Earth and Space Science最新文献

{"title":"Evaluation of Vegetation Bias in InSAR Time Series for Agricultural Areas Within the San Joaquin Valley, CA","authors":"Kelly R. Devlin,&nbsp;Rowena B. Lohman","doi":"10.1029/2024EA004062","DOIUrl":"https://doi.org/10.1029/2024EA004062","url":null,"abstract":"<p>Agricultural regions present a particularly difficult set of challenges during interferometric synthetic aperture radar (InSAR) displacement time series analyses due to the existence of abrupt transitions in land use over short spatial scales and rapid temporal changes associated with different stages of the agricultural cycle. Plant growth and soil moisture changes can introduce phase biases within interferograms that could be misinterpreted as displacement. We analyze a full-resolution, multi-year SAR time series over California's San Joaquin Valley, an intensively cultivated region producing a wide variety of crops. Using independent information about land cover and crop type, we isolate the effects of individual crops on backscatter amplitude, interferometric phase change, and interferometric coherence over space and time. We determine the temporal behavior of the phase changes associated with several key crop types by isolating the difference between the phase of pixels averaged over each agricultural field and the phase values of pixels in nearby roads and developed areas. We find that some fields are associated with a bias of ∼2–4 cm/yr of apparent subsidence, with strong seasonal variability in the degree of bias. When InSAR imagery is spatially averaged or filtered, these biases also impact the inferred phase in nearby roads and other land cover types. We show that even a simple approach, where pixels associated with agricultural fields are removed or masked out before further processing, can mitigate the crop-related biases that we observe in the study area.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Mechanism of Multiple Anomalies Prior to Japan Earthquakes From 2021 to 2023: Lithosphere-Coversphere-Atmosphere-Ionosphere Coupling Driven by Pressure-Stimulated Rock Current","authors":"Busheng Xie,&nbsp;Lixin Wu,&nbsp;Licheng Sun,&nbsp;Youyou Xu,&nbsp;Akimasa Yoshikawa,&nbsp;Wenfei Mao","doi":"10.1029/2025EA004320","DOIUrl":"https://doi.org/10.1029/2025EA004320","url":null,"abstract":"<p>This study investigated the underlying mechanism of multiple anomalies preceding the earthquakes occurred in Japan from 2021 to 2023. Key parameters, including induced magnetic field (IMF), atmospheric electric field (AEF), and total electron content (TEC), were analyzed to explore their spatiotemporal relationships with seismic activity. The IMF, AEF, and TEC anomalies were found to consistently appear a few days or hours preceding the earthquakes, following a clear temporal sequence. By integrating multi-source data and excluding external factors such as weather conditions and solar activity, this study identified strong correlations between the anomalies and seismic events. Spatially, these anomalies were concentrated near the seismogenic zones. Additionally, a linear relationship was observed between the electric parameters derived from IMF and AEF anomalies. The lithosphere-coversphere-atmosphere-ionosphere (LCAI) coupling driven by pressure-stimulated rock current (PSRC) is supposed essentially possible. The LCAI coupling explains how tectonic stress triggers the migration of positive hole (p-hole) charges from the lithosphere to ground surface, thereby influencing both the atmosphere and ionosphere. The geological conditions of the study area, which is rich in igneous rocks such as granite and andesite embodied with massive peroxy bonds, lead to the generation of PSRC. By comparing the timing differences between the anomalies, a deeper understanding of how pre-seismic electric and magnetic signals evolve and interact across different geospheres was proposed, which could be referenced for earthquake prediction especially in this particular region.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004320","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Multitaper Magnitude-Squared Coherence for Time Series With Missing Data: Understanding Oscillatory Processes Traced by Multiple Observables","authors":"Sarah E. Dodson-Robinson,&nbsp;Charlotte Haley","doi":"10.1029/2025EA004256","DOIUrl":"https://doi.org/10.1029/2025EA004256","url":null,"abstract":"<p>To explore the hypothesis of a common source of variability in two time series, observers may estimate the magnitude-squared coherence (MSC), which is a frequency-domain view of the cross correlation. For time series that do not have uniform observing cadence, MSC can be estimated using Welch's overlapping segment averaging. However, multitaper has superior statistical properties to Welch's method in terms of the tradeoff between bias, variance, and bandwidth. The classical multitaper technique has recently been extended to accommodate time series with underlying uniform observing cadence from which some observations are missing. This situation is common for solar and geomagnetic data sets, which may have gaps due to breaks in satellite coverage, instrument downtime, or poor observing conditions. We demonstrate the scientific use of missing-data multitaper magnitude-squared coherence by detecting known solar mid-term oscillations in simultaneous, missing-data time series of solar Lyman <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>α</mi>\u0000 </mrow>\u0000 <annotation> $alpha $</annotation>\u0000 </semantics></math> flux and geomagnetic Disturbance Storm Time index. Due to their superior statistical properties, we recommend that multitaper methods be used for all heliospheric time series with underlying uniform observing cadence.