Earth Science Informatics最新文献_第9页

Assessing the shear strength of sandy soil reinforced with polyethylene-terephthalate: an AI-based approach 评估聚对苯二甲酸乙二醇酯加固砂土的抗剪强度：一种基于人工智能的方法

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-07-10 DOI: 10.1007/s12145-024-01398-0

Masoud Samaei, Morteza Alinejad Omran, Mohsen Keramati, Reza Naderi, Roohollah Shirani Faradonbeh

{"title":"Assessing the shear strength of sandy soil reinforced with polyethylene-terephthalate: an AI-based approach","authors":"Masoud Samaei, Morteza Alinejad Omran, Mohsen Keramati, Reza Naderi, Roohollah Shirani Faradonbeh","doi":"10.1007/s12145-024-01398-0","DOIUrl":"https://doi.org/10.1007/s12145-024-01398-0","url":null,"abstract":"This research aimed to investigate the effectiveness of Polyethylene-Terephthalate (PET) as a reinforcement material for sandy soils in enhancing the shear strength. To achieve this, different concentrations of PET were tested, and 118 sets of data were collected. Parameters such as relative density, normal stress in direct shear strength test, and types of PET elements (1 × 1, 1 × 5, and fiber) were also recorded. Subsequently, four decision tree-oriented machine learning (ML) methods—decision tree (DT), random forest (RF), AdaBoost, and XGBoost—were applied to construct models capable of forecasting enhancements in shear strength. The evaluation of these models' effectiveness was conducted using four established statistical metrics: R2, RMSE, VAF, and A-10. The results showed that AdaBoost results in the highest prediction accuracy among other algorithms, representing the high modelling performance of the algorithm in dealing with complex nonlinear problems. The conducted sensitivity analysis also revealed that relative density is the most crucial parameter for all the algorithms in predicting the output, followed by PET percentage and normal stress. Furthermore, to make the developed model in this study more practical and easy to use, a Graphical User Interface (GUI) was created, enabling the engineers and researchers to perform the analysis straightforwardly.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141574459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative analysis of SPI, SPEI, and RDI ındices for assessing spatio-temporal variation of drought in Türkiye 用于评估土耳其干旱时空变化的 SPI、SPEI 和 RDI 指数的比较分析

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-07-08 DOI: 10.1007/s12145-024-01401-8

Fatma Yaman Öz, Emre Özelkan, Hasan Tatlı

{"title":"Comparative analysis of SPI, SPEI, and RDI ındices for assessing spatio-temporal variation of drought in Türkiye","authors":"Fatma Yaman Öz, Emre Özelkan, Hasan Tatlı","doi":"10.1007/s12145-024-01401-8","DOIUrl":"https://doi.org/10.1007/s12145-024-01401-8","url":null,"abstract":"This research presents a comprehensive drought analysis using climate data obtained from 219 homogeneously distributed meteorological stations in Türkiye between 1991 and 2022. In this context, Standard Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Reconnaissance Drought Index (RDI) drought indices were used and comparative analysis was made. Türkiye. The study demonstrates that below-normal precipitation over extended periods and increasing temperatures have contributed to the increased frequency of meteorological drought events. Türkiye's topographic conditions, particularly its location in the Mediterranean basin, significantly influence drought occurrences. It is noted that over the past 20 years, Türkiye has been trending towards drier conditions, with rising temperatures reinforcing this trend. The study observes that the moderate drought class range is the most frequently recurring in the SPI, SPEI, and RDI methods utilized. Regarding atmospheric conditions affecting the climate in Türkiye, it is observed that increased drought severity stands out prominently in years when the North Atlantic Oscillation is positive. During these years, increased drought severity is evident in the SPI, SPEI, and RDI indices, particularly in winter and autumn, while a wide area experiences drought effects in the summer months. Long-term analyses emphasize that drought periods occur less frequently but have more prolonged impacts, attributed to variations in precipitation patterns from year to year and the influence of rising temperatures due to global climate change. The potential future increase in drought in the Mediterranean basin due to global climate change and Türkiye's vulnerability to this situation could have adverse effects on water resources, food security, energy sources, and ecosystems.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141574454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating the impact of eccentric loading on strip footing above horseshoe tunnels in rock mass using adaptive finite element limit analysis and machine learning 利用自适应有限元极限分析和机器学习评估偏心荷载对岩体中马蹄形隧道上方条形路基的影响

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-07-08 DOI: 10.1007/s12145-024-01380-w

