Acta Geophysica最新文献

{"title":"An integrated three-dimensional water and multilayer sediment quality model for Tokyo Bay","authors":"Morteza Jedari Attari","doi":"10.1007/s11600-025-01548-y","DOIUrl":"10.1007/s11600-025-01548-y","url":null,"abstract":"<div><p>The aim of this study is to develop, combine, and optimize a three-dimensional water quality and sediment transport model with a bed module. This bed model incorporates a layered model that defines both dissolved and solid phases in vertically layered dimensions. To calibrate the integrated model, a comprehensive dataset consisting of water quality parameters in Tokyo Bay (Japan) was utilized. Tokyo Bay is known for its heavy eutrophication, which results in the accumulation of soft-organic sediment in inner parts, particularly near the head of the bay. One key focus of this study was to investigate near-bed–water conditions and their impact on oxygen depletion, nutrient cycling, primary production, and overall water quality. The results obtained from simulating particulate organic carbon content ratio on the sediment surface were found to be consistent with observed values. Additionally, accurate simulations of other water quality parameters were successfully achieved using this developed model. This developed model proves to be a valuable tool for future studies aimed at improving environmental management strategies within Tokyo Bay. The sophisticated modeling of hypoxic and anoxic conditions in inner parts of the bay, which pose chronic threats to benthic life and severely deteriorate water quality, provides valuable insights for analyzing the consequences of various scenarios related to anthropogenic actions. This model can assist researchers and policymakers in making informed decisions regarding environmental management strategies within Tokyo Bay.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 3","pages":"2683 - 2724"},"PeriodicalIF":2.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879510","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":"Evaluation of IMERG precipitation product in the investigation of drought events in the Kermanshah Province","authors":"Morteza Gheysouri,&nbsp;Ataollah Kavian,&nbsp;Mahin Kalehhouei,&nbsp;María Fernández-Raga,&nbsp;Jesus Rodrigo-Comino","doi":"10.1007/s11600-025-01558-w","DOIUrl":"10.1007/s11600-025-01558-w","url":null,"abstract":"<div><p>Our world is facing new challenges due to the prolonged drought events that have occurred over the past decade. These events are affecting ecosystem services and Earth surface processes adding unpredictable responses to be managed. In this study, a comprehensive evaluation of integrated multi-satellite retrieval (IMERG) global precipitation measurement products was conducted to determine the drought situation in semi-arid and arid areas. Predicted precipitation values from the final IMERG from 2001 to 2020 were used to evaluate 3, 6, and 12 months of drought in 13 meteorological stations in the Kermanshah Province, a representative study area. The results showed that IMERG has a relatively good performance in detecting the spatial patterns of heavy and low precipitation, so that in the northern stations, where the rainfall amount is higher, IMERG has shown good performance. The drought results obtained from IMERG show that the SPI-3 index is close to the station data; therefore, the average of RMSE, <i> R</i>², and <i>E</i>_NS of the 3 month time step data is equal to 0.7, 0.5, and 0.6 compared to SPI-6 and 12 data. The results of drought frequency showed that drought intensity in the Kermanshah Province was higher than that in SPI-3 at 12 and 6 months, whereas the 3 month drought frequency closely matched that of the station data. In examining the time step of the drought event, it was found that the IMERG data showed the drought 1–2 years earlier than the station data, which can be used to evaluate early droughts. In this case, we demonstrated that the SPI-3 drought index obtained from IMERG data was the best indicator of drought. The overall findings indicate that IMERG data are able to simulate droughts earlier than station data and can be a useful tool for drought monitoring and disaster management in semi-arid and arid regions. This research can be applied in the use of satellite precipitation products for regional drought management.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 3","pages":"2669 - 2682"},"PeriodicalIF":2.3,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879647","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":"Hybrid machine learning for drought prediction at multiple time scales: a case study of Ağrı station, Türkiye","authors":"Hatice Citakoglu,&nbsp;Gaye Aktürk,&nbsp;Vahdettin Demir","doi":"10.1007/s11600-024-01501-5","DOIUrl":"10.1007/s11600-024-01501-5","url":null,"abstract":"<div><p>Drought is a prolonged period of significantly reduced precipitation, resulting in water scarcity and environmental stress. In this study, Ağrı province, situated in the eastern region of Türkiye, where most of the land cannot be irrigated and the livelihood is based on agriculture, was selected as the study area. Meteorological droughts in Ağrı province were forecasted using hybrid machine-learning models, leveraging monthly precipitation and temperature series from 1965 to 2022. The study employed the standardized precipitation index (SPI), relying solely on precipitation data, and the standardized precipitation evapotranspiration index (SPEI), which also considers both temperature and precipitation data. Various timescales, including 1M (1 month), 3M, 6M, 