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004256","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Method to Improve the Equatorial Components of Effective Angular Momentum Forecasts Based on Real Environments","authors":"Wei Miao,&nbsp;Xueqing Xu,&nbsp;Yonghong Zhou,&nbsp;Cancan Xu","doi":"10.1029/2024EA004174","DOIUrl":"https://doi.org/10.1029/2024EA004174","url":null,"abstract":"<p>The findings from the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) suggest that integrating effective angular momentum (EAM) is vital for enhancing the accuracy of polar motion (PM) predictions. Building on Dill et al. (2021), https://doi.org/10.1029/2021ea002070, who identified systematic errors in the motion terms of atmospheric angular momentum (AAM), we discovered additional systematic errors in mass and motion terms of forecasts for the AAM, oceanic angular momentum (OAM) and hydrologic angular momentum (HAM), and promptly implemented corrections following their updates. During the hindcast experiment period from 20/5/2021 to 31/12/2023, and in the first three days in the <i>X</i> and <i>Y</i> directions, the proposed method showed advantages over the Helmholtz-Centre Potsdam—German Research Centre for Geosciences (GFZ) and Eidgenössische Technische Hochschule Zürich (ETH Zürich). Then, the corrected EAM forecasts and reference values were used in the PM forecasting experiments, which showed no improvement, likely due to the existing PM forecasting system and overcompensation. However, when employing the perfect analysis of GEAM (the difference between geodesic angular momentum (GAM) and EAM), the proposed method herein achieves a maximum improvement of 30.0% and 55.5% in the <i>X</i> direction and 8.4% and 48.4% in the <i>Y</i> direction, compared to the official EAM forecasts of GFZ and ETH Zürich. This fully demonstrates that improvements in both EAM and GEAM are essential for enhancing PM predictions, and the EAM forecasts corrected by the proposed method are more competitive than those of GFZ and ETH Zürich.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004174","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144179364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Assessing Clouds in GFDL's AM4.0 With Different Microphysical Parameterizations Using the Satellite Simulator Package COSP","authors":"Huan Guo,&nbsp;Levi G. Silvers,&nbsp;David Paynter,&nbsp;Wenhao Dong,&nbsp;Songmiao Fan,&nbsp;Xianwen Jing,&nbsp;Ryan Kramer,&nbsp;Kristopher Rand,&nbsp;Kentaroh Suzuki,&nbsp;Yuying Zhang,&nbsp;Ming Zhao","doi":"10.1029/2024EA004053","DOIUrl":"https://doi.org/10.1029/2024EA004053","url":null,"abstract":"<p>We evaluate cloud simulations using satellite simulators against multiple observational data sets. These simulators have been run within the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4.0 (AM4.0), as well as an alternative configuration where a fully two-moment Morrison-Gettelman cloud microphysical parameterization with prognostic precipitation (MG2) is applied, denoted as AM4-MG2. The modeled cloud spatial distributions, vertical profiles, phase partitioning, cloud-to-precipitation transitions, and radiative effects compare reasonably well with satellite observations. Model biases include the under-prediction of total and low-level clouds, especially optically thin/intermediate clouds with cloud optical depth of less than 23, but the over-prediction of thick clouds, indicating “too few, too bright” biases. These biases counteract each other, and give rise to reasonable estimates of cloud radiative effects. The underestimate of low-level clouds is associated with too early and too frequent drizzle/precipitation formation. The precipitation bias is improved in AM4-MG2, where the autoconversion scheme initiates the precipitation more realistically. There also exist discrepancies between models and observations for midlevel and high-level clouds. Additional biases include the underestimate of liquid cloud fraction and the overestimate of ice cloud fraction.