lithological units. This study provides detailed geological data for lithological mapping and serves as a significant reference for geological surveys and environmental monitoring.

Geophysical exploration for disseminated chromite deposits has always been challenging because the ore body does not exhibit significant geophysical anomalies. An understanding of petrophysical rock parameters can make the interpretation of geophysical data more accurate. The spectral induced polarization (SIP) method emerged as a promising technique to understand the electrical and petrophysical properties of rocks. In the present study, we tried to acquire the low frequency (0.01–1 kHz) spectral nature of chromite host rock samples, including harzburgite, dunite, and serpentinite, to understand their petrophysical properties. A double Cole–Cole (CC) model was adapted for the interpretation of SIP data. The results confirmed that the chargeability (m) and relaxation time (τ) for ferrochromite were (0.61) and (2.42 s), respectively, and for serpentinized rocks (0.40) and (1.86 s). These values were sufficient to produce anomalies with respect to background. Further, ferrochromite samples exhibited higher resistivity (~500,000 Ω m) with respect to harzburgite, dunite, and serpentinite. The serpentinized rocks showed the highest magnetic susceptibility (3.5 × 10⁻³ SI) followed by harzburgite (2.93 × 10⁻³ SI), ferrochromite (2.60 × 10⁻³ SI) and dunite (0.96 × 10⁻³ SI). The ferrochromite rocks showed the highest density (3.9 g/cm³), followed by harzburgite (3.5 g/cm³), dunite (3.02 g/cm³), and serpentinized rocks (2.7 g/cm³). Acquired results can be considered while using geophysical data to increase accuracy. This study contributes to understanding the electrical and petrophysical parameters of chromite deposits and their host rocks.

Accurate assessment of in situ fluid occurrence and content in complex shale reservoirs is crucial for effective resource evaluation and shale oil extraction. Laboratory tests on placed samples often lead to misestimations due to movable fluid loss. Low-field nuclear magnetic resonance (NMR) technology, which eliminates the need to crush cores for pyrolysis experiments, is emerging as a vital tool for studying shale pore fluids. Simultaneously, mobile full-diameter core (MFDC) NMR at wellsite is advancing rapidly, allowing for the first-time testing of cores immediately after extraction. However, research in this field remains limited. This study employed an innovative laboratory oil–water restoration technique alongside two-dimensional (2D) transverse (T₁) – longitudinal relaxation time (T₂) NMR and wellsite MFDC to evaluate the in situ fluid content in the lower first member of Cretaceous Tengger (K₁bt₁) and Aershan (K₁ba) Formations of the Wuliyasitai Depression, Erlian Basin. Our findings demonstrate that the 2D T₁–T₂ NMR technique effectively detected various hydrogen-containing components in shale oil reservoirs. Combined with quantitative analysis, it revealed the dynamic characteristics of oil–water signals during restoration, establishing a reliable method for assessing shale oil–water content. The multistage Rock-Eval (MRE) pyrolysis method strongly correlated with the 2D NMR results, confirming the reliability of NMR. Due to maturity-related variation in shale oil composition, the MRE pyrolysis results of the lower K₁bt₁ and K₁ba shales exhibited a different linear correlation with the 2D NMR data of as-received (AR) state shale, prompting adjustments to the NMR calibration coefficients for lower K₁bt₁ shale. The total oil content of the in situ fluid state shale was calculated to be 1.9582 and 3.2489 times greater than that of the AR state for the lower K₁bt₁ and K₁ba formations, respectively. The laboratory-measured oil content of the in situ state shale aligned well with MFDC NMR results, indicating that integrating laboratory oil–water restoration techniques with NMR provides a more effective and accurate representation of in situ fluid occurrence and content. Furthermore, the empirical S₁-corrected model developed for lower K₁bt₁ and K₁ba shales in the Erlian Basin holds potential for broader application in shale oil operations. Our research offers valuable insights into evaluating in situ fluids in shale oil reservoirs.