9M, and 12M, were taken into consideration. The best model for each hybrid model was determined using data at time points t, t-₁, t_-2, t_-3, and t_-4 for the relevant time series. The study combined ensemble least squares boosting algorithms (LSBoosting), adaptive network-fuzzy inference system (ANFIS), support vector machines (SVM), Gaussian process regression (GPR), and M5 model tree (M5Tree) approaches with the variational mode decomposition (VMD) technique to create hybrid models. The results indicate that certain models perform better at different timescales, with M5Tree and GPR generally providing higher accuracy. For instance, the M5Tree model achieved the lowest MAE (0.0714 and 0.0555) and RMSE (0.0909 and 0.0732) values for the _9MSPI and _12MSPI timescales, respectively, making it the best-performing model at these scales. Similarly, the GPR model stood out for the _1MSPI and _6MSPI scales with the lowest MAE values (0.1336 and 0.0736, respectively). Based on the performance criteria, the best hybrid model for the _1MSPI was the GPR approach. For the SPEI, except for _3MSPEI, the M5Tree approach showed the best performance at other timescales. However, since the prediction outcomes gave similar results according to classical performance criteria, a one-sided Wilcoxon sign rank test was applied to the outcomes of ANFIS, GPR, and M5Tree models for _3MSPEI, _6MSPI, _9MSPI, and _12MSPI. It has been determined that these three models are not superior to each other. Additionally, the one-sided Wilcoxon signed-rank test found no statistically significant difference between ANFIS, GPR, SVM, and M5Tree models for the _3MSPI. This research concluded that the performance of hybrid machine-learning methods applied to different timescales of SPI and SPEI varies.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 2","pages":"1643 - 1677"},"PeriodicalIF":2.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11600-024-01501-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GIS-based multifactor analysis for positioning water harvesting sites integrated with managed aquifer recharge","authors":"Amin Shaban,&nbsp;Mhamad El Hage,&nbsp;Nasser Farhat","doi":"10.1007/s11600-025-01550-4","DOIUrl":"10.1007/s11600-025-01550-4","url":null,"abstract":"<div><p>Runoff loss to the sea and seawater intrusion into coastal aquifers represents a dual hydrologic phenomenon in many coastal regions, and the coastal zone of Lebanon is a typical example. In this respect, an integrated management approach must be adopted to mitigate the impact of this geo-environmental problem through surface water harvesting and recharging (SWHR) into the beneath rock formations. However, positioning suitable sites, for surface water harvesting, from which water can be artificially/spontaneously recharged, is often a challenging. This study handles this challenge with innovative multi factor method using GIS to identify the optimal sites for SWHR. For this purpose, thematic maps were analyzed and systematically integrated, while data retrieved was mainly from satellite images (e.g., Sentinel-2, SRTM, etc.). The obtained geospatial data represent main factors controlling surface water flow regime, infiltration potential and other relevant environmental factors. Being converted to GIS digital files with various levels of impact “weight coefficients” on SWHR, these factors were systematically manipulated; and thus 28 sites were identified and hydrologically characterized and 13 of them were emphasized as primary ones. The produced SWHR sites' map serves decision-makers to take proper actions for water management in coastal zones whether in the investment of surface water or the mitigation of seawater intrusion.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 3","pages":"2935 - 2954"},"PeriodicalIF":2.3,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879715","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":"Mapping sediment depths using seismic arrays, rotational measurements, and spectral ratios","authors":"Claudia Finger,&nbsp;Sabrina Keil,&nbsp;Aileen Gotowik,&nbsp;Alexander Jüstel,&nbsp;Andreas Brotzer","doi":"10.1007/s11600-025-01552-2","DOIUrl":"10.1007/s11600-025-01552-2","url":null,"abstract":"<div><p>Unconsolidated sediments can amplify ground motions, increasing seismic hazard. Horizontal-to-vertical spectral ratios can derive the thickness of sediments overlaying stiffer bedrock. However, additional information about shear velocities and calibration with other structural information is necessary. Here, we propose a strictly ambient seismic noise-based workflow that can map the depth of sediments without additional information from other data sources. Rayleigh wave dispersion curves and ellipticities are derived from three-component beamforming of ambient noise and combined with dispersion curves from six-component measurements and horizontal-to-vertical spectral ratios. This is used to calibrate empirical relations between frequencies of extreme points in spectral ratio curves with depths of sediments. Applying the relations to more than forty seismic station data, we can map the depth of the Tertiary sediments at the southern margin of the lower Rhine embayment, Germany.