Determination of the Fennoscandian Land Uplift and Mass Variations in Northern Europe from GRACE Data

根据 GRACE 数据确定北欧芬诺斯坎迪亚土地隆起和质量变化

• 批准号: 30062430
• 负责人: Professor Dr.-Ing. Jürgen Müller
• 金额: --
• 依托单位: Institut für Erdmessung
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2006
• 资助国家: 德国
• 起止时间: 2005-12-31 至 2008-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/30062430?language=en
• 关键词: Determination; Fennoscandian; Land; Uplift; Mass

Gravity field variations as derived from the monthly GRACE solutions result from the integral effect of mass variations in the atmosphere, hydrosphere and geosphere. These effects include oceanic, atmospheric and hydrological mass movements and those caused by dynamics in the Earth¿s interior. In addition, residual signals from insufficient pre-processing may be present. The main objective of this project is to extract mass variations in Northern Europe (and Canada) from the GRACE data, especially those which are related to the Fennoscandian land uplift resp. glacial isostatic adjustment (GIA). One key issue is the separation of the various signal parts and the reduction of the observed (or derived) quantities by applying dedicated filter (e.g. Gaussian or non-isotropic) and analysis techniques (e.g. pattern recognition). The relevant time scales (e.g. annual, semi-annual, secular) for each process will be analysed in a consistent way. Besides filtering of the various signal parts, the quality assessment of the individual measurements and the reduction models are of special relevance, in order to determine the interrelations and the corresponding accuracies. The uplift results will then be compared resp. combined with terrestrial data. Finally, the developed methods shall be transferred to other regions (e.g. permafrost regions) and the results shall be generalised.

GRACE解的重力场月变化是大气圈、水圈和地圈质量变化综合作用的结果。这些影响包括海洋、大气和水文质量运动以及地球内部动力学造成的影响。此外，可能存在来自不充分预处理的残留信号。该项目的主要目标是从GRACE数据中提取北方欧洲（和加拿大）的质量变化，特别是与芬诺斯堪的纳维亚陆地隆起有关的质量变化。冰川均衡调整（GIA）。一个关键问题是通过应用专用滤波器（例如高斯或非各向同性）和分析技术（例如模式识别）来分离各种信号部分和减少观察到的（或导出的）量。将以一致的方式分析每一进程的相关时间尺度（例如，年度、半年期、长期）。除了对各个信号部分进行滤波之外，对各个测量值和简化模型的质量评估也是特别重要的，以便确定相互关系和相应的精度。然后将隆起结果分别进行比较。结合地面数据。最后，应将开发的方法转移到其他地区（如永久冻土地区），并推广结果。