Earth Science Informatics最新文献

{"title":"A 3D bedrock modeling method based on information mining of 2D geological map","authors":"Tong Niu, Bingxian Lin, Liangchen Zhou, Guonian Lv","doi":"10.1007/s12145-024-01375-7","DOIUrl":"https://doi.org/10.1007/s12145-024-01375-7","url":null,"abstract":"<p>The 2D geological map serves as a synthesis of geological investigations and expert knowledge, making it a crucial data source for bedrock 3D modeling. Nevertheless, insufficient geological information mining has been a problem in previous research utilizing 2D geological maps for bedrock modeling. To address this issue, this paper proposes a 3D bedrock modeling method that incorporates multiple information mining based on 2D geological maps. The method involves extracting surface undulation, occurrence, stratigraphic age, and other information from the 2D geological map multiple times using map-cut cross sections and virtual drills. This information is then stored using the generalized trigonal prismatic (GTP) element. Additionally, the paper introduces connection rules to handle different geological phenomena, such as stratigraphic pinch-out, stratigraphic inversion, stratigraphic duplication, and unconformity contact, and their corresponding GTP types. Finally, the GTPs are connected according to these rules, resulting in the construction of a 3D bedrock model. To validate the method’s consistency with expert speculation regarding the expression of geological body structures and occurrence, the paper compares a slice section of the example modeling results with expert hand-drawn section from the same location. The results demonstrate that the proposed modeling method effectively explores the geological information present in the planar geological map and clearly expresses a variety of complex geological formations, enabling the construction of a high-quality 3D bedrock model.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"15 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519644","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}

{"title":"GravNetAdj: a MATLAB-based data processing software for gravity network adjustment","authors":"Jun Zhao, XinQiang Xu, LiNa Wang, YaJun Zhang","doi":"10.1007/s12145-024-01374-8","DOIUrl":"https://doi.org/10.1007/s12145-024-01374-8","url":null,"abstract":"<p>In order to realize the gravity standard using the gravity data of a specified group of stations, the gravity reference network is crucial for national gravity measurements. Dependable gravity values are required in the modern height system and for different tasks in geophysics. In particular, it can be utilized as an initial data point for airborne gravity measurements or other fields. There are some programs for processing gravity data like GrafLaB, GRAVSOFT, but its main focus are the spherical harmonic computation and gravity field modeling, and can not process the observations of relative measurements. Even though there are pyGABEUR-ITB and GRAVNET, but above mentioned software don’t have the graphical user interface (GUI) and also don’t give the detail mathematical error correction. Therefore, the GravNetAdj software package, which is based on the MATLAB 2018a platform by using the appdesigner, is developed using an up-to-date robust method for parameter estimation. Firstly, this paper reviews the fundamental theory of gravity network adjustment, covering the relative gravity data processing, the methods of network adjustment, and precision evaluation. Then, a case study for the local gravity reference network in China is conducted, which is used to prove the reliability and availability of the presented software.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"134 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519829","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}

{"title":"Assessing land degradation neutrality in semi-arid dryland agroecosystems of the matabeleland North province of Zimbabwe","authors":"Bright Chisadza, Onalenna Gwate, Simon Peter Musinguzi","doi":"10.1007/s12145-024-01384-6","DOIUrl":"https://doi.org/10.1007/s12145-024-01384-6","url":null,"abstract":"<p>Semi-arid agroecosystems are crucial for food security and ecosystem services, but land degradation threatens their sustainability. This study assessed land degradation neutrality (LDN) in a semi-arid agroecosystem of Matabeleland North Province, Zimbabwe, leveraging trends.earth within QGIS and data from the European Space Agency Climate Change Initiative (1992–2020). We analysed land use/land cover (LULC), soil organic carbon (SOC), and land productivity to identify areas of degradation and stability. Additionally, we simulated 2050 land-use/land-cover maps using a cellular automata model within a GIS framework assuming a business-as-usual scenario. The model considered the 2015 LULC map as a baseline along with environmental variables such as the digital elevation model and slope. While historical trends (1992–2020) showed a decrease in bare areas (-71%) and an increase in settlements (+ 163%), cellular automata modeling predicted a concerning future trajectory with further expansion of bare land (+ 238%) and settlements (+ 72%) by 2050, alongside a decline in water bodies (-23%) and forests (-3.5%). Notably, around 26.5% of the land exhibited degradation, often linked to low SOC levels in croplands, while 59.55% remained stable over the study period. However, caution is necessary as increases in greenness may not always reflect positive restoration. Land cover transitions, particularly the conversion of forests to grasslands and settlements, emerged as potential drivers of degradation, likely leading to ecosystem service loss, habitat fragmentation, and potentially exacerbating the impacts of climate change. These findings highlight the urgent need for targeted restoration and land management strategies focused on improving SOC levels in croplands and conserving vital forest and grassland ecosystems to achieve LDN in this semi-arid region. Further research is needed to quantify specific degradation drivers, assess the effectiveness of intervention strategies, and explore the socio-economic dimensions of land degradation and LDN efforts.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"21 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519831","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}