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 3","pages":"2657 - 2667"},"PeriodicalIF":2.3,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11600-025-01552-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Daily prediction of Urmia Lake water level using remote sensing data and honey badger optimization-based data-driven models","authors":"Mohsen Saroughi,&nbsp;Okan Mert Katipoğlu,&nbsp;Gaye Aktürk,&nbsp;Enes Gul,&nbsp;Oguz Simsek,&nbsp;Hatice Citakoglu","doi":"10.1007/s11600-024-01520-2","DOIUrl":"10.1007/s11600-024-01520-2","url":null,"abstract":"<div><p>Artificial neural networks (ANNs), support vector regression (SVR) and CatBoost regression (CBR) machine learning methods have been combined with the honey badger optimization algorithm (HBA) and metaheuristic optimization algorithm to accurately and reliably predict lake water level (LWL), which is of great importance for the management and planning of water resources. In this study, meteorological and hydrological parameters, including temperature (T), precipitation (P), date (D), surface soil moisture (SSW), root zone moisture (RZW) and water level (WL), were employed as input data for predicting the LWL of Urmia Lake. The input data were employed to develop six different prediction scenarios. This study not only examined the impact of meteorological and hydrological parameters on LWL prediction but also compared the performance of individual models and hybrid models. The Akaike information criterion (AIC) index was used to ascertain the optimal machine learning model and to evaluate the six prediction scenarios. The results of the study indicate that, according to the AIC index, the data regarding the water level (WL) were significant in the prediction models. However, it should be noted that satisfactory results could also be obtained without using the WL data in certain scenarios. In scenario 4 (input data: D, T, P, SSW, RZW), where the WL variable was not included, the HBA-CBR hybrid model was the best model with the lowest AIC value (Train: -63,735, Test:-4693). In prediction scenario 6 (input data: D, T, P, SSW, RZW, WL), which included the WL data, the HBA-SVR hybrid model demonstrated high performance with the lowest AIC value (Train: -102,358, Test:-27,233). Accordingly, it was recommended to use lagged WL values as input in WL prediction because the prediction accuracy of the models significantly improved. Furthermore, hybrid models were found to perform better than individual models due to their more consistent results.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 3","pages":"2909 - 2933"},"PeriodicalIF":2.3,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879645","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":"The effect of geomorphic and anthropogenic factors on the karst spring occurrence (case studies of central Zagros Mountain Range, Iran)","authors":"Mehrnoosh Ghadimi,&nbsp;Samaneh Esmaili,&nbsp;Seiyed Mossa Hosseini,&nbsp;Mohammadali Kiani","doi":"10.1007/s11600-025-01543-3","DOIUrl":"10.1007/s11600-025-01543-3","url":null,"abstract":"<div><p>Karst groundwaters are vulnerable and essential resources that require comprehensive management for protection and preservation. For this purpose, awareness of effective factors (water quality, low pollution vulnerability, steady temperature, low susceptibility to environmental disaster and climate change) are required for the development of karst water resources and their quality management. Identifying the spatial distribution of springs in karst settings is important for a better understanding of groundwater flow because springs are the terminal sites of karst flow networks which are often understudied. This study aims to identify the location of karst spring occurrence with an emphasis on geomorphic factors using the Analytical Hierarchy Process (AHP) and Logistic Regression (LR) model. As the case studies in this research, the Lordegan and Shahrekord karst basins located in Iran’s Zagros Mountains were selected. Nine factors influencing spring occurrence are considered and classified into four major groups: geological layer (lithology and distance from fault), hydrology layer (distance from river and drainage density), geomorphological layer (slope, aspect, elevation, and plan curvature), and anthropogenic layer (land use/land cover). The occurrence map of karst groundwater spring weighed by AHP was classified into five classes (very low, low, moderate, high, and very high) and both basins were in very high to moderate class. The geological layer (i.e., lithology and distance from faults) was the most significant geomorphological factor in the Lordegan basin, with the weight of 56.3%, whereas the topographical layer (i.e., slope, aspect, elevation, and curvature) was in the Shahrekord basin, with the weight of 38.4%. Due to the high-altitude of the studied basins (1944–3297 m), the land use/land cover layer had the lowest weight.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 2","pages":"1627 - 1641"},"PeriodicalIF":2.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602317","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":"Earthquake recurrence characteristics and earthquake occurrence probabilities in the Anninghe-Zemuhe-Daliangshan fault system, southeastern Tibetan Plateau","authors":"Qi Zhang,&nbsp;Yingwei Du,&nbsp;Yajing Gao","doi":"10.1007/s11600-024-01521-1","DOIUrl":"10.1007/s11600-024-01521-1","url":null,"abstract":"<div><p>The Anninghe-Zemuhe-Daliangshan fault system is one of the most severe seismic hazard regions in China, and it is of great practical significance to analyze the characteristics of seismicity and probability of earthquake occurrence in this region. The available earthquake catalogs from both the historical and instrumental are short and incomplete, making it challenging to accurately estimate the earthquake recurrence behavior and earthquake occurrence probabilities based on these catalogs. A simulated long-term