The Pokiok Plutonic Suite (PPS) lies within the southern segment of New Brunswick's Central Plutonic Belt, Canada. The PPS exhibits significant Devonian intrusive events, including four main phases, namely the Hartfield Tonalite, the Hawkshaw Granite, the Skiff Lake Granite, and the Allandale Granite, hosting notable intrusion-related W–Mo–Sb–Au deposits. This study aimed to identify potential exploration targets for intrusion-related W–Mo–Sb–Au deposits using knowledge-driven mineral prospectivity mapping (MPM) techniques. Model- and judgment-related uncertainties undermine the reliability of knowledge-driven MPM. This study adopted a multifaceted approach, combining the mineral systems approach, parsimonious weighting methods, Monte Carlo simulation (MCS), and a risk–return analysis, to mitigate the effects of these uncertainties on MPM. We employed three multi-criteria decision-making systems, namely MCS-based Best Worst Method (BWM) with Measurement Alternatives and Ranking according to the Compromise Solution (MARCOS) (MCS–BWM–MARCOS), MCS-based Full Consistency Method (FUCOM) with MARCOS (MCS–FUCOM–MARCOS), and MCS-based Level Based Weight Assessment (LBWA) with MARCOS (MCS–LBWA–MARCOS), for MPM, with MCS–LBWA–MARCOS exhibiting the highest accuracy. The risk–return analysis was employed to interpret the results of our models. Low-risk, high-return cells reduced the search space for mineral exploration by ~ 15%, while predicting ~ 73% of the known intrusion-related W–Mo–Sb–Au occurrences. The methodology applied herein allows for a more confident selection of exploration targets using knowledge-driven MPM.

Coalbed methane primarily exists as adsorbed gas within the microscopic pores and fractures of coal. However, the complex pore structure of deep coal seams and its quantitative relationship with methane adsorption capacity remain unclear. This study investigated nine samples from a coal seam in the Ningwu Basin, representing different burial depths, including five middle-shallow and four deep burials. This was accomplished through a series of experiments, including high-pressure mercury injection (HPMI), low-temperature nitrogen adsorption (LTGA–N₂), low-pressure carbon dioxide adsorption (LPGA–CO₂), and high-pressure (30 MPa) methane isothermal adsorption (HPGA–CH₄). The study revealed the characteristics of the pore structure in deep coal seams and their differences compared to those in middle-shallow coal seams. Moreover, it clarified the mechanism by which the pore structure influences CH₄ adsorption capacity. Given the differences in methane adsorption mechanisms at various pore scales, a novel method for quantitatively assessing the methane adsorption capacity using pore structure parameters is proposed. The results showed that the micropore pore volume and specific surface area of the deep coal seam were significantly higher than those of the middle-shallow coal seams. In contrast, the development of mesopores and macropores was relatively limited. The CH₄ adsorption capacity of a coal seam was calculated using pore structure parameters across multiple scales, considering the coexistence of two-dimensional “filling adsorption” and three-dimensional “monolayer adsorption” mechanisms. The calculated capacity V_L’ closely matched the measured value of V_L, with error of less than 10%. The degree of micropore development is the main factor influencing the accuracy of this method. Therefore, using pore structure parameters at different scales to calculate methane adsorption capacity is effective and feasible for deep coal seams with extensive micropore development. This study established a connection between microscopic pore structure and macroscopic methane adsorption capacity, offering a novel method to determine the methane adsorption capacity of deep coal seams.

Predicting flyrock is key to safety and efficiency in open pit mining. In this study, we developed and tested four hybrid models utilizing an adaptive neuro–fuzzy inference system (ANFIS) integrated with metaheuristic optimization techniques: Lévy-enhanced Jaya (ANFIS–LJ), bat algorithm (ANFIS–BA), firefly algorithm (ANFIS–FA) and social spider optimization (ANFIS–SSO). Remarkably, the Lévy technique was applied to enhance the JA algorithm and improve the performance of the ANFIS model for predicting flyrock distance. The models were trained and tested using a dataset from Ta Phoi copper mine with 204 blast events and flyrock distance as the target variable. A drone was used to measure flyrock distance in this study with high resolution to capture the entire flyrock phenomenon of each blast. The k-fold cross-validation technique (with 5 folds) was applied to ensure that AI-based models are not only accurate but also generalize well to new data. It helps in evaluating model performance, tuning hyperparameters, reducing overfitting, and providing a more reliable estimate of how the model will perform in predicting blast-induced flyrock. The models were evaluated using MAE (mean absolute error), RMSE (root mean-squared error) and R². The result showed that ANFIS–LJ outperformed the other models with MAE of 1.423, RMSE of 1.895 and R² of 0.981 on the testing dataset. It was also validated through 13 blasts in practice and achieved a high R² of 0.988, indicating excellent agreement between predicted and observed flyrock distances. Besides, the low MAE (1.322) and RMSE (1.825) values confirmed the model's precision and reliability in predicting flyrock distances. These results confirmed its potential as a valuable tool for optimizing blast designs, enhancing safety, and reducing environmental impacts in real-world engineering applications. This study showed that combining ANFIS with metaheuristic algorithms, especially Lévy-enhanced Jaya algorithm, can produce accurate flyrock prediction. The result can be used to improve the predictive model in open pit mining and decision making. Future study can focus on refining the models and applying them in different mining environments to improve the accuracy.

