The Belt and Road global navigation satellite system(B&R GNSS)network is the first large-scale deployment of Chinese GNSS equipment in a seismic system.Prior to this,there have been few systematic assessments of t...The Belt and Road global navigation satellite system(B&R GNSS)network is the first large-scale deployment of Chinese GNSS equipment in a seismic system.Prior to this,there have been few systematic assessments of the data quality of Chinese GNSS equipment.In this study,data from four representative GNSS sites in different regions of China were analyzed using the G-Nut/Anubis software package.Four main indicators(data integrity rate,data validity ratio,multi-path error,and cycle slip ratio)used to systematically analyze data quality,while evaluating the seismic monitoring capabilities of the network based on earthquake magnitudes estimated from high-frequency GNSS data are evaluated by estimating magnitude based on highfrequency GNSS data.The results indicate that the quality of the data produced by the three types of Chinese receivers used in the network meets the needs of earthquake monitoring and the new seismic industry standards,which provide a reference for the selection of equipment for future new projects.After the B&R GNSS network was established,the seismic monitoring capability for earthquakes with magnitudes greater than M_(W)6.5 in most parts of the Sichuan-Yunnan region improved by approximately 20%.In key areas such as the Sichuan-Yunnan Rhomboid Block,the monitoring capability increased by more than 25%,which has greatly improved the effectiveness of regional comprehensive earthquake management.展开更多
【目的】围绕AI场景下科学数据的共享与利用问题,针对现有FAIR原则不足以指导科学数据满足AI就绪的现状,构建面向AI就绪的科学数据共享与利用原则框架。【方法】通过系统梳理传统机器学习、大模型预训练、大模型微调、检索增强生成及智...【目的】围绕AI场景下科学数据的共享与利用问题,针对现有FAIR原则不足以指导科学数据满足AI就绪的现状,构建面向AI就绪的科学数据共享与利用原则框架。【方法】通过系统梳理传统机器学习、大模型预训练、大模型微调、检索增强生成及智能体等5类典型AI任务的数据需求,在传统FAIR“四可”维度的基础上,提出面向AI就绪(即For AI Ready)的科学数据共享与利用原则框架FAIR×FAIR,进而提出与框架相适应的层次化技术栈。【结果】FAIR×FAIR框架明确了13项科学数据满足AI就绪的技术要求,为弥合AI任务与科学数据之间的语义鸿沟提供了系统化方案。【局限】本研究提出的原则框架其实施效果仍需通过后续领域应用案例进一步验证。【结论】FAIR×FAIR框架为AI时代的科学数据共享与高效利用提供了理论依据和实践路径,对推动数据驱动型科研范式的演进具有重要意义。展开更多
The seismological observation system in China has experienced rapid development over the Tenth Five-year Plan period. China Earthquake Administration (CEA) has completed the establishment of China digital seismologica...The seismological observation system in China has experienced rapid development over the Tenth Five-year Plan period. China Earthquake Administration (CEA) has completed the establishment of China digital seismological observation network. CEA has accomplished analog-to-digital conversion of the existing seismological observation systems and set up a number of new digital seismic stations. This indicates full digitization of seismological ob-servation in China. This paper presents an overview of the scale,layout principle and major functions of the up-dated national digital seismograph network,regional digital seismograph network,and digital seismograph net-work for volcano monitoring and mobile digital seismograph networks in China.展开更多
2021年7月发射的风云三号E星(FY-3E)是世界首颗民用晨昏轨道气象卫星,其搭载的WindRAD双频测风雷达具有全球海面风场探测能力。本文首先基于FY-3E/WindRAD L1级观测资料,研究了雷达海面后向散射和风场之间的非线性关系,分别建立了适用于...2021年7月发射的风云三号E星(FY-3E)是世界首颗民用晨昏轨道气象卫星,其搭载的WindRAD双频测风雷达具有全球海面风场探测能力。本文首先基于FY-3E/WindRAD L1级观测资料,研究了雷达海面后向散射和风场之间的非线性关系,分别建立了适用于C和Ku波段VV/HH极化的地球物理模式函数(GMF)。随后,结合最大似然估计法(MLE)对WindRAD散射计探测资料进行风场反演。利用海洋浮标、中法海洋卫星散射计(CSCAT)和美国国家环境预报中心(NCEP)模式风场资料对WindRAD反演风场进行验证。结果显示:WindRAD反演风速与浮标风速偏差约为0.2 m s^(-1),均方根误差(RMSE)在1.13~1.44 m s^(-1)之间,优于2 m s^(-1)的业务化应用的风速精度要求;两者风向偏差在1.4°~3.0°之间,RMSE在25.3°~30.1°之间。WindRAD和CSCAT风场具有较好的一致性,风速RMSE在1.37~1.6 m s^(-1)之间,风向RMSE在22.9°~25.9°之间。WindRAD和NCEP模式风速RMSE在1.87~2.23 m s^(-1)之间,风向RMSE在22.4°~27.1°之间。研究表明WindRAD散射计C和Ku波段VV/HH极化反演风场均具有较高的精度,充分显示了WindRAD载荷在全球海面风场探测方面的应用潜力和价值。展开更多
基金supported by grants from the National Natural Science Foundation of China(No.42004010)the B&R Seismic Monitoring Network Project of the China Earthquake Networks Center(No.5007).