{"title":"An investigation into the changes in the number and intensity of coincident Mediterranean-Red Sea cyclones (CMRSC) simultaneous with Iran’s precipitation","authors":"Hossein Asakereh, Roya Poorkarim Barabadi","doi":"10.1007/s12145-024-01362-y","DOIUrl":"https://doi.org/10.1007/s12145-024-01362-y","url":null,"abstract":"<p>The variation of synoptic systems impacting Iran's precipitation climatology can have significant climatic consequences. Among the cyclones contributing to the occasionally widespread precipitation in Iran are the coincident Mediterranean-Red Sea cyclones (CMRSC). This research aims to elucidate the long-term associated with the frequency and intensity of CMRSC by examining geopotential height (GH) and geopotential height gradient (GHG), along with CMRSC patterns, as influential factors on Iran's precipitation climatology. To achieve this, 4-daily GH data at 1000 hPa from the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA-Interim, spanning from 1979 through 2018, were utilized. Throughout this period, a total of 97 CMRSC events accompanied precipitation in Iran. Methodologically, the non-parametric Chi-square test, alongside the Standard Normal Homogeneity Test (SNHT), were employed to assess variations in the cyclones' frequency and strength. The Chi-square non-parametric statistic was harnessed to discern trends in GH and GHG, while linear regression was applied to ascertain long-term trends. The findings indicate that the number of CMRSC did not display statistically significant changes over the study period when comparing successive decades and two successive halves of the time series. Notwithstanding, a more detailed examination of shorter timescales, particularly towards the end of the study period, disclosed statistically significant changes in two- and four-year averages. Additionally, the increase in GH at the formation site of Mediterranean cyclones and the decrease in GHG likely contributed to reduced atmospheric instability and precipitation in the affected regions. A pronounced GHG jump in the Mediterranean Sea in 1996 divided the time series into two distinguishable periods. The results demonstrate an upward trend in both periods; however, the second period exhibited a more gradual increase compared to the preceding period.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"14 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519833","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}

{"title":"Assessing the performance of GNSS PPP-AR using OSB products from different analysis centers","authors":"Qi Zhang, Shuhui Li, Lihua Li, Linhui Zhao, Zihang Niu, Huimin Cao","doi":"10.1007/s12145-024-01371-x","DOIUrl":"https://doi.org/10.1007/s12145-024-01371-x","url":null,"abstract":"<p>Integer ambiguity resolution (AR) has been demonstrated to be an effective approach to reduce convergence time and improve accuracy for Precise Point Positioning (PPP) technology. Many bias products have been employed for PPP-AR, including wide-lane uncalibrated phase delays (UPD), narrow-lane UPD, and re-estimated clocks merged with ionosphere-free UPD. Recently, observable-specific signal bias (OSB) products that can be directly applied to raw observations are preferred by International GNSS Service (IGS). Several IGS Analysis Centers (ACs) now release independent orbit, clock and OSB products on a regular basis. To evaluate the performance of the OSB products and IGS precise products from different ACs, this study investigated the performance of GPS-only and multi-GNSS combined PPP-AR using satellite products from four ACs: National Center for Space Studies (CNES), Wuhan University Multi-GNSS Experiment (WUM), Center for Orbit Determination in Europe (CODE), and German Research Centre for Geosciences (GFZ). Observations from 30 IGS MGEX stations over 15 days with an interval of 30 s were processed in PPP static solution, and the results indicate that the float solution is minimally affected by the choice of satellite product but a significant influence on the fixed solution. In general, the CODE products outperform the others in terms of positioning performance. For GPS/Galileo/BDS combined PPP-AR, the satellite products from GFZ outperform those from WUM. Furthermore, the convergence time to achieve centimeter-level coordinate accuracy is approximately 13 min for GPS-only PPP-AR, while can be shortened to about 6 min for GPS/Galileo PPP-AR. However, the GPS/Galileo/BDS PPP-AR reduces the convergence time by about 0.5–1.6 min compared to GPS/Galileo PPP-AR.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"13 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550467","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}

