Natural Resources Research最新文献

{"title":"Predicting the Concentrations of Rare Earth Elements and Yttrium in Coal Using Self-Organizing Map","authors":"Na Xu, Fei Li, Wei Zhu, Mark A. Engle, Jiapei Kong, Pengfei Li, Qingfeng Wang, Lishan Shen, Robert B. Finkelman, Shifeng Dai","doi":"10.1007/s11053-025-10477-y","DOIUrl":"https://doi.org/10.1007/s11053-025-10477-y","url":null,"abstract":"<p>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.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"17 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607790","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":"Influence of Grade on the Splitting Mechanical Properties of Iron Ore: Insights from Microstructure Analysis","authors":"You Ju, Aibing Jin, Yiqing Zhao, Shuaijun Chen, Shaokang Tang","doi":"10.1007/s11053-025-10469-y","DOIUrl":"https://doi.org/10.1007/s11053-025-10469-y","url":null,"abstract":"<p>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.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"31 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599247","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":"Lithology Identification of Lithium Minerals Based on TL-FMix-MobileViT Model","authors":"Jianpeng Jing, Nannan Zhang, Hao Zhang, Shibin Liao, Li Chen, Jinyu Chang, Jintao Tao, Siyuan Li","doi":"10.1007/s11053-025-10475-0","DOIUrl":"https://doi.org/10.1007/s11053-025-10475-0","url":null,"abstract":"<p>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.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"158 9 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599240","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":"An Integrated Geodata Science Workflow for Resource Estimation: A Case Study from the Merensky Reef, Bushveld Complex","authors":"Glen T. Nwaila, Derek H. Rose, Hartwig E. Frimmel, Yousef Ghorbani","doi":"10.1007/s11053-025-10471-4","DOIUrl":"https://doi.org/10.1007/s11053-025-10471-4","url":null,"abstract":"<p>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.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"38 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583016","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":"Prediction of Lithium Mineralization Potential in the Jiulong Area, Western Sichuan (China), Using Spectral Residual Attention Convolutional Neural Network","authors":"Haiyang Luo, Na Guo, Chunhao Li, Hang Jiang","doi":"10.1007/s11053-025-10473-2","DOIUrl":"https://doi.org/10.1007/s11053-025-10473-2","url":null,"abstract":"<p>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.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"12 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575255","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":"Prediction of Groundwater Level and its Correlation with Land Subsidence and Groundwater Quality in Cangzhou, North China Plain, Using Time-Series Long Short-Term Memory Neural Network and Hybrid Models","authors":"Mouigni Baraka Nafouanti, Junxia Li, Hamada Chakira, Edwin E. Nyakilla, Denice Cleophace Fabiani, Jane Ferah Gondwe, Ismaila Sallah","doi":"10.1007/s11053-025-10474-1","DOIUrl":"https://doi.org/10.1007/s11053-025-10474-1","url":null,"abstract":"<p>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 <i>R</i>² 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 <i>R</i>² of 0.99 and a <i>p</i> value of 0.01 for the correlation between groundwater level and land subsidence, and the HRFLM model yielded an <i>R</i>² of 0.94. For predicting groundwater fluoride contamination, the hybrid RF–KNN had the highest <i>R</i>² of 0.97 compared to HRFLM and GBR–SVR, with <i>R</i>² 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.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"12 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143569781","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":"Tracing Deep-Seated Saturated Fractures in Depleted Shallow Aquifer Systems in a Granitic Terrain: An Integrated Hydro-geophysical Approach","authors":"Sahebrao Sonkamble, Erugu Nagaiah, Enatula Appalanaidu, Joy Choudhury, Virendra M. Tiwari","doi":"10.1007/s11053-025-10456-3","DOIUrl":"https://doi.org/10.1007/s11053-025-10456-3","url":null,"abstract":"<p>Groundwater is a vital renewable natural resource that largely supports the agriculture sector, especially in semi-arid climate of hard rock. However, the over-exploitation and inadequate recharge of groundwater in crystalline granitic terrains have depleted the shallow aquifer systems constraining the groundwater to be sporadically distributed in deep fractures. Therefore, tracing bedrock fractures becomes important, but the overlying thick pile of unsaturated saprolite layer presents a challenge to map them due to geophysical ambiguity. Currently, most studies have been done at laboratory scale, while bedrock fractures at natural field conditions are rarely attended as evidenced by numerous failures of borehole drillings in semi-arid hard rock terrain. To trace saturated bedrock fractures at natural field sites, we performed a multi-disciplinary experiment comprising hydro-geological insights, social information, remote sensing, gradient resistivity profile (GRP), vertical electrical sounding (VES) and electrical resistivity tomography (ERT) followed by exploratory borehole drillings, and hydro-chemical source speciation in a semi-arid, crystalline granitic terrain in southern India. The results showed (1) GRP as a precursor records the signatures of saturated bedrock fractures qualitatively, (2) least square inversion models of ERT demarcate distinct litho-units and saturated bedrock fractures, (3) exploratory borehole drilling shows saturated bedrock fractures at 49–54 m and 63–67 m depth designated with high yield (<i>Q</i> = 3382 lph), which compare well with electrical imaging results, and (4) hydro-chemical source speciation with dominated alkali-feldspar (albite) weathering confirmed groundwater from bedrock fractures, which supplemented the geophysical anomalies. These observations led to a practical step-by-step field guide for tracing deep-seated bedrock fractures in geologically similar semi-arid regions.