Geoscience Data Journal最新文献_第6页

Deep Quake Dynamics: A Multimodal Fault-Aware Approach to Earthquake Magnitude and Occurrence Time Forecasting 深震动力学：地震震级和发生时间预测的多模态故障感知方法

IF 2.4 3区地球科学

Geoscience Data Journal Pub Date : 2026-03-16 DOI: 10.1002/gdj3.70066

Kiymet Kaya, Murat Sahin, Furkan Mangir, Alp Baris Beydemir, Cenk Yaltirak, Sule Gunduz Oguducu

{"title":"Deep Quake Dynamics: A Multimodal Fault-Aware Approach to Earthquake Magnitude and Occurrence Time Forecasting","authors":"Kiymet Kaya, Murat Sahin, Furkan Mangir, Alp Baris Beydemir, Cenk Yaltirak, Sule Gunduz Oguducu","doi":"10.1002/gdj3.70066","DOIUrl":"https://doi.org/10.1002/gdj3.70066","url":null,"abstract":"Earthquake (EQ) forecasting is critical for early warning systems. We propose an EQ Magnitude-Time (EQMT) Forecasting Model that integrates real-world inter-fault relations, multiple data sources, and advanced modelling techniques to forecast EQ magnitudes and classify occurrence times. The model converts fault lines into graph representations and extracts node embeddings to capture spatial inter-fault dependencies. We evaluated EQMT on seismic events in the northern Aegean region, where the North Anatolian Fault intersects the Aegean extensional system. The proposed model attains a mean absolute error of 0.33 for magnitude forecasting and an F1 score of 81.39% for occurrence-time classification. Furthermore, to enhance the interpretability of both magnitude and occurrence time forecasting tasks, we applied SHapley Additive exPlanations (SHAP)-based explainable Artificial Intelligence (XAI) analyses, which provide insights into the contribution of individual seismic and fault-related features to the model's forecast. SHAP-based explainability analysis reveals that observation-duration features and graph embeddings are the primary contributors to model predictions. These results demonstrate the potential of fault-aware, graph-based approaches for operational EQ forecasting.","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566095","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}

引用次数: 0

A Hydrogeophysical Survey Utilising the sTEM and sTEMprofiler Systems: Transient Electromagnetic Data and Resistivity Models 利用sTEM和sTEMprofiler系统的水文地球物理调查：瞬变电磁数据和电阻率模型

IF 2.4 3区地球科学

Geoscience Data Journal Pub Date : 2026-03-10 DOI: 10.1002/gdj3.70067

Line M. Madsen, Angelos Almpanis, Frederikke Storm, Jesper B. Pedersen

{"title":"A Hydrogeophysical Survey Utilising the sTEM and sTEMprofiler Systems: Transient Electromagnetic Data and Resistivity Models","authors":"Line M. Madsen, Angelos Almpanis, Frederikke Storm, Jesper B. Pedersen","doi":"10.1002/gdj3.70067","DOIUrl":"10.1002/gdj3.70067","url":null,"abstract":"Transient electromagnetic (TEM) methods are widely applied to characterise subsurface geological structures relevant to groundwater management. Here, we present a TEM dataset and the resulting resistivity models (available at https://doi.org/10.5281/zenodo.16894039) from a hydrogeophysical survey in northwestern Denmark aimed at mapping buried Quaternary valley systems. The data were collected with the newly developed sTEMprofiler system, a portable offset-loop TEM system and supplemented by stationary sTEM central-loop soundings for deeper penetration. The sTEMprofiler enabled rapid data acquisition of 319 soundings across 300 ha in 3 days. Data were processed and inverted using spatially constrained inversion, yielding reliable resistivity models with average data residuals within the standard deviation of the measurements, and with depths of investigation of 100–150 m for the sTEMprofiler and > 300 m for the largest central-loop configuration (depending on subsurface geology and noise conditions). The resistivity models show complex Quaternary valley infills incised into Paleogene clays, uplifted limestone and low-resistivity zones within the limestone, likely caused by residual saline porewater. In addition to the use of the resistivity models for local geological interpretation and hydrological modelling, the dataset offers considerable reuse potential. By providing raw data, system setup files, resistivity models and residuals, it enables researchers to explore a wide range of applications, including benchmarking and comparing inversion methods (deterministic, probabilistic or machine learning approaches), performing joint inversions, integrating resistivity models into geological and hydrological studies, and training or teaching sTEM data processing and inversion workflows.","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564842","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}