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Analyzing the Inversion Performance of a Permanent Urban Column GHG Network: An OSSE Perspective","authors":"Jun Zhang,&nbsp;Jia Chen,&nbsp;Kai Wu,&nbsp;Haoyue Tang","doi":"10.1029/2024EA004175","DOIUrl":"https://doi.org/10.1029/2024EA004175","url":null,"abstract":"<p>Observations of atmospheric columns offer an effective approach to monitoring greenhouse gas (GHG) emissions, as they are less sensitive to the dynamics of atmospheric transport in comparison to in situ measurements. MUCCnet, the world's first permanent urban ground-based column network, has been utilized as an innovative method for measuring column GHGs. We present here an observing system simulation experiment framework to characterize the behavior of this unique network in estimating urban CO₂ emissions. An assumed in situ tower-based network (AISTnet) is performed to improve our understanding of MUCCnet's observing performance. We conduct a set of Bayesian atmospheric inversions to validate the current network deployment strategy and analyze its sensitivity to large point sources (LPSs). From our base inversions, we found overall good consistency between MUCCnet and AISTnet inversions, with nearly all grid cells showing corrections in the same direction during the inversions. While the sensitivities of in situ CO₂ synthetic observations are approximately an order of magnitude higher than those of column measurements, the column measurements have the advantage of broader coverage. This leads to larger uncertainty reduction around the site locations in the AISTnet inversions, while the MUCCnet inversions present larger values over the area away from the network. The inaccurate information of the LPSs provided in the prior estimate can adversely impact the estimated emissions. Our results suggest that MUCCnet is less sensitive to LPSs errors compared to AISTnet. The findings of this work can contribute valuable insights for advancing future observing strategies in an urban environment.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004175","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144140377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Multi-Spectral Field Study of Planetary Analog Material in Extreme Environments—Alteration Products of Volcanic Deposits of Vulcano/Italy","authors":"K. Stephan,&nbsp;K. Rammelkamp,&nbsp;M. Baqué,&nbsp;S. Schröder,&nbsp;A. Pisello,&nbsp;K. Gwinner,&nbsp;G. Ortenzi,&nbsp;P. Irmisch,&nbsp;F. Sohl,&nbsp;V. Unnithan","doi":"10.1029/2024EA004036","DOIUrl":"https://doi.org/10.1029/2024EA004036","url":null,"abstract":"<p>The potential of multi-spectral investigations for planetary exploration strongly depends on the specific geologic environment and related science questions. In this work, we used a visible-near infrared spectrometer, a laser-induced breakdown spectroscopy (LIBS) instrument, and a Raman spectrometer for studying acid alteration of volcanic deposits in the field as an analog for what can be potentially observed on Mars. These deposits were studied on Vulcano, one of the Aeolian Islands/Italy, where volcanic deposits are affected by active hydrothermal alteration processes and fumarolic activity. The results show that VIS-NIR spectroscopy is sufficient to identify the major minerals formed through the alteration process. This is the only technique that can identify and characterize hydrated silica, the major alteration residue, whose spectral properties vary depending on environmental conditions and the formation process. However, only LIBS spectra allow a detailed insight into the geochemistry of the pristine volcanic deposits, which is needed to define the starting point of the alteration process. LIBS also indicated the existence of chemical elements for which no corresponding mineral could be identified in the VIS-NIR data, presumably since their spectral signature is masked by strongly absorbing species. These minerals, however, could be confirmed in the Raman spectra—nicely completing the achieved results and highlighting the high potential of the sensor suite for our study.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144140378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Retrospective Mapping of Global Snow and Ice Cover Beyond the Satellite Observational Era","authors":"Kingsley K. Kumah,&nbsp;Omid Zandi,&nbsp;Ali Behrangi","doi":"10.1029/2024EA004171","DOIUrl":"https://doi.org/10.1029/2024EA004171","url":null,"abstract":"<p>Monitoring Earth's snow and ice cover is essential for diverse applications, including climate studies, hydrological forecasting, and precipitation mapping. This study develops and evaluates methodologies to extend the Global Multisensor Automated Snow and Ice Mapping System (GMASI) records prior to its July 1987 inception, reconstructing high-resolution global snow and ice cover data. Using ERA5 reanalysis variables, three machine learning (ML) approaches—ML-E (ML with ERA5 predictors only), ML-EC (ML with ERA5 and Climatology-based predictors), and ML-ECC (ML with ERA5 predictors, Climatology-based predictors, and additional Consistency Checks)—were tested alongside climatological and fractional cover-based methods. Validation against GMASI (1988–1991) shows that ML-EC and ML-ECC achieve superior alignment, with the latter offering marginal accuracy gains. Both methods demonstrated stable daily estimates, with mean percentage biases for snow and ice cover remaining below 3% during validation. Their high accuracy is further reflected in probabilities of detection (POD) exceeding 97% across key surface types. Across all methods, there was a general tendency to underestimate snow-free areas and overestimate snow-covered regions in the Northern Hemisphere, while classification challenges in the Southern Hemisphere were more pronounced over snow-free land and Antarctic sea ice. The ML-EC approach was subsequently applied to extend the GMASI record back to 1980, capturing seasonal and interannual variability consistent with GMASI-era trends. These results underscore the potential of ML techniques to extend snow and ice cover records as far back as the beginning of the reanalysis era (1940–present), providing invaluable insights for climate analysis and operational applications.