{"title":"EGMS-toolkit: a set of Python scripts for improved access to datasets from the European Ground Motion Service","authors":"Alexis Hrysiewicz, Mahdi Khoshlahjeh Azar, Eoghan P. Holohan","doi":"10.1007/s12145-024-01356-w","DOIUrl":"https://doi.org/10.1007/s12145-024-01356-w","url":null,"abstract":"<p>Continental-scale, open-access datasets of ground surface displacement in all countries of the European Union, plus Norway, United Kingdom, and Iceland, are now available from the European Ground Motion Service (EGMS). Under the European Union’s Copernicus program, the EGMS has been available since the end of 2022 and will continue for the foreseeable future. The EGMS data are presently derived from Interferometric Synthetic Aperture Radar (InSAR) processing of the Sentinel-1 SAR satellite imagery, which has been collected from 2015 to date. While EGMS data can be visualised and obtained through an online platform (EGMS Explorer), the data access arrangements are inefficient for large-scale analysis of ground surface displacements due to the volume of data, the tile-formatting of datasets and some server limitations. Here we present a Python-based toolkit, named <i>EGMS-toolkit</i>, to provide a unified and more efficient workflow for accessing EGMS datasets. The toolkit can automatically detect and download EGMS datasets based on a Region of Interest provided by users, then it can merge, clip, and crop the results to that region regardless of its scale. The toolkit then produces files of EGMS ground surface motions in GIS-ready formats for further analysis.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"237 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519835","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}

{"title":"Ensemble machine learning models for forecasting tropical cyclones in North Indian region","authors":"Md Yeasin, Ranjit Kumar Paul, S. Vishnu Shankar","doi":"10.1007/s12145-024-01366-8","DOIUrl":"https://doi.org/10.1007/s12145-024-01366-8","url":null,"abstract":"<p>Forecasting of tropical cyclones helps the coastal communities to prepare and minimize the damage. Despite numerous studies, the inherent complexity and non-linear nature of cyclones pose challenges for achieving accurate forecasts. The versatility and effectiveness of ensemble forecasting techniques make them well-suited for cyclonic data. This study proposed a Dynamic Weight Ensemble model (DWEnsemble) based on Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Support Vector Regression (SVR) model for predicting tropical cyclones in north Indian region using NINO and Oceanic Nino Index (ONI) as exogenous variables. The data on number of cyclones occurred in India during the period 1951 to 2022 has been considered for the present study. The NINO and ONI series have been used as exogenous variables to predict the number of cyclones in India. To understand the historical changes in cyclone frequency over the last seven decades, trend and residual trend methods were implemented using the Mann-Kendall test in conjunction with Sen’s slope. The performance of proposed model was compared with candidate models and Fixed Weight Ensemble (FWEnsemble) model using Root Mean Squared Logarithmic Error (RMSLE) and Symmetric Mean Absolute Percentage Error (SMAPE). The findings indicated that DWEnsemble outperformed the other models with RMSLE and SMAPE values 0.470 and 0.494 respectively, showcasing its effectiveness in tropical cyclone prediction. Five years out of sample forecast have been obtained and distributed month wise using transition probability matrix derived through Markov Chain analysis.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"32 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519807","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}

{"title":"Enhancing image processing performance with attention long short-term domain adversarial crossover orchard algorithm","authors":"K. Venkatraman, A. Chandrasekar, S. Radhika","doi":"10.1007/s12145-024-01331-5","DOIUrl":"https://doi.org/10.1007/s12145-024-01331-5","url":null,"abstract":"<p>The usage of digital devices has increased across the world due to global digitalization. The global digitalization concept arises due to the easy accessibility of the internet around the world. One of the important data that contributes a large amount to the internet is the image data. Images are used in various fields for advertising the products, expressing the individual’s point of view, describing the diseases in the medical field, etc. Hence a large number of images need to be transformed into digital images to exchange them in the digital images. The process of converting a normal image into a digital image and then extracting information from it is known as image processing. The existing techniques for image processing exhibit issues like high error rates and poor image processing performance. To overcome these issues, this paper proposes an Attention Long Short-term Domain Adversarial Crossover Orchard (ALSDA-CO) algorithm. The proposed method is highly effective in image processing approach and extracted varied class labels clearly that are not visible effectively. The weight functions of each image are trained effectively and optimize the hyperparameters with best solution. To evaluate the performance of this algorithm, the Fruits 100 dataset is used. The proposed algorithm showed better performance than the existing methods such as pre-trained CNN, LS-SVM, MobileNetV2-LSTM, and DCNN in terms of all the performance measures used. The proposed algorithm attained 98.5% accuracy, 98.9% recall, 98.7% precision, and 98.5% F1 score in image processing. Compared to existing approaches the attained range of accuracy outperformed 1.1% of proposed method than existing approaches. The proposed algorithm also exhibited a reduced error rate and enhanced image processing.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"33 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519836","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}

