Advances in Space Research最新文献

{"title":"Capabilities of the GAMMA-400 gamma-ray telescope to study the relationship between high-energy particle precipitation from the Earth’s inner radiation belt and electromagnetic radiation of gamma-ray bursts","authors":"A.G. Mayorov ,&nbsp;A.A. Leonov ,&nbsp;S.Yu. Aleksandrin ,&nbsp;V.V. Alexeev ,&nbsp;I.V. Arkhangelskaja ,&nbsp;A.I. Arkhangelskiy ,&nbsp;A.V. Bakaldin ,&nbsp;I.V. Chernysheva ,&nbsp;O.D. Dalkarov ,&nbsp;M.D. Kheymits ,&nbsp;M.G. Korotkov ,&nbsp;A.V. Kuznetsov ,&nbsp;V.V. Malakhov ,&nbsp;A.G. Malinin ,&nbsp;P.Yu. Minaev ,&nbsp;D.N. Morozova ,&nbsp;N.Yu. Pappe ,&nbsp;M.V. Razumeyko ,&nbsp;S.A. Siruk ,&nbsp;Yu.I. Stozhkov ,&nbsp;Yu.T. Yurkin","doi":"10.1016/j.asr.2025.04.026","DOIUrl":"10.1016/j.asr.2025.04.026","url":null,"abstract":"<div><div>Capabilities of the GAMMA-400 gamma-ray telescope for measurements of gamma-ray bursts (GRB) spectra under 100 MeV turns out to be very useful in the view of relationship between high-energy particle precipitation from the Earth’s inner radiation belt and the electromagnetic radiation from the GRB’s. The search of possible correlation between these phenomena is motivated by the results from the PAMELA spectrometer where we found and analyzed several such events corresponding to precipitating particles of energies more than 50 MeV. To date, no studies have been found that paid attention to the influence of sources of electromagnetic radiation of GRB on precipitation of geomagnetically trapped particles. In this view, detailed measurements of GRB spectrum especially in the energy range up to 100 MeV, is quite important for establishing a theoretical framework to explain a possible mechanism of particles’ precipitation due to GRB or to discard the hypothesis of their interconnection.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"76 1","pages":"Pages 286-297"},"PeriodicalIF":2.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144222754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Error compensation method of GNSS/INS integrated navigation system based on PSO-LSTM","authors":"Guiling Zhao,&nbsp;Yuan Wang,&nbsp;Xu Wang","doi":"10.1016/j.asr.2025.04.024","DOIUrl":"10.1016/j.asr.2025.04.024","url":null,"abstract":"<div><div>The integrated navigation system based on the Global Navigation Satellite System and Inertial Navigation System (GNSS/INS), can provide continuous and dependable positioning information for airborne motion. The GNSS signal outages are easily caused by electromagnetic interference environments such as high-rise buildings, and canyons high-voltage towers. A long period of missing GNSS positioning information will lead to a rapid decline in the navigation precision of the GNSS/INS system. In order to solve this problem, the optimum value of Long Short-Term Memory (LSTM) is obtained through Particle Swarm Optimization (PSO). An LSTM network model based on the PSO algorithm (PSO-LSTM) is designed. It is used to assist the integrated navigation of GNSS/INS based on Kalman Filter (KF). When GNSS signals are available, the PSO-LSTM model is trained with carrier dynamic information and navigation information. When GNSS signals are not available, the PSO-LSTM model is used to obtain pseudo-GNSS signals for Kalman Filter measurement updates. In order to verify the validity of the algorithm, unmanned aerial vehicle (UAV) flight tests of GNSS signal outages are performed. The results of the positioning are compared to the conventional LSTM model. When the GNSS signal experiences outages of 30 s, the maximum positioning error of the LSTM model is 4.142 m. The PSO-LSTM model is 2.883 m, which decreases by 30.4 % relative to LSTM. When the GNSS signal experiences outages of 60 s, the maximum positioning error of the LSTM model is 5.992 m. The PSO-LSTM model is 2.898 m, which decreases by 51.2 % relative to LSTM. When the GNSS signal experiences multiple outages, the maximum positioning error of the LSTM model is 11.362 m. The PSO-LSTM model is 6.042 m, which decreases by 46.8 % relative to LSTM.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 12","pages":"Pages 8657-8666"},"PeriodicalIF":2.8,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 agricultural drought monitoring with the Integrated Agricultural Drought Index (IADI): A multi-source remote sensing approach","authors":"Ujjal Senapati ,&nbsp;Tapan Kumar Das","doi":"10.1016/j.asr.2025.04.021","DOIUrl":"10.1016/j.asr.2025.04.021","url":null,"abstract":"<div><div>Drought has significantly threatened India’s agro-economic sector, resulting in widespread environmental degradation and socio-economic challenges. This study provides remote sensing technology to overcome the limitations of traditional drought monitoring methods. To address this, Integrated Agricultural Drought Index (IADI), a newly proposed index that combines multiple remotely sensed-derived indicators with Analytical Hierarchy Process (AHP) for comprehensive agricultural drought assessment, is used. IADI integrates indicators such as Vegetation Health Index (VHI), Normalized Difference Drought Index (NDDI) and Enhanced Vegetation Index (EVI) to provide a concise assessment suitable for different farming systems. The study was conducted in the upper Dwarkeswar river basin focusing on drought trends from 2000 to 2019. Initially, drought years were identified