文摘The Belt and Road global navigation satellite system(B&R GNSS)network is the first large-scale deployment of Chinese GNSS equipment in a seismic system.Prior to this,there have been few systematic assessments of the data quality of Chinese GNSS equipment.In this study,data from four representative GNSS sites in different regions of China were analyzed using the G-Nut/Anubis software package.Four main indicators(data integrity rate,data validity ratio,multi-path error,and cycle slip ratio)used to systematically analyze data quality,while evaluating the seismic monitoring capabilities of the network based on earthquake magnitudes estimated from high-frequency GNSS data are evaluated by estimating magnitude based on highfrequency GNSS data.The results indicate that the quality of the data produced by the three types of Chinese receivers used in the network meets the needs of earthquake monitoring and the new seismic industry standards,which provide a reference for the selection of equipment for future new projects.After the B&R GNSS network was established,the seismic monitoring capability for earthquakes with magnitudes greater than M_(W)6.5 in most parts of the Sichuan-Yunnan region improved by approximately 20%.In key areas such as the Sichuan-Yunnan Rhomboid Block,the monitoring capability increased by more than 25%,which has greatly improved the effectiveness of regional comprehensive earthquake management.
文摘【目的】围绕AI场景下科学数据的共享与利用问题,针对现有FAIR原则不足以指导科学数据满足AI就绪的现状,构建面向AI就绪的科学数据共享与利用原则框架。【方法】通过系统梳理传统机器学习、大模型预训练、大模型微调、检索增强生成及智能体等5类典型AI任务的数据需求,在传统FAIR“四可”维度的基础上,提出面向AI就绪(即For AI Ready)的科学数据共享与利用原则框架FAIR×FAIR,进而提出与框架相适应的层次化技术栈。【结果】FAIR×FAIR框架明确了13项科学数据满足AI就绪的技术要求,为弥合AI任务与科学数据之间的语义鸿沟提供了系统化方案。【局限】本研究提出的原则框架其实施效果仍需通过后续领域应用案例进一步验证。【结论】FAIR×FAIR框架为AI时代的科学数据共享与高效利用提供了理论依据和实践路径,对推动数据驱动型科研范式的演进具有重要意义。
文摘The seismological observation system in China has experienced rapid development over the Tenth Five-year Plan period. China Earthquake Administration (CEA) has completed the establishment of China digital seismological observation network. CEA has accomplished analog-to-digital conversion of the existing seismological observation systems and set up a number of new digital seismic stations. This indicates full digitization of seismological ob-servation in China. This paper presents an overview of the scale,layout principle and major functions of the up-dated national digital seismograph network,regional digital seismograph network,and digital seismograph net-work for volcano monitoring and mobile digital seismograph networks in China.
文摘2021年7月发射的风云三号E星(FY-3E)是世界首颗民用晨昏轨道气象卫星,其搭载的WindRAD双频测风雷达具有全球海面风场探测能力。本文首先基于FY-3E/WindRAD L1级观测资料,研究了雷达海面后向散射和风场之间的非线性关系,分别建立了适用于C和Ku波段VV/HH极化的地球物理模式函数(GMF)。随后,结合最大似然估计法(MLE)对WindRAD散射计探测资料进行风场反演。利用海洋浮标、中法海洋卫星散射计(CSCAT)和美国国家环境预报中心(NCEP)模式风场资料对WindRAD反演风场进行验证。结果显示:WindRAD反演风速与浮标风速偏差约为0.2 m s^(-1),均方根误差(RMSE)在1.13~1.44 m s^(-1)之间,优于2 m s^(-1)的业务化应用的风速精度要求;两者风向偏差在1.4°~3.0°之间,RMSE在25.3°~30.1°之间。WindRAD和CSCAT风场具有较好的一致性,风速RMSE在1.37~1.6 m s^(-1)之间,风向RMSE在22.9°~25.9°之间。WindRAD和NCEP模式风速RMSE在1.87~2.23 m s^(-1)之间,风向RMSE在22.4°~27.1°之间。研究表明WindRAD散射计C和Ku波段VV/HH极化反演风场均具有较高的精度,充分显示了WindRAD载荷在全球海面风场探测方面的应用潜力和价值。
