Collaborative Research: Elements: Monitoring Earth Surface Deformation with the Next Generation of InSAR Satellites: GMTSAR

合作研究：要素：利用下一代 InSAR 卫星监测地球表面形变：GMTSAR

基本信息

批准号：
2209807
负责人：
Dunyu Liu
金额：
$ 23.14万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209807&HistoricalAwards=false
关键词：
Collaborative Research Elements Monitoring Earth

项目摘要

Understanding the processes of earthquakes, volcanoes and hydrological changes, and their associated hazards is a top priority of the solid earth research community and USGS, due to the potential for societal disruption, financial consequences, and possible loss of life if not prepared for adequately. This requires not only long-term estimates of such processes and their hazards within a socially relevant timeframe, but also an evaluation on the impact of human activities over the Earth’s surface and interior. These estimates and evaluations hinge on the capability of accurately measuring how the Earth’s surface changes and deforms over time. For example, it is important to know how fast the seismic moment is accumulating over the San Andreas fault system, as that will tell us where and when will we be expecting the next destructive earthquake. This requires us to be able to measure the deformation that spans hundreds of kilometers at an accuracy of 0.5 mm/yr with resolution better than 10 km. Interferometric Synthetic Aperture Radar (InSAR) is the best technique for this crucial task, as the current remote sensing satellite observations that inform this technique come with broad-scale coverage, at low-cost, regardless of weather and on a regular basis. However, the upcoming new InSAR missions are raising a new challenge: how to efficiently handle drastically increasing amounts of data (~80 TB per day for the NISAR mission). To answer this challenge, the freely-available InSAR processing software GMTSAR is developing robust and efficient approaches to take full advantage of the satellite-generated data for both scientific research and societal applications. The main innovations of this project are to enable the cloud computing capabilities, transfer to newer generation of programing language, and keep engaging more users to build their own data processing strategies using this software. The developers will ensure that users from across the globe have the support they need for access to state-of-the-art processing techniques, and will continue improving the documentation, example datasets and tutorials to strengthen the foundation for education in the field of space geodesy. Interferometric Synthetic Aperture Radar (InSAR) is a powerful technique for measuring small displacements (1-10 cm) of the surface of the earth including those caused by tectonic loading, earthquakes, volcanoes, landslides, glaciers, ground fluid injection/withdrawal and underground nuclear tests. Over the past decade, a freely available, open-source software has been developed to harness these valuable datasets, which is called GMTSAR. During past investigations, this software has been equipped with the power to capitalize on the freely available ~1200 TB per year of data from Sentinel-1 satellite operated by the European Space Agency, and was provided as a robust research tool to the user base. The upcoming NISAR mission operated by NASA and ISRO, will dramatically increase the amount of available SAR data to over 30,000 TBytes per year. While this is a boon for InSAR science, it presents two main processing hurdles: how can one achieve maximum productivity with these increasing large datasets, while still preserving the accuracy of the measurements and how can one best facilitate broad user access to this trove of data. This project addresses these challenges by (1) enabling GMTSAR to permit rapid processing of very large data sets in a cloud computing environment, and (2) further expanding the usage of these InSAR data in both research and student communities by integrating with Python and streamlining processing modules to simplify user interactions.Facilitating the processing of large InSAR datasets with enhanced GMTSAR software will allow solid earth and cryosphere scientists to utilize the massive InSAR data sets to advance their interdisciplinary investigations, including global observations of volcanoes, estimates of seismic hazard through strain-rate mapping, monitoring urban infrastructure, tracking ice sheet movements, and detecting coastal subsidence. The global reach of InSAR science will be advanced by streamlined processing modules that are accessible to both specialists and students alike. In addition, the improvements we propose to make to specific modules, including the development of routine integration with Global Navigation Satellite System (GNSS) data and the combination of line-of-sight (LOS) InSAR measurements from both right-looking Sentinel-1 and left-looking NISAR satellites, will improve the accuracy of measurements to enable full 3D vector displacement time series analyses.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.

了解地震、火山和水文变化的过程及其相关危害是固体地球研究界和美国地质勘探局的首要任务，因为如果没有做好充分准备，可能会造成社会混乱、经济后果，甚至可能造成人员伤亡。这不仅需要在社会相关的时间范围内对此类过程及其危害进行长期估计，还需要对人类活动对地球表面和内部的影响进行评估。这些估计和评估取决于准确测量这些过程的能力。地球表面的变化和例如，了解圣安德烈亚斯断层系统上地震矩累积的速度非常重要，因为这将告诉我们预计下一次破坏性地震将在何时何地发生。以 0.5 毫米/年的精度测量跨越数百公里的变形，分辨率优于 10 公里，因为当前的遥感技术是完成这项关键任务的最佳技术。为这项技术提供信息的卫星观测具有广泛的覆盖范围、低成本、不受天气影响并且定期进行。然而，即将到来的新 InSAR 任务提出了一个新的挑战：如何有效地处理急剧增加的数据量。 NISAR 任务每天约 80 TB），为了应对这一挑战，免费提供的 InSAR 处理软件 GMTSAR 正在开发强大而有效的方法，以充分利用卫星生成的数据进行科学研究和社会应用。本项目的创新点旨在启用云计算功能，转移到新一代编程语言，并继续吸引更多用户使用该软件构建自己的数据处理策略，开发人员将确保来自全球的用户获得访问所需的支持。最先进的处理技术，并将继续改进文档、示例数据集和教程，以加强空间大地测量学领域的教育基础。干涉合成孔径雷达（InSAR）是测量小位移的强大技术。地球表面（1-10 厘米）的范围，包括由构造载荷、地震、火山、山体滑坡、冰川、地面流体注入/抽取和地下核试验引起的情况。在过去的十年中，这是一款免费提供的开源软件。 GMTSAR 是为了利用这些有价值的数据集而开发的，在过去的调查中，该软件能够利用由 Sentinel-1 卫星运行的每年约 1200 TB 的免费数据。由欧洲航天局提供，并作为强大的研究工具提供给用户群，即将由 NASA 和 ISRO 运营的 NISAR 任务将大大增加可用的 SAR 数据量，每年超过 30,000 TBytes。对于 InSAR 科学来说，它提出了两个主要的处理障碍：如何利用这些不断增加的大型数据集实现最大生产力，同时仍然保持测量的准确性，以及如何最好地促进广大用户访问这一数据宝库。通过 (1) 使 GMTSAR 能够在云计算环境中快速处理非常大的数据集，以及 (2) 通过与 Python 集成并简化处理模块，进一步扩大这些 InSAR 数据在研究和学生社区中的使用，以应对这些挑战。简化用户交互。通过增强的 GMTSAR 软件促进大型 InSAR 数据集的处理，将使固体地球和冰冻圈科学家能够利用大量 InSAR 数据集来推进他们的跨学科研究，包括全球火山观测、通过地震危险性估计应变率测绘、监测城市基础设施、跟踪冰盖运动和检测海岸沉降将通过专家和学生都可以使用的简化处理模块来推进 InSAR 科学的全球影响力。此外，我们还提出了改进建议。特定模块的开发，包括开发与全球导航卫星系统（GNSS）数据的常规集成以及右视 Sentinel-1 和左视 NISAR 卫星的视距（LOS）InSAR 测量的组合，将提高测量的准确性，以实现完整的 3D 矢量位移时间序列分析。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。