using the Rainfall Anomaly Index (RAI). Subsequently, spatial drought indices were calculated and integrated into the IADI framework. The results revealed significant spatial variation in drought severity with an annual increase of 21.88 km² in drought-affected areas (R² = 0.98). Validation using Area Under Curve-Receiver Operating Characteristic (AUC-ROC) showed that the IADI achieved an accuracy of 76 %, outperforming individual indices such as VHI (72 %), NDDI (49 %) and EVI (47 %). These results highlight the improved predictive power of IADI, providing useful insights for real-time drought monitoring, resource allocation and strategic planning. This approach provides a sustainable framework for drought management, assisting policymakers and stakeholders in enhancing regional food security and resilience to climate variability.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 12","pages":"Pages 8599-8626"},"PeriodicalIF":2.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 innovative hybrid method utilizing fused transformer-based deep features and deep neural networks for detecting forest fires","authors":"Kemal Akyol","doi":"10.1016/j.asr.2025.04.020","DOIUrl":"10.1016/j.asr.2025.04.020","url":null,"abstract":"<div><div>Forest fires, one of the most pernicious and devastating disasters, cause deforestation, wildlife extinction, global warming, and climate change. Early fire detection is critical before it reaches catastrophic dimensions. Artificial intelligence-based systems that detect forest fires accurately and quickly are needed for early intervention. Delayed extinguishing efforts without such systems cause tremendous damage and losses. An effective monitoring system allows for the reduction of fire damage and hence prevents forest loss. This study aims to develop a successful artificial intelligence model to detect fire from forest landscape images. In this context, this work is new as it provides new insights into robust and scientific modeling for forest fire detection by analyzing feature maps based on fused transformer architectures using Deep Neural Networks. The experimental models were validated using accuracy, sensitivity, precision, and area under the receiver operating characteristic curve measures. The validation findings reveal that the proposed hybrid model performs the best while all models yield reasonable results. To summarize, satisfactory accuracy values of 99.58% and 96.79% for both datasets, respectively, strongly support the proposed hybrid model’s fire detection achievement with the 5-fold cross-validation. Furthermore, the high sensitivity and high precision measures imply that the model has few false negatives and false positives. Considering the obtained accuracies, the proposed hybrid model could be used for comprehensive fire detection modeling. To the author’s best knowledge, this study is the first to use transformer architectures and Deep Neural Networks for forest fire detection and is therefore important for the relevant literature. In this context, this study presents a new approach to distinguishing landscape images of forest fires and further developing fire detection strategies by the role of transformer architectures in feature extraction. It is thought that by executing the proposed model in an unmanned aerial vehicle equipped with a real-time system, fire detection will provide decision support to field professionals in reducing damage and managing forest fires.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 12","pages":"Pages 8583-8598"},"PeriodicalIF":2.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial coupling coordination evaluation between land use efficiency and urbanization in the Chang-Zhu-Tan urban agglomeration, China","authors":"Qiong Zheng,&nbsp;Huangteng Zhu,&nbsp;Qing Xia,&nbsp;Zixiao Guo,&nbsp;Lihong Zhu","doi":"10.1016/j.asr.2025.04.023","DOIUrl":"10.1016/j.asr.2025.04.023","url":null,"abstract":"<div><div>Ensuring that land use efficiency and urbanization are in harmony has emerged as a critical concern requiring immediate attention to achieve sustainable urbanization in China. Taking the Chang-Zhu-Tan urban agglomeration (CZTUA) as an example, this study establishes an evaluation index system of land use efficiency and urbanization using multi-source remote sensing data in 2020 and analyzes the coupling coordination degree (CCD) between these factors. Furthermore, the standardization methods and exploratory spatial data analysis method are combined to investigate the spatial correlation of the CCD among land use efficiency and urbanization. The study shows that: (1) In CZTUA, there is a positive correlation between land use efficiency and urbanization and this promotes urbanization. (2) The overall coupling degree in the CZTUA is in the running-in level and most districts are at the elementary or middle rank coordinated levels. (3) The spatial clustering characteristics have the “center-periphery” pattern, with the further away from the core city, the lower the level of coupling coordination becomes. (4) From the perspective of geographical association, the High-High agglomeration, mainly located in Changsha City, showed higher development levels compared to surrounding areas. Development gradually diminished as one moves outward, forming a Low-High agglomeration. Low-Low agglomeration was represented by You, Chaling and Yanling districts. The methodology used in this study is relevant for promoting urbanization and improving land use efficiency in Chinese city clusters.