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"84 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143532578","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":"Enhancing Mining Exploration through Geostatistical Analysis of Seismic Tomographies at Different Scales: Improving Low-Resolution Data by High-Resolution Results","authors":"José Joaquín González, Nadia Mery, Felipe Navarro, Gonzalo Díaz, Diana Comte, Sergio Pichott","doi":"10.1007/s11053-025-10472-3","DOIUrl":"https://doi.org/10.1007/s11053-025-10472-3","url":null,"abstract":"<p>In the context of mining exploration, local earthquake tomography serves as a valuable complementary tool, applicable across varying scales from greenfield to brownfield projects. Nevertheless, interpreting body-wave velocity anomalies within tomographies poses a significant challenge, which largely depends on the expertise of the analyst and the availability of information. Addressing this challenge, this paper proposes a geostatistical analysis to effectively compare and enhance the information extracted from tomographies ranging from lower to higher resolutions. The data utilized in this study correspond to the tomographic inversion values of Mantos Rojos (MR) and Radomiro Tomic (RT) porphyry copper deposits situated within the Chuquicamata District in northern Chile. MR has a resolution of 2 × 2 km², comparatively lower than RT’s resolution of 1 × 1 km², yet both share the same spatial zone. This study evaluated the discernment capabilities of lower-resolution tomography (MR) in comparison to its higher-resolution counterpart (RT) using turning bands simulation. The simulated Vp/Vs values of MR were compared against RT seismic tomography data. Visual validation revealed that simulated Vp/Vs values from P- and S-wave velocity values of MR can identify the low Vp/Vs anomalies (&lt; 1.7). Moreover, spatial analysis compared the experimental variograms for MR realizations and for RT values in preferential directions for Vp/Vs ratios, finding a correspondence between both spatial tools. Finally, geological validation was carried out by comparing the simulation results with geological maps of the study area and copper grades obtained through drilling campaigns provided by CODELCO, where spatial patterns indicative of mineralization and larger-scale geological features like the West Fault were identified. Our research has practical implications because, through geostatistical simulations, the grid dimensions of seismic tomography of MR can be reduced and still identify low Vp/Vs anomalies within the area of study, being consistent with the lower-resolution validation grid of RT. Our findings demonstrate the efficacy of geostatistical methods in enhancing exploration decision-making by providing insights into subsurface geological features and their relationship to mineralization. This approach not only improves the efficiency and success rate of mineral exploration projects but also minimizes environmental impact by allowing for more targeted and informed exploration activities.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"33 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518820","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":"Low-Temperature Oxidation Characteristics and Spontaneous Combustion Limit Parameters of Residual Coal in Deep Mine Goafs","authors":"Yun-chuan Bu, Hui-yong Niu, Hai-yan Wang, Yan-Xiao Yang, Lu-lu Sun","doi":"10.1007/s11053-024-10436-z","DOIUrl":"https://doi.org/10.1007/s11053-024-10436-z","url":null,"abstract":"<p>As mining depth increases, the temperature of coal seams increases gradually, which increases the risk of spontaneous coal combustion in goaf areas. This paper uses a programmed temperature rise experiment to analyze the oxidation combustion characteristics of high ground temperature coal and calculates the oxidation kinetic parameters and limit characteristic parameters. The results show that, above 100 °C, the ratios C₃H₈/C₂H₆ and C₂H₄/C₂H₆ change with the same trend. The CO and CO₂ release rates, oxygen consumption rate, heating rate and heat release intensity are positively correlated with the ground temperature. There is a good exponential relationship between the release rate of CO and CO₂ and the coal temperature. The activation energy increases and then decreases as the oxidation temperature increases (above 100 °C). In the later stage of low-temperature oxidation, the higher the ground temperature is, the lower the activation energy, the more active the organic active groups and the more violent the oxidation reaction. As the oxidation temperature increases, the lower-limit oxygen concentration (<i>C</i>_min) and the minimum floating coal thickness (<i>h</i>_min) increase and then decrease, and the high ground temperature reduces the temperature at which the maximum is reached. With increasing ground temperature, <i>h</i>_min and <i>C</i>_min increase, and the maximum air leakage intensity decreases. The research results provide a theoretical basis for the prevention and control of spontaneous coal combustion in high ground temperature coal seam mining in deep mines.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473560","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":"Multiscale Pore–Fracture Structure Characteristics of Deep Coal Reservoirs in the Eastern Margin of the Ordos Basin, China","authors":"Guangbiao Tao, Zhenzhi Wang, Yi Jin, Haichao Wang, Daping Xia, Jienan Pan","doi":"10.1007/s11053-025-10463-4","DOIUrl":"https://doi.org/10.1007/s11053-025-10463-4","url":null,"abstract":"<p>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 (<i>SSA</i>) of the coal specimens with respect to the distribution of pore diameters. The micropore volume and <i>SSA</i> 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.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"49 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427308","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}