{"title":"Suspended sediment load prediction in river systems via shuffled frog-leaping algorithm and neural network","authors":"Okan Mert Katipoğlu, Gaye Aktürk, Hüseyin Çağan Kılınç, Zeynep Özge Terzioğlu, Mehdi Keblouti","doi":"10.1007/s12145-024-01338-y","DOIUrl":"https://doi.org/10.1007/s12145-024-01338-y","url":null,"abstract":"<p>Suspended sediment load estimation is vital for the development of river initiatives, water resources management, the ecological health of rivers, determination of the economic life of dams and the quality of water resources. In this study, the potential of Feed Forward Neural Network (FFNN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Shuffled Frog Leaping Algorithm (SFLA) models was evaluated for suspended sediment load (SSL) estimation in Yeşilırmak River. The heat map of Pearson correlation values of meteorological and hydrological parameters in 1973–2021, which significantly impacted SSL estimation, was examined to estimate SSL values. As a result of the analysis it was developed a prediction model with three different combinations of precipitation, stream flow and past SSL values (M1: streamflow, M2: streamflow and precipitation, M3: streamflow, precipitation, and SSL). The prediction accuracy of the models was visually compared with the Coefficient of Determination (R²), Bias Factor (BF), Mean Absolute Error (MAE), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), Kling-Gupta Efficiency (KGE) statistical criteria and Bland-Altan plot, boxplot, scatter plot and line plot. Based on the analyses, the PSO-ANN model in the M1 model combination showed good estimation performance with an RMSE of 1739.92, MAE of 448.56, AIC of 1061.55, R² of 0.96, MBE of 448.56, and BF of 0.29. Similarly, the SFLA-ANN model in the M2 model combination had an RMSE of 1819.58, MAE of 520.64, AIC of 1069.9, R² of 0.96, MBE of 520.64, and BF of 0.19. In the M3 model combination, the SFLA-ANN model achieved an RMSE of 1423.09, MAE of 759.88, AIC of 1071.9, R² of 0.81, MBE of 411.31, and BF of -0.77. Overall, these models can be considered good estimators as their predicted values are generally close to the measured values. The study outputs can help ensure water structures’ effective lifespan and operation and take precautions against sediment-related disaster risks.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"21 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519808","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}

{"title":"An improved dung beetle optimization with recurrent convolutional neural networks for efficient detection and classification of undersea water object images","authors":"J. Jeno Jasmine, S. Edwin Raja, R. Muniraj, T. Jarin","doi":"10.1007/s12145-024-01358-8","DOIUrl":"https://doi.org/10.1007/s12145-024-01358-8","url":null,"abstract":"<p>The exploration of the underwater environment has become increasingly important due to the utilization and development of deep-sea resources in recent years. To overcome the hazards of high-pressure deep-sea conditions, autonomous underwater operations have become essential, with intelligent computer vision playing a pivotal role.This study proposes a novel deep-learning model for the effective detection and classification of underwater object images (UWOI). The model addresses the challenge of low-quality, weak illumination, and noise in underwater images by employing an Anisotropic Diffusion Filter (ADF) during pre-processing. To enhance segmentation accuracy, the model utilizes Adaptive Spectral Clustering (ASC). Textural and statistical features are then extracted using the Gray Level Co-occurrence Matrix (GLCM) for robust feature representation. Finally, the proposed model leverages an Improved Dung Beetle Optimization (IDBO) algorithm in conjunction with a Recurrent Convolutional Neural Network (RCNN) for UWOI detection and classification. Extensive evaluations demonstrate that the proposed model achieves significantly improved performance compared to previous methods, attaining superior results in terms of accuracy, Dice score, sensitivity, Structural Similarity Index (SSIM), and specificity. The proposed method consistently demonstrates strong performance in both specificity and sensitivity compared to existing methods, with specificity ranging from 94 to 97% across iterations (10–100), exceeding existing methods (SDCS, UCPS, AEA-QoS, and RLOD), and sensitivity ranging from 94% to 96.65% across iterations (10–50), with the value rising to 96.65% at the 100th iteration. Overall, the findings suggest that the proposed method achieves both high true positive rates (specificity) and low false negative rates (sensitivity), indicating its effectiveness in correctly identifying true targets and minimizing false alarms compared to existing methods. This work contributes to the advancement of underwater object recognition by offering a robust and efficient deep-learning approach.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"65 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519806","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}