GEO OSE Track 1: Enhancing the accessibility of novel geostatistical inversion workflows for cryosphere research

GEO OSE 轨道 1：增强冰冻圈研究的新型地质统计反演工作流程的可访问性

• 批准号: 2324092
• 负责人: Emma MacKie
• 金额: $ 25.39万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2324092&HistoricalAwards=false
• 关键词: GEO; OSE; Track; Enhancing; accessibility

Many Earth science data sets require spatial interpolation in order to be used in scientific studies. It is often important that these interpolations are done in a way that satisfies certain physical and statistical properties. These “geostatistical” methods are widely used in the mining and petroleum industries, but there is no free software that makes these methods available to students, academics, and environmental researchers. This project addresses this accessibility barrier by creating freely available geostatistics software. This software will include novel methods for combining physical and statistical information. These algorithms will be fast enough so that they can be used for very large data sets. To make it easy for other researchers in different disciplines to use these tools, the software will be installed in several different online scientific computing hubs. The software will be accompanied by online educational materials. The software will be used to create robust maps of the topography underneath Antarctic glaciers by combining a variety of different measurement sources. These topographic maps will help improve the rigor of ice-sheet and sea level rise models.This work will build upon the existing GStatSim Python geostatistics software and educational Jupyter Book to perform high-speed physics-informed geostatistical simulations. The research team will develop a novel Markov Chain Monte Carlo approach for performing geophysical inversions where geostatistical simulations are iteratively perturbed until the outputs of a geophysical forward model converge with measurements. Parallel processing techniques will be used to improve the scalability of this method. This will enable users to generate ensembles of large-scale geostatistical simulations at high speeds while accounting for both spatial and physical constraints. This method will be tested and applied to two case studies: 1) the simulation of sub-ice-shelf topography using gravity observations, and 2) the simulation of subglacial topography with mass conservation constraints. These case studies will provide critical parameters for ice-sheet models. To facilitate the use of GStatSim in the cryosphere community, these tools will be hosted on the GHub and CryoCloud computing platforms. This software will also be linked to the Earth Science Information Partners toolbox page in order to make this package accessible to broader geosciences audiences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

许多地球科学数据集需要空间插值才能用于科学研究。通常，重要的是要以满足某些物理和统计特性的方式进行这些插值。这些“地统计”方法广泛用于采矿和石油行业，但是没有免费软件可以使这些方法可供学生，学者和环境研究人员使用。该项目通过创建可免费获得的地列表软件来解决此可访问性障碍。该软件将包括结合物理和统计信息的新方法。这些算法将足够快，因此可以用于非常大的数据集。为了使其他学科的其他研究人员轻松使用这些工具，该软件将安装在几个不同的在线科学计算中心中。该软件将通过在线教育材料来完成。该软件将用于通过组合各种不同的测量源来创建在南极冰川下方的地形图强大的地图。这些地形地图将有助于改善冰原和海平面上升模型的严格性。这项工作将基于现有的GSTATSIM Python GeoStatistics软件和教育式Jupyter书，以执行高速物理学的地理模拟。研究团队将开发一种新型的马尔可夫链蒙特卡洛方法，用于执行地球物理倒置，在该方法上进行地理仿真迭代地扰动，直到地球物理前向模型的输出与测量相融合为止。并行处理技术将用于提高该方法的可扩展性。这将使用户能够高速生成大规模地统计模拟的合奏，同时考虑空间和物理约束。该方法将经过测试并应用于两种案例研究：1）使用重力观测来模拟亚丝架形状的模拟，以及2）模拟具有质量保护约束的冰川地形地形。这些案例研究将为冰盖模型提供关键参数。为了促进在Cryosphere社区中GSTATSIM的使用，这些工​​具将托管在GHUB和Croclod Computing平台上。该软件还将链接到地球科学信息合作伙伴工具箱页面，以使该软件包可以访问更广泛的地球科学受众。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子优点和更广泛的影响来评估标准，认为通过评估来获得支持。