Aayush Kumar, Vinay Bhushan Chauhan

{"title":"Evaluating the impact of eccentric loading on strip footing above horseshoe tunnels in rock mass using adaptive finite element limit analysis and machine learning","authors":"Aayush Kumar, Vinay Bhushan Chauhan","doi":"10.1007/s12145-024-01380-w","DOIUrl":"https://doi.org/10.1007/s12145-024-01380-w","url":null,"abstract":"The present study investigates the ultimate bearing capacity (UBC) of a footing subjected to an eccentric load situated above an unlined horseshoe-shaped tunnel in the rock mass, following the Generalized Hoek-Brown (GHB) failure criterion. A reduction factor (Rf) is introduced to investigate the impact of the tunnel on the UBC of the footing. Rf is determined using upper and lower bound analyses with adaptive finite-element limit analysis. The study examines the influence of several independent variables, including normalized load eccentricity (e/B), normalized vertical and horizontal distances (δ/B and H/B) of the footing from the tunnel, tunnel size (W/B), and other rock mass parameters. It was found that all these parameters significantly affect the behavior of tunnel-footing interaction depending on the range of varying parameters. The findings of the study indicate that the critical depth (when Rf is nearly 1) of the tunnel decreases with increasing load eccentricity. The critical depth is found to be δ/B ≥ 2 for e/B ≤ 0.2 and δ/B ≥ 1.5 for e/B ≥ 0.3, regardless of H/B ratios. Additionally, the GHB parameters of the rock mass significantly influence the interaction between the tunnel and the footing. Moreover, this study identifies some typical potential failure modes depending on the tunnel position. The typical potential failure modes of the footing include punching failure, cylindrical shear wedge failure, and Prandtl-type failure. This study also incorporates soft computing techniques and formulates empirical equations to predict Rf using artificial neural networks (ANNs) and multiple linear regression (MLR).","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141574455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

One-dimensional deep learning driven geospatial analysis for flash flood susceptibility mapping: a case study in North Central Vietnam 一维深度学习驱动的地理空间分析用于绘制山洪灾害易感性地图：越南中北部案例研究

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-07-06 DOI: 10.1007/s12145-024-01285-8

Pham Viet Hoa, Nguyen An Binh, Pham Viet Hong, Nguyen Ngoc An, Giang Thi Phuong Thao, Nguyen Cao Hanh, Phuong Thao Thi Ngo, Dieu Tien Bui

{"title":"One-dimensional deep learning driven geospatial analysis for flash flood susceptibility mapping: a case study in North Central Vietnam","authors":"Pham Viet Hoa, Nguyen An Binh, Pham Viet Hong, Nguyen Ngoc An, Giang Thi Phuong Thao, Nguyen Cao Hanh, Phuong Thao Thi Ngo, Dieu Tien Bui","doi":"10.1007/s12145-024-01285-8","DOIUrl":"https://doi.org/10.1007/s12145-024-01285-8","url":null,"abstract":"Flash floods rank among the most catastrophic natural disasters worldwide, inflicting severe socio-economic, environmental, and human impacts. Consequently, accurately identifying areas at potential risk is of paramount importance. This study investigates the efficacy of Deep 1D-Convolutional Neural Networks (Deep 1D-CNN) in spatially predicting flash floods, with a specific focus on the frequent tropical cyclone-induced flash floods in Thanh Hoa province, North Central Vietnam. The Deep 1D-CNN was structured with four convolutional layers, two pooling layers, one flattened layer, and two fully connected layers, employing the ADAM algorithm for optimization and Mean Squared Error (MSE) for loss calculation. A geodatabase containing 2540 flash flood locations and 12 influencing factors was compiled using multi-source geospatial data. The database was used to train and check the model. The results indicate that the Deep 1D-CNN model achieved high predictive accuracy (90.2%), along with a Kappa value of 0.804 and an AUC (Area Under the Curve) of 0.969, surpassing the benchmark models such as SVM (Support Vector Machine) and LR (Logistic Regression). The study concludes that the Deep 1D-CNN model is a highly effective tool for modeling flash floods.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141574456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From land to ocean: bathymetric terrain reconstruction via conditional generative adversarial network 从陆地到海洋：通过条件生成对抗网络重建测深地形

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-07-05 DOI: 10.1007/s12145-024-01381-9

