Natural Resources Research最新文献

{"title":"Coal Spontaneous Combustion Early Warning Methods Based on Slope Grey Relation Analysis","authors":"Xing-wang Huo, Hai-dong Chen, Yong-liang Xu, Lan-yun Wang, Lin Li","doi":"10.1007/s11053-025-10508-8","DOIUrl":"https://doi.org/10.1007/s11053-025-10508-8","url":null,"abstract":"<p>As the depth of coal mining increases, concealed fires from residual-coal spontaneous combustion in goaf pose a significant threat to underground mining safety. Preferred index gases are used to predict temperature of coal spontaneous combustion (CSC), providing ideas for an early warning system for concealed fires. Here, a new mathematical method of slope grey relation analysis (SGRA) is established and proved to be reasonable, the index gases obtained from experiments are calculated and screened according to the relation degree, and the coal temperature is predicted according to the screened index gases concentration and prediction model. The conclusions are as follows: The coal oxidation process is divided into a slow oxidation stage and a rapid oxidation stage according to the speed of oxygen consumption and gases generation, and the rapid oxidation stage approximates an exponential growth, and the trend of gases ratio changes shows an exponential growth in localized stages. Compared with index gases screened by other types of grey relation analysis, the index gases screened by SGRA accurately reflect the coal temperature, and the magnitude of the relation degree reflects the prediction accuracy. Although the SGRA has computational errors, when the relation degree of the screened index gases is greater than 0.93 in the slow oxidation stage and greater than 0.95 in the rapid oxidation stage, the prediction results can satisfy engineering applications, and the method is considered reliable. Based on SGRA and CSC prediction model, combined with artificial neural network learning, an early warning system for CSC is proposed, which is expected to accurately forecast the temperature of CSC and guarantee the safety of mine production.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"27 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144146013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Framework for Identifying Hot Spots in Coal Research","authors":"Pengfei Li, Yuqing Wang, Na Xu","doi":"10.1007/s11053-025-10504-y","DOIUrl":"https://doi.org/10.1007/s11053-025-10504-y","url":null,"abstract":"<p>The global imperative for a low-carbon energy transition is prompting significant shifts in the coal industry, driving the need to identify and analyze emerging research hot spots in coal-related research. Traditional methods that rely on domain knowledge to identify hot spots may have limitations, such as time costs and incomplete coverage. Moreover, a comprehensive analysis of coal-related research has yet to be conducted. Therefore, in this paper, a novel framework consisting of the semantic part and the word frequency part is proposed to analyze hot spots of coal-related research. Initially, a dataset consisting of 40,120 coal-related paper information from the Scopus database was constructed. Then, the novel framework was employed to analyze coal-related research. In the semantic part, bidirectional encoder representations from transformers and <i>K</i>-means algorithms were combined to conduct the hot spot analysis, and six hot spots are obtained. In the word frequency part, the bag-of-words and the latent Dirichlet allocation algorithms were combined to conduct hot spot analysis, and six hot spots were obtained. Finally, through the framework analysis, this study found that the 12 coal-related hot spots mainly revealed four main research directions: efficient coal utilization and resource recovery, carbon dioxide capture and emission reduction, environmental impact assessment and pollution control, and coal mine safety and geological modeling.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"7 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Machine Learning Techniques for Mineral Resource Categorization in a Copper Deposit in Peru","authors":"Marco A. Cotrina-Teatino, Jairo J. Marquina-Araujo, Álvaro I. Riquelme","doi":"10.1007/s11053-025-10505-x","DOIUrl":"https://doi.org/10.1007/s11053-025-10505-x","url":null,"abstract":"<p>The primary objective of this study was to evaluate the effectiveness of three machine learning techniques in the confidence categorization of mineral resources within a copper deposit in Peru: extreme gradient boosting (XGBoost), random forest (RF), and deep neural network (DNN). To achieve this, geostatistical and geometric datasets were employed to categorize mineral resources into measured, indicated, and inferred categories. The dataset included ordinary kriging estimates, kriging variance, average distances, the number of composites, the kriging Lagrangian, and geological confidence. This dataset was used to train the models, followed by the application of smoothing techniques to the initial classification results to ensure a spatially coherent representation of the deposit. The results indicate that the RF model achieved the highest overall accuracy (94%), categorizing 1403.70 million tons (Mt) as measured resources (average grade of 0.43%), 2230.58 Mt as indicated resources (average grade of 0.33%), and 2225.08 Mt as inferred resources (average grade of 0.31%). XGBoost classified a slightly higher tonnage of measured resources (1412.35 Mt) with average accuracy of 91%, while DNN excelled in inferred resources, classifying 2254.64 Mt with accuracy of 93%. Smoothing improved the transitions between categories, reducing discontinuities and providing a more coherent representation of the deposit. The study concluded that machine learning techniques are robust and accurate tools for mineral resource categorization, particularly in geologically complex deposits.