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 12","pages":"Pages 8642-8656"},"PeriodicalIF":2.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-satellite based possible precursory signals detection linked to the 2024 Mw 7.5 Noto Peninsula Japan earthquake","authors":"Rasim Shahzad ,&nbsp;Munawar Shah ,&nbsp;Imtiaz Nabi ,&nbsp;Punyawi Jamjareegulgarn","doi":"10.1016/j.asr.2025.04.015","DOIUrl":"10.1016/j.asr.2025.04.015","url":null,"abstract":"<div><div>Satellite-based anomaly detection can provide substantial precursory information linked to impending earthquakes (EQ). The strong EQs are followed by some complex precursory signals both before and after the main shock. For this, different methods and datasets are employed to monitor these disastrous events. In our study, we used the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to evaluate land surface temperature (LST), the Global Navigation Satellite System (GNSS) to observe total electron content (TEC) variations, Swarm satellites to monitor spatial variations in electron density, and Cosmic satellites were used to measure variations in the vertical profile of electron density to look for the complex precursors of Noto Peninsula Japan EQ of 7.5 Mw (occurred on 1st January 2024). Our objective was to observe both the pre- and post-EQ induced anomalies within 25 days and 10 days of the main shock by integrating the statistical, nonlinear autoregressive network with exogenous inputs (NARX) and continuous wavelet transformation (CWT) methods. We found synchronized and co-located pre-seismic anomalies on December 25 in LST, TEC and electron density. Which was further confirmed using NARX and CWT as well. Additionally, we found some potential post-seismic anomalies. There was an anomalous enhancement in daytime LST, TEC, and electron density on January 2nd with the exception of nighttime LST which showed abrupt increments on the night of the main shock (i.e., January 1st). These findings point towards the strong EQ-induced energy into the atmosphere and ionosphere for more prominent proof of lithosphere-atmosphere–ionosphere coupling (LAIC).</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 12","pages":"Pages 8830-8848"},"PeriodicalIF":2.8,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CMKD-Net: a cross-modal knowledge distillation method for remote sensing image classification","authors":"Huaxiang Song ,&nbsp;Junping Xie ,&nbsp;Yingying Duan ,&nbsp;Xinyi Xie ,&nbsp;Yang Zhou ,&nbsp;Wenhui Wang","doi":"10.1016/j.asr.2025.04.009","DOIUrl":"10.1016/j.asr.2025.04.009","url":null,"abstract":"<div><div>Cross-modal knowledge distillation (KD) offers the potential to synergize the strengths of Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) in remote sensing image (RSI) classification. However, existing KD techniques in this field are frequently ineffective and time-consuming. We contend that this inefficiency stems from the data sparsity inherent in RSI samples—a challenge long overlooked in previous studies. To address this issue, we propose a novel algorithm designed to alleviate data sparsity and enhance the quality of the training data. Building upon this, we introduce CMKD-Net, a KD framework that facilitates knowledge transfer from a ViT teacher to a CNN student model. Experimental evaluations on three RSI datasets demonstrate that CMKD-Net outperforms 17 state-of-the-art models published since 2022 on classification accuracy and model compactness. Furthermore, our method cuts down training time by at least 83% compared to current KD methods, making cross-modal KD for RSI classification much more effective.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 12","pages":"Pages 8515-8534"},"PeriodicalIF":2.8,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A landslide susceptibility modeling method using an optimal parameters-based geographical detector","authors":"Xiaokang Liu ,&nbsp;Shuai shao ,&nbsp;Shengjun Shao ,&nbsp;Chen Zhang","doi":"10.1016/j.asr.2025.04.019","DOIUrl":"10.1016/j.asr.2025.04.019","url":null,"abstract":"<div><div>Current methodologies for modeling landslide susceptibility using machine learning algorithms typically involve grading landslide conditioning factors, selecting major factors, and calculating model input data. However, these approaches are often highly subjective and random, requiring sequential, step-by-step calculations that significantly reduce modeling efficiency. To address these limitations, this study proposes a novel landslide susceptibility modeling method based on an Optimal Parameters-based Geographical Detector (OPGD). Leveraging the theory of spatial stratified heterogeneity, the proposed method determines the optimal grading strategy for conditioning factors and ranks the importance of all factors. Furthermore, this study introduces risk detection metrics derived from the OPGD model as inputs to the susceptibility model, enabling simultaneous factor grading and model input data calculation. The effectiveness of the proposed approach is validated through six models based on two machine learning algorithms—Random Forest (RF) and Support Vector Machine (SVM)—across three regions of the Loess Plateau. The results demonstrate that the proposed method achieves exceptional landslide prediction performance, with AUC values consistently close to or exceeding 0.9. Notably, the RF-based model outperforms others, achieving a maximum AUC value of 0.978. The proposed method is not only straightforward to implement but also provides an interpretable modeling process, making it applicable to any region requiring landslide susceptibility analysis. Overall, this study significantly enhances modeling efficiency, bridges the gap between theoretical knowledge and practical applications, and advances the standardization of landslide susceptibility modeling.