The overmature Lower Cambrian shale in southern China typically exhibits underdeveloped organic matter (OM) pores, with low porosity, and it is commonly believed that both of them have a causal linkage. However, there remains a lack of in-depth study on the characteristics of OM pores and their controlling factors for this shale. In this study, a suite of Lower Cambrian shale samples was taken from a well in the Upper Yangtze Platform, and their isolated kerogen was used to represent their OM. The shale samples were subjected to the analysis of TOC (total organic carbon) contents, mineral composition and maturity, and the kerogen samples were measured by low pressure gas absorption and X-ray photoelectron spectroscopy to characterize the OM nanopore structure and heterogeneity, and the degree of graphitization, respectively. These data were jointly used to investigate the influencing factors of OM nanopores. The results show that the shale samples were rich in quartz and clay minerals, mainly belonging to siliceous shale in lithofacies, their OM was overmature, with average equivalent vitrinite reflectance (EqVRo) values of 3.48–3.49% and graphitization degrees of 12.77–18.56%. The development of their OM nanopores varied widely, with total pore volumes of 0.321–0.786 cm³/g and total specific surface areas of 142.27–206.02 m²/g (the data were normalized by the elemental carbon content of kerogen samples). The variable graphitization degree and pore structure parameters of OM in the shale samples are primarily attributable to the differential compaction caused by their differences in TOC content and mineral composition. The shale samples with higher TOC contents tended to have lower ratios of quartz to TOC, with the disadvantage to the formation of an effective rigid framework, which increases the compaction of OM particles in shale, and enhances their graphitization degree as well as the collapse of their larger nanopores (such as mesopores and macropores) to form smaller nanopores (typically micropores). However, these processes are weakened to some extent by the pressure-shielding effect of OM-clay aggregations. In contrast, as the graphitization degree increases, the orderly arrangement of carbon atoms is enhanced, leading to the OM particles are easier to be deformed. Combined with the influence of compaction, the graphitization can promotes the transformation of OM mesopores and macropores into micropores, which also complicates the pore structure to enhance the heterogeneity. Therefore, the OM nanopore characteristics and heterogeneity of the studied overmature shale samples were directly affected by their compositions, and the primary mechanism was the synergistic effect of compaction and graphitization.

The discovery of new lithium resources is essential because lithium plays a vital role in the manufacturing of green technology. Along with brines and volcano–sedimentary deposits, approximately a one-third share of global lithium resources is associated with lithium-cesium-tantalum (LCT) pegmatites, with Canada hosting numerous examples. This research applied generative adversarial networks, natural language processing, and convolutional neural networks to generate mineral prospectivity models and support exploration targeting for Canadian LCT pegmatites. Geoscientific text data included within public bedrock geology maps and natural language processing were used to convert conceptual targeting criteria into evidence layers that complement more traditional, geophysical and geochronological data used for mineral prospectivity modeling (MPM). A multilayer architecture of convolutional neural networks, including an attention mechanism, was designed for data modeling. This architecture was trained and validated using variable synthetically generated class labels, input image sizes, and hyperparameters, resulting in an ensemble of 1000 models. The uncertainty of the ensemble was analyzed using a risk–return analysis, yielding a bivariate choropleth risk–return plot that facilitates the interpretation of prospectivity models for downstream applications. This was further complemented by employing post hoc interpretability algorithms to translate the black-box nature of neural networks into comprehensible content. The low-risk and high return class of our prospectivity models reduces the search space for discovering LCT pegmatites by 88%, delineating 99% of known LCT pegmatites in Canada. The results of this study suggest that our workflow (i.e., combining synthetic data generation, natural language processing, convolutional neural networks, and uncertainty propagation for MPM) facilitates decision-making for regional-scale lithium exploration and could also be applied to other mineral systems.