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"97 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anisotropy and Hysteresis of Coal Dynamic Deformation During Adsorption and Desorption","authors":"Fenghua An, Liang Wang, Yanning Ding, Haidong Chen, Xiaolei Zhang","doi":"10.1007/s11053-025-10500-2","DOIUrl":"https://doi.org/10.1007/s11053-025-10500-2","url":null,"abstract":"<p>Coal deformation-induced by adsorption/desorption is dynamic and anisotropic, influenced by various factors, such as pressure, temperature, and gas type. This paper investigates the dynamic deformation of coal during the adsorption–desorption process and analyzes the anisotropic and hysteretic characteristics. Results show that maximum deformation is reduced by approximately half with every 10 °C increase above 40 °C, and nearly doubles with each 1 MPa pressure increase. The swelling of CO₂ at adsorption equilibrium is twice that of CH₄, and almost 4 × that of N₂. During desorption, shrinkage and desorption gas are approximately linear. Anisotropy coefficients increase initially, then decrease with adsorption, stabilizing around 2. During desorption, anisotropy coefficients generally decrease. The anisotropy coefficient of CO₂ is higher than that of CH₄ and N₂, and all show a tendency to increase with equilibrium pressure. Cumulative hysteresis deformation decreases with the increasing temperature, even reversing at higher temperatures. CO₂ exhibits significantly higher hysteresis than CH₄ and N₂. These findings offer valuable insights for engineering applications.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"31 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143979617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated Clustering and Electrofacies Analysis for Reservoir Quality and Heterogeneity Assessment: A Case Study from a Southern Iranian Gas Field","authors":"Adeleh Jamalian, Ahmad Reza Rabbani, Morteza Asemani","doi":"10.1007/s11053-025-10499-6","DOIUrl":"https://doi.org/10.1007/s11053-025-10499-6","url":null,"abstract":"<p>The efficient characterization of heterogeneous carbonate reservoirs remains a significant challenge due to complex depositional environments and diagenetic alterations. While traditional methods like electrofacies analysis and clustering techniques offer inherent benefits, they often yield incomplete or conflicting results if used solely. This paper suggests an integrated study using petrophysical, geological, and statistical analyses to improve reservoir characterization. The proposed approach was applied to a carbonate reservoir case study of a gas field in South Iran. Well-log data and core samples were employed for detailed petrographic and petrophysical analyses. Electrofacies analysis using multi-resolution graph-based clustering (MRGC) identified five distinct electrofacies. Clustering techniques, including K-means and Gaussian mixture models (GMMs), were applied to petrophysical data to delineate similar zones. The Silhouette coefficient was used to evaluate the quality of the clusters. Results showed strong correlation between electrofacies 5 and clusters 4 (from K-means) and 5 (from GMMs), implying the best reservoir properties. This integrated approach suggested a more accurate assessment of reservoir quality attributes (e.g., porosity and water saturation) and highlighted the importance of dolomitized ooid grainstone in controlling hydrocarbon accumulation. This study provides a comprehensive framework for efficiently characterizing heterogeneous carbonate reservoirs by combining petrophysical, geological, and statistical methods. This integrated approach, validated through its successful application in similar reservoir studies, enables a more accurate assessment of reservoir quality attributes such as porosity and water saturation. By leveraging the complementary strengths of these methods, the approach ensures a comprehensive understanding of reservoir heterogeneity and its impact on hydrocarbon accumulation. Additionally, it is beneficial for improving reservoir modeling, enhancing hydrocarbon recovery, and reducing exploration risks.