Liwen Zhang, Jiabao Wen, Ziqiang Huo, Zhengjian Li, Meng Xi, Jiachen Yang

{"title":"From land to ocean: bathymetric terrain reconstruction via conditional generative adversarial network","authors":"Liwen Zhang, Jiabao Wen, Ziqiang Huo, Zhengjian Li, Meng Xi, Jiachen Yang","doi":"10.1007/s12145-024-01381-9","DOIUrl":"https://doi.org/10.1007/s12145-024-01381-9","url":null,"abstract":"Acquiring global ocean digital elevation model (DEM) is a forefront branch of marine geology and hydrographic survey that plays a crucial role in the study of the Earth’s system and seafloor’s structure. Due to limitations in technological capabilities and surveying costs, large-scale sampling of ocean depths is very coarse, making it challenging to directly create complete ocean DEM. Many traditional interpolation and deep learning methods have been applied to reconstruct ocean DEM images. However, the continuity and heterogeneity of ocean terrain data are too complex to be approximated effectively by traditional interpolation models. Meanwhile, due to the scarcity of available data, training an sufficient network directly with deep learning methods is difficult. In this work, we propose a conditional generative adversarial network (CGAN) based on transfer learning, which applies knowledge learned from land terrain to ocean terrain. We pre-train the model using land DEM data and fine-tune it using ocean DEM data. Specifically, we utilize randomly sampled ocean terrain data as network input, employ CGAN with U-Net architecture and residual blocks to capture terrain features of images through adversarial training, resulting in reconstructed bathymetric terrain images. The training process is constrained by the combined loss composed of adversarial loss, reconstruction loss, and perceptual loss. Experimental results demonstrate that our approach reduces the required amount of training data, and achieves better reconstruction accuracy compared to traditional methods.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Harris Hawks optimization-based cellular automata model for urban growth simulation 基于哈里斯-霍克斯优化的城市增长模拟蜂窝自动机模型

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-07-05 DOI: 10.1007/s12145-024-01399-z

Yuan Ding, Hengyi Zheng, Fuming Jin, Dongming Chen, Xinyu Huang

引用次数: 0

Real-time sharing algorithm of earthquake early warning data of hydropower station based on deep learning 基于深度学习的水电站地震预警数据实时共享算法

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-07-05 DOI: 10.1007/s12145-024-01400-9

Gang Yang, Min Zeng, Xiaohong Lin, Songbai Li, Haoxiang Yang, Lingyan Shen

{"title":"Real-time sharing algorithm of earthquake early warning data of hydropower station based on deep learning","authors":"Gang Yang, Min Zeng, Xiaohong Lin, Songbai Li, Haoxiang Yang, Lingyan Shen","doi":"10.1007/s12145-024-01400-9","DOIUrl":"https://doi.org/10.1007/s12145-024-01400-9","url":null,"abstract":"Different geographical locations have different time series and types of earthquake early warning data of hydropower stations, and the packet loss rate in data sharing is high. In this regard, a real-time sharing algorithm of earthquake early warning data of hydropower stations based on deep learning is proposed. The compressed sensing method is used to collect the seismic data of the hydropower station, and the dictionary learning algorithm based on ordered parallel atomic updating is introduced to improve the compressed sensing process and to sparse the seismic data of the hydropower station. Combining FCOS and DNN, the seismic velocity spectrum is picked up from the collected seismic data and used as the input of the convolutional neural network. The real-time sharing of earthquake early warning data is realized using the CDMA1x network and TCP data transmission protocol. Experiments show that the algorithm can accurately pick up the regional seismic velocity spectrum of hydropower stations, the packet loss rate of earthquake early warning data transmission is low, and the sharing results contain a variety of information, which can provide a variety of data for people who need information and has strong practicability.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review on deep learning-based automated lunar crater detection 基于深度学习的月球环形山自动探测综述

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-07-05 DOI: 10.1007/s12145-024-01396-2

Chinmayee Chaini, Vijay Kumar Jha

{"title":"A review on deep learning-based automated lunar crater detection","authors":"Chinmayee Chaini, Vijay Kumar Jha","doi":"10.1007/s12145-024-01396-2","DOIUrl":"https://doi.org/10.1007/s12145-024-01396-2","url":null,"abstract":"The lunar surface, which has been extensively explored and studied, offers valuable insights into its geological history and crater distribution due to the abundance of impact craters on its surface. Detecting numerous craters of different sizes on the lunar surface necessitated an automated process to avoid manual intervention, which consumed significant time and effort. However, traditional methods rely on manual feature extraction methods, encountering similar challenges, including low performance, particularly when confronted with diverse crater sizes and illumination conditions. In recent years, intelligent algorithms that introduce automated crater detection algorithms (CDAs) using deep learning (DL) techniques have played a vital role in detecting various sizes of craters on the lunar surface that may be missed or miss-classification by visual interpretation. This study outlines the challenges faced by traditional methods and explores recent advancements in DL techniques. The main objective is to provide a comprehensive review of prior studies, highlighting the advantages and limitations of each DL-based technique for automatic crater detection. Additionally, this study aggregates existing research on various image-processing tasks (such as semantic segmentation, classification-based, and object detection) utilizing DL-based techniques for detecting various sizes of craters on the lunar surface. Further, this study provides a comprehensive analysis of both manually and automatically compiled crater databases to assist new researchers in validating their models both qualitatively and quantitatively. By reviewing existing literature, this study aids new researchers in understanding the limitations and key findings of recent research, thereby promoting progress toward greater automation in crater detection.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of segment anything model (SAM) for automated labelling in machine learning classification of UAV geospatial data 评估用于无人机地理空间数据机器学习分类中自动标注的分段任何模型 (SAM)