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 12","pages":"Pages 8561-8582"},"PeriodicalIF":2.8,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Damage analysis of aluminum projectiles and aluminosilicate fibrous porous ceramic targets in hypervelocity impacts","authors":"Wentong An ,&nbsp;Runqiang Chi ,&nbsp;Miao Sun ,&nbsp;Xianpeng Zhou ,&nbsp;Hongyu Zhang ,&nbsp;Wuxiong Cao ,&nbsp;Baojun Pang ,&nbsp;Xiaoxia Lu","doi":"10.1016/j.asr.2025.04.016","DOIUrl":"10.1016/j.asr.2025.04.016","url":null,"abstract":"<div><div>Low-density porous materials are widely used in cosmic dust collectors, low-density asteroid simulant targets, and thermal protection systems for space vehicles. However, the interaction processes between projectiles and porous materials remains to be further investigated. In this paper, a two-stage light gas gun was employed to conduct the experiments of aluminum projectiles impacting aluminosilicate fibrous porous ceramic targets at different velocities. The impact process was captured in situ using the flash X-ray radiography system. The damage characteristics of projectiles and targets, the damage processes of projectiles, and the formation processes of target cavities were investigated. The results show that the damage of the projectile can be classified into plastic deformation and fragmentation states, significantly influencing the evolution of the target cavity. When the impact velocity is below 3.37 km/s, the projectile only exhibits plastic deformation and penetrates the target as a whole, resulting in a “carrot” cavity. In contrast, when the impact velocity reaches or exceeds 3.37 km/s, the projectile breaks and penetrates the target in the form of fragments, resulting in a “bulb” cavity. The analysis of the interaction processes between the projectile and the porous material provides important reference data for validating numerical simulations and theoretical models.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 12","pages":"Pages 8792-8804"},"PeriodicalIF":2.8,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust distributed autonomous orbit determination for LEO mega constellation based on inter-satellite ranging and astronomical measurement","authors":"Yuanlan Wen ,&nbsp;Ping Zeng ,&nbsp;Chengxin Ran ,&nbsp;Zhetao Zhang ,&nbsp;Xiufeng He ,&nbsp;Lina He ,&nbsp;Zhaokui Wang ,&nbsp;Fucheng Liu","doi":"10.1016/j.asr.2025.04.013","DOIUrl":"10.1016/j.asr.2025.04.013","url":null,"abstract":"<div><div>Low earth orbit (LEO) satellites are pivotal for the development of mega satellite constellations (MSC), offering low latency and global coverage. However, the rising number of satellites in LEO MSC presents significant challenges, particularly in terms of computational burden and data processing requirements. In this study, a robust distributed autonomous orbit determination (AOD) for LEO MSC based on inter-satellite link (ISL) ranging and astronomical observations is proposed. Specifically, first, we fully consider the advantages of ISL observations in autonomous navigation, and utilize astronomical observations to compensate for the missing datum mark problem. Second, to address the significant computational burden resulting from the large number of satellites in the LEO MSC, a distributed AOD strategy that combines active and auxiliary satellites is given. In addition, the robust AOD method of balanced extended kalman filter (BEKF) for LEO MSC is derived. The results show that the proposed distributed strategy with robust parameters estimation method can achieve convergence time within 30 min even with up to 1600 satellites, while the traditional method needs over 240 min. In addition, the combination of ISL ranging for distance constraints and astronomical observations for directional constraints significantly enhances accuracy in T and N directions by approximately 20 %. Furthermore, the error accumulation effect is reduced by approximately 64 % compared to traditional methods as the number of satellites increases from 400 to 1600. Moreover, as the complexity of LEO MSC increases, the traditional amplified measurement covariance extended kalman filter (AMCEKF) method may become ineffective. However, the robust BEKF method enables satellites with lower orbit accuracy to make incremental adjustments, while satellites with higher orbit accuracy receive fewer adjustments. Therefore, the proposed robust method of BEKF balances orbit accuracy among satellites, and the stability of the BEKF method is improved compared to the traditional method.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 12","pages":"Pages 8545-8560"},"PeriodicalIF":2.8,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}