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"114 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143979563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of Cyclic Heat Treatment on Transport Properties of Hot Dry Rock","authors":"Peng Xiao, Dan Shen, Hong Tian, Bin Dou, Jun Zheng, Alessandro Romagnoli, Lizhong Yang","doi":"10.1007/s11053-025-10497-8","DOIUrl":"https://doi.org/10.1007/s11053-025-10497-8","url":null,"abstract":"<p>Hot dry rock undergoes cyclic temperature variation during an enhanced geothermal system (EGS) operation, resulting in variations in reservoir rock’s transport properties and subsequently influencing the heat extraction efficiency of EGS. Therefore, the subject of this study was to systematically investigate the effect of cyclic heat treatment on the transport properties of granite, commonly employed in EGS, through the analysis of P-wave velocity, density, and scanning electron microscopy images. Besides, the effect of changes in the granite transport properties on EGS operation was also comprehensively discussed. The results indicated that the cyclic heat treatment led to an increase in granite permeability and a reduction in thermal conductivity. These changes primarily occurred due to the initiation and propagation of microcracks within the granite. Notably, higher-temperature heat treatments exhibited a more pronounced impact on granite properties. Additionally, a significant shift in the granite properties was observed within 450–550 °C, serving as a threshold temperature in this study. Due to the Kaiser memory effect and the blocking effect of the pre-microcrack on the subsequent microcrack, the effect of heat treatment on the properties of granite mainly came from the first heat treatment. Finally, the relationship models between heat treatment temperature and transport properties damage factors were obtained by fitting literature data.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"14 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coal Sample Dynamics Experiment under the Combined Influence of Cyclic Dynamic Load and Gas Pressure: Phenomenon and Mechanism","authors":"Siqing Zhang, Xiaofei Liu, Zhoujie Gu, Xiaoran Wang, Xin Zhou, Ang Gao","doi":"10.1007/s11053-025-10503-z","DOIUrl":"https://doi.org/10.1007/s11053-025-10503-z","url":null,"abstract":"<p>The deterioration of coal strength caused by geological conditions of high gas in deep mines and disturbance from mining operations is one of the elements that influence the incidence of dynamic disasters like gas outbursts and rock bursts. To study how gas pressure and cyclic loads interact to determine the mechanisms and phenomena of coal dynamics, the split Hopkinson pressure bar apparatus was used to perform cyclic impact test on coal samples to investigate the mechanical behavior of gas-bearing coal samples under cyclic dynamic load and gas pressures. The findings indicated that there are three stages in the stress–strain evolution of gas-bearing coal: linear elastic stage, plastic stage, and post-peak stress attenuation. As cycle time grows, the peak stress and attenuation stress of the coal samples decrease, while the maximum and peak strains exhibit a general increasing trend. Under the impact of dynamic load, the macroscopic damage form of the coal sample is mainly a macroscopic crack, and the microscopic examination revealed that the coal samples interior crystal was primarily a trans-granular fracture. By considering dynamic load, gas pressure, and number of cycles, the test results can be more accurately verified by the mechanical damage constitutive model. Finally, based on cyclic dynamic load and gas pressure, the proposed fatigue prediction model of gas-bearing coal can better anticipate coal samples dynamic load-bearing capability.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"34 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Algerian Reservoir Petrophysics Properties by Principal Components Analysis: Case Study of Illizi Basin","authors":"Djamel Chehili, Kaddour Sadek, Badr Eddine Rahmani, Benaoumeur Aour, Mehdi Bendali, Abdelmoumen Bacetti, Brahmi Serhane","doi":"10.1007/s11053-025-10502-0","DOIUrl":"https://doi.org/10.1007/s11053-025-10502-0","url":null,"abstract":"<p>Optimizing hydrocarbon recovery in the Illizi Basin requires precise reservoir characterization. Traditional methods face challenges in efficiently handling large datasets from multiple wells. This paper employs principal components analysis (PCA) to evaluate the petrophysical properties of the reservoir intervals (IV-3, IV-1b, IV-1a) using wells P8, P4, and P6, situated in the northern, center, and south of our reservoir, respectively. PCA reduced the dimensionality of the data, while preserving original information, facilitating the analysis of the reservoir's geological and sedimentological features. The results showed that unit IV-3 has the highest average porosity (average NET porosity) and the lowest average water saturation (average PAY log sw) across all wells, indicating significant hydrocarbon production potential. In contrast, units IV-1b and IV-1a exhibited higher water saturations, suggesting less favorable conditions for hydrocarbon extraction. Strong negative correlations between petrophysical properties and water saturation in unit IV-3 highlighted its potential for hydrocarbon production. PCA correlation circles illustrated these relationships, with unit IV-3 showing predominantly hydrocarbon saturation, Unit IV-1b exhibited mixed saturation, whereas unit IV-1a was characterized by high water saturation. These findings demonstrate the effectiveness of PCA in guiding hydrocarbon resource management and exploitation strategies in the Illizi Basin; therefore, we recommend prioritizing drilling in zones with optimal reservoir properties, as identified through PCA. These zones are likely to have higher porosity, permeability, and lower water saturation, we also recommend Considering implementing suitable enhanced oil recovery techniques, such as waterflooding, polymer flooding, or gas injection, to improve recovery factors, especially in low-permeability zones. Finally, we recommend implementing a robust monitoring system to track reservoir performance and adjust production strategies as needed. This may involve real-time monitoring of pressure, temperature, and flow rates. These recommendations, can significantly enhance hydrocarbon recovery from unit IV-3, maximizing economic benefits, while minimizing environmental impact. This study demonstrates the practical application of PCA in reservoir characterization and provides valuable insights for optimizing field development and production strategies in the Illizi Basin.