earthquake catalog can largely make up for the shortcomings of the available observational catalogs. In this paper, we established a finite element dynamics model of the Anninghe-Zemuhe-Daliangshan fault system to simulate the earthquake cycles of the regional faults and generate a long-term simulated earthquake catalog that satisfies the regional geodynamic background. Based on the simulated earthquake catalog, we analyzed the characteristics of earthquake recurrence of different magnitudes at different locations along faults in the Anninghe-Zemuhe-Daliangshan fault system, the temporal distribution of these earthquakes, and the occurrence probabilities of strong earthquakes at various future time intervals on each fault. We found that the recurrence behavior of strong earthquakes at the same location along faults in the Anninghe-Zemuhe-Daliangshan fault system often has quasi-periodicity. We observed that the Weibull model can well describe the recurrence times of these strong earthquakes of each fault. Model results showed that the probability of a next strong earthquake to be occurred on the Anninghe fault is the highest in the entire fault system.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 3","pages":"2299 - 2318"},"PeriodicalIF":2.3,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879619","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 understanding of soil electrical behavior: multi-variable analysis and correlation modeling","authors":"Soumitra Kumar Kundu,&nbsp;Rajat Debnath,&nbsp;Ashim Kanti Dey","doi":"10.1007/s11600-025-01532-6","DOIUrl":"10.1007/s11600-025-01532-6","url":null,"abstract":"<div><p>Electrical resistivity (ER) approach leverages soil’s electrical behavior to detect its anomaly in a non-destructive (NDT) manner. This behavior is influenced by varying parameters which include soil’s resistivity, particle size gradation, physical parameters (water content, porosity and density), salinity and temperature which are amongst most crucial ones. Considering all above parameters, field ER tends to provide a continuous subsurface profile in a quick, effective and reliable manner resulting in increase of usage in the past decades. ER measurements are not only confined to in situ tests, but laboratory setup is also developed to estimate ER values. In this context, researchers in the past have adopted both field- and laboratory-based resistivity values to establish correlations with varying soil properties showcasing significant limitations in the form of site-specific nature with single input variable. In this context, the present study aims to evaluate effect of multiple parameters in the form of temperature, clay content, salinity, density, air content and water content on resistivity of soil. Results derived from the current analysis clearly showcases that effect of multiple parameters has a profound impact on soil’s resistivity compared to a single parameter. Further, correlationships were also developed involving simple and multiple regression analysis which resulted in formation of multi-variable model having coefficient of correlation (R²) value of 0.97. Furthermore, sensitivity of variables was also analyzed to ascertain effect of individual parameter on resistivity. Based on derived data, it could be inferred that density and temperature were found to be the most least sensitive input variables. Thus, results derived from the present study would play a pivotal role in accurate assessment of ER values.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 3","pages":"2635 - 2656"},"PeriodicalIF":2.3,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879603","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":"Geophysical-guided Wasserstein cycle-consistent generative adversarial networks for seismic impedance inversion","authors":"Urip Nurwijayanto Prabowo,&nbsp;Sudarmaji Saroji,&nbsp;Sismanto Sismanto","doi":"10.1007/s11600-025-01536-2","DOIUrl":"10.1007/s11600-025-01536-2","url":null,"abstract":"<div><p>Deep learning has shown great ability to solve the nonlinear inversion problem in geophysical fields. Insufficient-labeled data and a lack of geophysical constraints become the main challenges in training the networks. In seismic impedance inversion, combined semisupervised learning and generative adversarial networks (GANs) named cycle-consistent GAN (cyc-GAN) are proven to achieve better inversion accuracy with insufficient labeled data. The next improvement of cyc-GAN is Geo-cyc-GAN, which imposes the convolutional model as a geophysical constraint. This improvement can speed up the training process and achieve better accuracy. However, like most GAN algorithms, the cyc-GAN and Geo-cyc-GAN suffer from training instability. Therefore, we proposed geophysical-guided Wasserstein cycle-consistent generative adversarial networks (Geo-cyc-WGANs) to overcome the training instability of GAN and increase its accuracy. Geo-cyc-WGAN uses Wasserstein distance as a loss function instead of cross-entropy to improve the training stability. The experiment results of synthetic data using small labeled traces show that Geo-cyc-WGAN achieves the highest accuracy, better lateral continuity, and a more stable training process than another geophysical guide-based method. The experiment results of real data also show that Geo-cyc-WGAN can obtain better accurate impedance results than other methods.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 3","pages":"2621 - 2634"},"PeriodicalIF":2.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879568","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}