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-07-05 DOI: 10.1007/s12145-024-01402-7

Bhargav Parulekar, Nischal Singh, Anandakumar M. Ramiya

{"title":"Evaluation of segment anything model (SAM) for automated labelling in machine learning classification of UAV geospatial data","authors":"Bhargav Parulekar, Nischal Singh, Anandakumar M. Ramiya","doi":"10.1007/s12145-024-01402-7","DOIUrl":"https://doi.org/10.1007/s12145-024-01402-7","url":null,"abstract":"With the present trend toward digitization in many areas of urban planning and development, accurate object classification is becoming increasingly vital. To develop machine learning models that can effectively classify the broader region, it is crucial to have accurately labelled datasets for object extraction. However, the process of generating sufficient labelled data for machine learning models remains challenging. A recently developed AI-assisted segmentation approach called the Segment Anything Model (SAM) offers a solution to enhance the labelling of complex and intricate image structures. By utilizing SAM, the accuracy and consistency of annotation results can be improved, while also significantly reducing the time required for annotation. This paper aims to assess the efficiency of SAM annotated labels for training machine learning models using high-resolution remote sensing data captured by UAVs (Unmanned Aerial Vehicles) in the peri-urban region of Anad, Kerala, India. A comparative analysis was conducted to evaluate the performance of training datasets generated using SAM and manual labelling with existing tools. Multiple machine learning models, including Random Forest, Support Vector Machine, and XGBoost, were employed for this analysis. The findings demonstrate that employing the XGBoost algorithm in combination with SAM annotated labels yielded an accuracy of 78%. In contrast, the same algorithm trained with the manually labeled dataset achieved an accuracy of only 68%. A similar pattern was observed when employing the Random Forest algorithm, with accuracies of 78% and 60% while using SAM annotated labels and manual labels, respectively. These outcomes unequivocally showcase the enhanced effectiveness and dependability of the SAM-based segmentation method in producing accurate results.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of static Young’s modulus of sandstone types: effective machine learning and statistical models 估算砂岩类型的静态杨氏模量：有效的机器学习和统计模型

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-07-04 DOI: 10.1007/s12145-024-01392-6

Na Liu, Yan Sun, Jiabao Wang, Zhe Wang, Ahmad Rastegarnia, Jafar Qajar

{"title":"Estimation of static Young’s modulus of sandstone types: effective machine learning and statistical models","authors":"Na Liu, Yan Sun, Jiabao Wang, Zhe Wang, Ahmad Rastegarnia, Jafar Qajar","doi":"10.1007/s12145-024-01392-6","DOIUrl":"https://doi.org/10.1007/s12145-024-01392-6","url":null,"abstract":"The elastic modulus is one of the important parameters for analyzing the stability of engineering projects, especially dam sites. In the current study, the effect of physical properties, quartz, fragment, and feldspar percentages, and dynamic Young’s modulus (DYM) on the static Young’s modulus (SYM) of the various types of sandstones was assessed. These investigations were conducted through simple and multivariate regression, support vector regression, adaptive neuro-fuzzy inference system, and backpropagation multilayer perceptron. The XRD and thin section results showed that the studied samples were classified as arenite, litharenite, and feldspathic litharenite. The low resistance of the arenite type is mainly due to the presence of sulfate cement, clay minerals, high porosity, and carbonate fragments in this type. Examining the fracture patterns of these sandstones in different resistance ranges showed that at low values of resistance, the fracture pattern is mainly of simple shear type, which changes to multiple extension types with increasing compressive strength. Among the influencing factors, the percentage of quartz has the greatest effect on SYM. A comparison of the methods' performance based on CPM and error values in estimating SYM revealed that SVR (R2 = 0.98, RMSE = 0.11GPa, CPM = + 1.84) outperformed other methods in terms of accuracy. The average difference between predicted SYM using intelligent methods and measured SYM value was less than 0.05% which indicates the efficiency of the used methods in estimating SYM.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0