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"22 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Class Label Representativeness in Machine Learning-Based Mineral Prospectivity Mapping","authors":"Mohammad Parsa, Renato Cumani","doi":"10.1007/s11053-025-10468-z","DOIUrl":"https://doi.org/10.1007/s11053-025-10468-z","url":null,"abstract":"<p>Mineral prospectivity mapping (MPM) can be deemed a binary classification task, with classifiers trained and validated on labels indicating the presence or absence of the targeted mineralized zones. Using economically viable mineral deposits as positive labels could, in theory, yield prospectivity models with geometallurgical reliability, thereby aiding land management and decision-making. The inherent scarcity of economically viable deposits, however, ultimately affects MPM products. The positive class label, therefore, often requires augmentation with either mineral occurrences (i.e., mineralized sites lacking economic viability) or synthetically generated labels. This paper examines how augmented positive labels and different negative label selection procedures geospatially represent economically viable mineral deposits and affect deep learning-based MPM’s classification performance and its spatial selectivity (i.e., MPM’s capability to efficiently narrow the exploration search space). To achieve this objective, large ensembles of deep learning classifiers were trained and validated with diverse combinations of positive and negative labels. Two positive class label sets were created by augmenting mineral deposits with either synthetic labels, generated using generative adversarial networks, or mineral occurrences, paired with distinct negative label sets selected based on (1) locations distant from known mineral deposits, (2) areas geospatially dissimilar to known mineral deposits, and (3) mineralized areas unrelated to the targeted style of mineralization, resulting in six unique class configurations. This study ultimately provides insights into how different label sets affect MPM's classification performance and spatial selectivity. The results indicate that selecting negative class labels from geospatially different localities enhances classification performance and MPM's spatial selectivity compared to other negative label selection procedures.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"8 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discriminating Deposit and Mineralization Types Using Major Elements and Fluorine in Mica: A Machine Learning Approach","authors":"Ziqi Hu, Dexian Zhang, Shaowei Chen, Hao Xu, Shuishi Zeng, Junzhe Kou","doi":"10.1007/s11053-025-10498-7","DOIUrl":"https://doi.org/10.1007/s11053-025-10498-7","url":null,"abstract":"<p>Machine learning (ML) is increasingly being used in geosciences for complex classification tasks. Mica minerals are commonly found in deposits of precious metals, rare metals, and rare earth elements, including tungsten, tin, lithium, and copper, among others. These minerals can provide insights into the formation environment and age of various deposits. While ML has been applied mainly for optical recognition and compositional analysis of mica, its use for classification of deposit types and mineralization types remains underexplored. This study aimed to fill this gap by developing a stacking multi-classification model, which integrates multiple ML algorithms, and logistic regression as the meta-model. Trained with a dataset of 3479 and 4005 mica major element compositions, both models achieved 0.99 accuracy on the test set. Precision, recall, and F1-scores were all reported at 0.99, indicating excellent classification performance. Feature importance analysis revealed that elements such as F, MgO, FeO, MnO, and Al₂O₃ are crucial for classification, reflecting distinct geological conditions across different types of ore deposits. Copper and gold deposits typically form around 700 °C under high oxygen fugacity and low fluorine fugacity, while W and Sn deposits form in the temperature range of 600–700 °C with varying oxygen fugacity. Lithium and beryllium deposits form at temperatures ranging 500–650 °C, exhibiting moderate oxygen fugacity and a wide range of fluorine fugacity. This paper presents a robust model for classifying deposit types and mineralization types based on mica composition and emphasizes the strong link between ML outcomes and geological characteristics.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"25 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143893138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}