引用次数: 0

Inventory of Landslides Triggered by Heavy Rainfall in Fuling District, Chongqing, China, June 2019 2019年6月重庆市涪陵区强降雨引发山体滑坡情况调查

IF 2.4 3区地球科学

Geoscience Data Journal Pub Date : 2026-03-10 DOI: 10.1002/gdj3.70062

Xue Zhiwen, Xu Chong, Yawei Xie, Xuning Nie, Xin Li, Zikang Xiao, Xiwei Xu, Zhiqiang Zhang, Dengjie Zhu, Bo Gong, Hourong Zhang

{"title":"Inventory of Landslides Triggered by Heavy Rainfall in Fuling District, Chongqing, China, June 2019","authors":"Xue Zhiwen, Xu Chong, Yawei Xie, Xuning Nie, Xin Li, Zikang Xiao, Xiwei Xu, Zhiqiang Zhang, Dengjie Zhu, Bo Gong, Hourong Zhang","doi":"10.1002/gdj3.70062","DOIUrl":"https://doi.org/10.1002/gdj3.70062","url":null,"abstract":"In this study, a landslide triggered by a rainfall event in Fuling District of Chongqing was comprehensively investigated. Based on the high-resolution remote sensing satellite image data and DEM data provided by GIS and Google Earth platform, and by comparing the image differences before and after the event, the landslide database triggered by this rainfall in Fuling District was constructed. Fuling District in Chongqing covers an area of about 2944 km2, and the total number of landslides is 791, with a total area of 818,418.1 m2, the smallest landslide area of 69 m2, the largest landslide area of 12,044.8 m2 and the average landslide area of 1034.7 m2. Most landslides were categorised as small to medium-sized, with those less than 1000 m2 comprising over 70% of the total count. The landslides predominantly occurred in the northern and northeastern mountainous areas of Fuling District. This database not only provides detailed information on the location, size and distribution characteristics of landslides but also offers crucial data support for future research on rainfall-induced landslide hazards. Researchers can utilise this data to further analyse patterns of landslide occurrence, identify influencing factors, and assess disaster risks, thereby providing a scientific basis for disaster prevention and mitigation strategies. Moreover, the collection and analysis of this data can serve as a significant reference for urban planning, land use management, and disaster risk management, fostering regional sustainable development and safe construction practices.","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564872","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}

引用次数: 0

IF 2.4 3区地球科学

Geoscience Data Journal Pub Date : 2026-03-10

引用次数: 0

Inventory of Landslides Triggered by Heavy Rainfall in Fuling District, Chongqing, China, June 2019 2019年6月重庆市涪陵区强降雨引发山体滑坡情况调查

IF 2.4 3区地球科学

Geoscience Data Journal Pub Date : 2026-03-10 DOI: 10.1002/gdj3.70062

Xue Zhiwen, Xu Chong, Yawei Xie, Xuning Nie, Xin Li, Zikang Xiao, Xiwei Xu, Zhiqiang Zhang, Dengjie Zhu, Bo Gong, Hourong Zhang