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Global Distribution of Low Frequency Family Marsquakes From Deep-Learning-Based Polarization Estimation","authors":"Quanhong Li,&nbsp;Zhuowei Xiao,&nbsp;Jinlai Hao,&nbsp;Juan Li","doi":"10.1029/2025EA004303","DOIUrl":"https://doi.org/10.1029/2025EA004303","url":null,"abstract":"<p>The seismometer has recorded thousands of marsquakes. Accurately locating these events is crucial for understanding Mars' internal structure and geological evolution. With only a single station, determining the location, especially the accurate back-azimuth, is more challenging than on Earth. Deep learning, being data-driven, can learn patterns of complex noise that are difficult for traditional methods to model, making it promising for improving back-azimuth estimation of marsquakes. However, challenges arise when applying deep learning to estimate marsquake polarization due to the limited quantity and low signal-to-noise ratios (SNR) of the data. In this study, we trained deep learning models for learning the noise patterns preceding marsquakes to address these challenges. By combining the proposed Sliding Window Inference and Featured-Training (SWIFT) to handle the high uncertainty in P phase picking, we are able to estimate polarizations of low frequency family marsquakes with improved accuracy. As a result, we have further improved the localization of marsquakes by relocating 56 events, including seven Quality C events with epicentral distances over 90°. For two Martian impact events with ground-truth locations, S1000a and S1094b, our deviations are only ∼5.65° and ∼2.72°. Our results reveal a new identified clustered seismicity zone around compressional structures in Hesperia Planum, including seven marsquakes with magnitudes from 2.7 to 3.6. Marsquakes are also widely distributed along the northern lowlands, the dichotomy boundary, and the higher-latitude southern highlands, suggesting a globally distributed pattern. Our renewed marsquake locations provide new insights into the tectonic interpretation of marsquakes.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Collaborative and Reproducible Planetary Science Through the Europlanet GMAP JupyterHub Processing Environment","authors":"G. Nodjoumi,&nbsp;C. H. Brandt,&nbsp;J. E. Suárez-Valencia,&nbsp;E. Luzzi,&nbsp;M. Valiante,&nbsp;A. P. Rossi","doi":"10.1029/2025EA004251","DOIUrl":"https://doi.org/10.1029/2025EA004251","url":null,"abstract":"<p>JupyterHub is an open-source system enabling multiple users to access individual computational environments. This facilitates collaborative development and execution of Jupyter notebooks, Python scripts, and other tools among researchers and educators through a unified interface. Through the integration of container technologies, including Docker, JupyterHub achieves seamless scalability for numerous users while maintaining efficient computational resource management. This flexible approach is especially useful in specialized areas like planetary data science, which requires robust and reproducible workflows to manage large volumes of mission data. The Europlanet Geologic MApping of Planetary surfaces (GMAP) project employs a Docker-based JupyterHub deployment to centralize essential data processing tools, such as the Integrated Software for Imagers and Spectrometers (ISIS) and the NASA Ames Stereo Pipeline (ASP). These open-source tools facilitate tasks ranging from image calibration and map projection to stereogrammetry and 3D modeling. The deployment of these elements within Docker containers facilitates simplified installation and consistent performance across disparate hardware configurations. The use of pre-configured image formats within ISIS, ASP, and other GIS and Python libraries allows planetary scientists to efficiently process raw data into analytical products, including Digital Terrain Models. Additionally, JupyterHub's architecture enables secure collaboration via authentication methods (e.g., OAuth, GitHub), with concurrent provision for private and shared data directories. This integrated framework promotes reproducible research by streamlining the sharing of scripts, notebooks, and workflows. The GMAP JupyterHub platform significantly accelerates scientific discovery through the reduction of technical barriers, the promotion of standardization, and the provision of global access to planetary data science resources.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004251","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}