Earth Science Informatics最新文献_第9页

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":"40 1","pages":""},"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":"16 1","pages":""},"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":"8 1","pages":""},"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":"42 1","pages":""},"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":"42 1","pages":""},"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":"136 1","pages":""},"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

SpinelVA. A new perspective for the visual analysis and classification of spinel group minerals 尖晶石VA。尖晶石类矿物视觉分析和分类的新视角

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-07-03 DOI: 10.1007/s12145-024-01393-5

Antonella S. Antonini, Leandro Luque, Gabriela R. Ferracutti, Ernesto A. Bjerg, Silvia M. Castro, María Luján Ganuza

{"title":"SpinelVA. A new perspective for the visual analysis and classification of spinel group minerals","authors":"Antonella S. Antonini, Leandro Luque, Gabriela R. Ferracutti, Ernesto A. Bjerg, Silvia M. Castro, María Luján Ganuza","doi":"10.1007/s12145-024-01393-5","DOIUrl":"https://doi.org/10.1007/s12145-024-01393-5","url":null,"abstract":"Spinel group minerals, found within various rock types, exhibit distinct categorizations based on their host rocks. According to Barnes and Roeder (2001), these minerals can be classified into eight primary groups, each further subdivided into variable numbers of subgroups that can be related to a particular tectonic setting. This classification is based on the cations corresponding to the end-members of the spinel prism and is traditionally analyzed in this prismatic space or using projections of it. In this prismatic representation, several categories tend to overlap, making it impossible to determine which is the tectonic environment in that scenario. An alternative to solve this problem is to generate representations of these groups considering more attributes, making the most of the many values measured during the geochemical analysis. In this paper, we present SpinelVA, a visual exploration tool that integrates Machine Learning techniques and allows the identification of groups using the cations considered by Barnes and Roeder and some additional ones obtained from chemical analysis. SpinelVA allows us to know the tectonic environment of unknown samples by categorizing them according to the Barnes and Roeder classification. Additionally, SpinelVA integrates a collection of visual analysis techniques alongside the already used spinel prism projections and provides a set of interactions that assist geologists in the exploration process. Users can perform a complete data analysis by combining the proposed techniques and associated interactions.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"82 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519636","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

Modelling of snow and glacier melt dynamics in a mountainous river basin using integrated SWAT and machine learning approaches 利用 SWAT 和机器学习综合方法建立山区河流流域积雪和冰川融化动态模型

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-07-03 DOI: 10.1007/s12145-024-01397-1

Abhilash Gogineni, Madhusudana Rao Chintalacheruvu, Ravindra Vitthal Kale

{"title":"Modelling of snow and glacier melt dynamics in a mountainous river basin using integrated SWAT and machine learning approaches","authors":"Abhilash Gogineni, Madhusudana Rao Chintalacheruvu, Ravindra Vitthal Kale","doi":"10.1007/s12145-024-01397-1","DOIUrl":"https://doi.org/10.1007/s12145-024-01397-1","url":null,"abstract":"Modelling streamflow in snow-covered mountainous regions with complex hydrology and topography poses a significant challenge, particularly given the pronounced influence of temperature lapse rate (TLAPS) and precipitation lapse rate (PLAPS). The Present study area covers 54,990 km2 in the western Himalayas, including the Tibetan Plateau and the Indian portion of the USRB up to Bhakra Dam in Himachal Pradesh. In order to estimate the snowmelt and rainfall runoff contributions to the catchment, an integrated Soil and Water Assessment Tool (SWAT) model incorporates a Temperature Index with an Elevation Band approach. The uncertainty analysis of the SWAT model has been conducted using the Sequential Uncertainty Fitting algorithm (SUFI-2). Furthermore, machine-learning models such as Long Short-Term Memory (LSTM) neural networks and Random Forest (RF) are integrated with the SWAT model to enhance the accuracy of streamflow predictions resulting from snowmelt. The performance indices of a model for the monthly calibration period are R2 = 0.83, NSE = 0.82, P-BIAS = 2.3, P-factor = 0.82, and R-factor = 0.81. The corresponding values for the validation period are R^2 = 0.78, NSE = 0.77, P-BIAS = 5.7, P-factor = 0.72 and R-factor = 0.66. The results show that 63.08% of the Bhakra gauging station’s annual streamflow has attributed to snow and glacier melt. The highest snow and glacier melt occur from May to August, while the minimum is observed from November to February. Regarding snowmelt forecasting, the LSTM model outperforms the RF model with an R2 value of 0.86 and 0.85 during training and testing, respectively. Additionally, sensitivity analysis highlights that soil and groundwater flow parameters, specifically SOL_K, SOL_AWC, and GWQMN, are the most sensitive parameters for streamflow modelling. The study confirms the effectiveness of SWAT for water resource planning and management in the mountainous USRB.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"124 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519635","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

Implementing a new Research Data Alliance recommendation, the I-ADOPT framework, for the naming of environmental variables of continental surfaces 实施新的研究数据联盟建议--I-ADOPT 框架，为大陆表面环境变量命名

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-07-02 DOI: 10.1007/s12145-024-01373-9

Coussot Charly, Braud Isabelle, Chaffard Véronique, Boudevillain Brice, Sylvie Galle

{"title":"Implementing a new Research Data Alliance recommendation, the I-ADOPT framework, for the naming of environmental variables of continental surfaces","authors":"Coussot Charly, Braud Isabelle, Chaffard Véronique, Boudevillain Brice, Sylvie Galle","doi":"10.1007/s12145-024-01373-9","DOIUrl":"https://doi.org/10.1007/s12145-024-01373-9","url":null,"abstract":"To improve data usage in an interdisciplinary context, a clear understanding of the variables being measured is required for both humans and machines. In this paper, the I-ADOPT framework, which decomposes variable names into atomic elements, was tested within the context of continental surfaces and critical zone science, characterized by a large number and variety of observed environmental variables. We showed that the I-ADOPT framework can be used effectively to describe environmental variables with precision and that it was flexible enough to be used in the critical zone science context. Variable names can be documented in detail while allowing alignment with other ontologies or thesauri. We have identified difficulties in modeling complex variables, such as those monitoring fluxes between different environmental compartments and for variables monitoring ratios of physical quantities. We also showed that, for some variables, different decompositions were possible, which could make alignments with other ontologies and thesauri more difficult. The precision of variable names proved inadequate for data discovery services and a non-standard label (SimplifiedLabel) had to be defined for this purpose. In the context of open science and interdisciplinary research, the I-ADOPT framework has the potential to improve the interoperability of information systems and the use of data from various sources and disciplines.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"36 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141531237","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