{"title":"Inventory of Landslides Triggered by Heavy Rainfall in Fuling District, Chongqing, China, June 2019","authors":"Xue Zhiwen, Xu Chong, Yawei Xie, Xuning Nie, Xin Li, Zikang Xiao, Xiwei Xu, Zhiqiang Zhang, Dengjie Zhu, Bo Gong, Hourong Zhang","doi":"10.1002/gdj3.70062","DOIUrl":"10.1002/gdj3.70062","url":null,"abstract":"In this study, a landslide triggered by a rainfall event in Fuling District of Chongqing was comprehensively investigated. Based on the high-resolution remote sensing satellite image data and DEM data provided by GIS and Google Earth platform, and by comparing the image differences before and after the event, the landslide database triggered by this rainfall in Fuling District was constructed. Fuling District in Chongqing covers an area of about 2944 km2, and the total number of landslides is 791, with a total area of 818,418.1 m2, the smallest landslide area of 69 m2, the largest landslide area of 12,044.8 m2 and the average landslide area of 1034.7 m2. Most landslides were categorised as small to medium-sized, with those less than 1000 m2 comprising over 70% of the total count. The landslides predominantly occurred in the northern and northeastern mountainous areas of Fuling District. This database not only provides detailed information on the location, size and distribution characteristics of landslides but also offers crucial data support for future research on rainfall-induced landslide hazards. Researchers can utilise this data to further analyse patterns of landslide occurrence, identify influencing factors, and assess disaster risks, thereby providing a scientific basis for disaster prevention and mitigation strategies. Moreover, the collection and analysis of this data can serve as a significant reference for urban planning, land use management, and disaster risk management, fostering regional sustainable development and safe construction practices.","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564870","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}

引用次数: 0

IF 2.4 3区地球科学

Geoscience Data Journal Pub Date : 2026-03-10

引用次数: 0

A Hydrogeophysical Survey Utilising the sTEM and sTEMprofiler Systems: Transient Electromagnetic Data and Resistivity Models 利用sTEM和sTEMprofiler系统的水文地球物理调查：瞬变电磁数据和电阻率模型

IF 2.4 3区地球科学

Geoscience Data Journal Pub Date : 2026-03-10 DOI: 10.1002/gdj3.70067

Line M. Madsen, Angelos Almpanis, Frederikke Storm, Jesper B. Pedersen

{"title":"A Hydrogeophysical Survey Utilising the sTEM and sTEMprofiler Systems: Transient Electromagnetic Data and Resistivity Models","authors":"Line M. Madsen, Angelos Almpanis, Frederikke Storm, Jesper B. Pedersen","doi":"10.1002/gdj3.70067","DOIUrl":"https://doi.org/10.1002/gdj3.70067","url":null,"abstract":"Transient electromagnetic (TEM) methods are widely applied to characterise subsurface geological structures relevant to groundwater management. Here, we present a TEM dataset and the resulting resistivity models (available at https://doi.org/10.5281/zenodo.16894039) from a hydrogeophysical survey in northwestern Denmark aimed at mapping buried Quaternary valley systems. The data were collected with the newly developed sTEMprofiler system, a portable offset-loop TEM system and supplemented by stationary sTEM central-loop soundings for deeper penetration. The sTEMprofiler enabled rapid data acquisition of 319 soundings across 300 ha in 3 days. Data were processed and inverted using spatially constrained inversion, yielding reliable resistivity models with average data residuals within the standard deviation of the measurements, and with depths of investigation of 100–150 m for the sTEMprofiler and > 300 m for the largest central-loop configuration (depending on subsurface geology and noise conditions). The resistivity models show complex Quaternary valley infills incised into Paleogene clays, uplifted limestone and low-resistivity zones within the limestone, likely caused by residual saline porewater. In addition to the use of the resistivity models for local geological interpretation and hydrological modelling, the dataset offers considerable reuse potential. By providing raw data, system setup files, resistivity models and residuals, it enables researchers to explore a wide range of applications, including benchmarking and comparing inversion methods (deterministic, probabilistic or machine learning approaches), performing joint inversions, integrating resistivity models into geological and hydrological studies, and training or teaching sTEM data processing and inversion workflows.","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564719","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}

引用次数: 0

IF 2.4 3区地球科学

Geoscience Data Journal Pub Date : 2026-03-08

引用次数: 0

GloMarGridding: A Python Toolkit for Flexible Spatial Interpolation in Climate Applications glargridding：气候应用中灵活空间插值的Python工具包

IF 2.4 3区地球科学

Geoscience Data Journal Pub Date : 2026-03-08 DOI: 10.1002/gdj3.70064

Richard C. Cornes, Steven C. Chan, Archie Cable, Duo Chan, Agnieszka Faulkner, Elizabeth C. Kent, Joseph T. Siddons

{"title":"GloMarGridding: A Python Toolkit for Flexible Spatial Interpolation in Climate Applications","authors":"Richard C. Cornes, Steven C. Chan, Archie Cable, Duo Chan, Agnieszka Faulkner, Elizabeth C. Kent, Joseph T. Siddons","doi":"10.1002/gdj3.70064","DOIUrl":"https://doi.org/10.1002/gdj3.70064","url":null,"abstract":"Global surface temperature datasets are constructed through processing chains that inherently introduce structural uncertainty, arising from choices made both in the processing of input observations and in the spatial interpolation methods employed. Because these steps are often tightly integrated, it is difficult to isolate their individual contributions to uncertainty. Here, we introduce GloMarGridding, a Python package designed to support the evaluation of the component of structural uncertainty arising specifically from spatial interpolation. It provides tools to apply Gaussian Process Regression Modelling (GPRM), widely used in the production of global temperature datasets, enabling the generation of spatially complete temperature fields from grid-box average and point observations, along with estimation of uncertainty in those fields. GloMarGridding currently supports three spatial covariance parametrizations: fixed isotropic variograms, ellipse-based anisotropic, and empirically derived covariance matrices. It also allows for uncertainty propagation via error covariance matrices and conditional simulation from input ensembles. By decoupling spatial interpolation from earlier stages of dataset development—such as homogenization, quality control, and aggregation—this framework enables independent assessment of upstream processing choices and their impacts on gridded outputs.","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564070","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}

引用次数: 0

GloMarGridding: A Python Toolkit for Flexible Spatial Interpolation in Climate Applications glargridding：气候应用中灵活空间插值的Python工具包

IF 2.4 3区地球科学

Geoscience Data Journal Pub Date : 2026-03-08 DOI: 10.1002/gdj3.70064

Richard C. Cornes, Steven C. Chan, Archie Cable, Duo Chan, Agnieszka Faulkner, Elizabeth C. Kent, Joseph T. Siddons

{"title":"GloMarGridding: A Python Toolkit for Flexible Spatial Interpolation in Climate Applications","authors":"Richard C. Cornes, Steven C. Chan, Archie Cable, Duo Chan, Agnieszka Faulkner, Elizabeth C. Kent, Joseph T. Siddons","doi":"10.1002/gdj3.70064","DOIUrl":"10.1002/gdj3.70064","url":null,"abstract":"Global surface temperature datasets are constructed through processing chains that inherently introduce structural uncertainty, arising from choices made both in the processing of input observations and in the spatial interpolation methods employed. Because these steps are often tightly integrated, it is difficult to isolate their individual contributions to uncertainty. Here, we introduce GloMarGridding, a Python package designed to support the evaluation of the component of structural uncertainty arising specifically from spatial interpolation. It provides tools to apply Gaussian Process Regression Modelling (GPRM), widely used in the production of global temperature datasets, enabling the generation of spatially complete temperature fields from grid-box average and point observations, along with estimation of uncertainty in those fields. GloMarGridding currently supports three spatial covariance parametrizations: fixed isotropic variograms, ellipse-based anisotropic, and empirically derived covariance matrices. It also allows for uncertainty propagation via error covariance matrices and conditional simulation from input ensembles. By decoupling spatial interpolation from earlier stages of dataset development—such as homogenization, quality control, and aggregation—this framework enables independent assessment of upstream processing choices and their impacts on gridded outputs.","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564035","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}

引用次数: 0