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

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，可能会造成社会破坏、财务后果和可能的生命损失。这不仅需要在与社会相关的时间框架内对这种过程及其危害进行长期估计，而且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地球表面随时间变化和变形的能力。例如，重要的是要知道地震矩在圣安德烈亚斯断层系统上累积的速度有多快，因为这将告诉我们将在何时何地期待下一次破坏性地震。这就要求我们能够以0.5毫米/年的精度测量跨越数百公里的形变，分辨率优于10公里。干涉合成孔径雷达(干涉合成孔径雷达)是这项关键任务的最佳技术，因为目前为这项技术提供信息的遥感卫星观测具有覆盖范围广、成本低、不受天气影响和定期进行的特点。然而，即将到来的新的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正在开发强有力和有效的方法，以充分利用卫星产生的数据用于科学研究和社会应用。该项目的主要创新之处在于启用云计算能力，向新一代编程语言过渡，并不断吸引更多的用户使用该软件构建自己的数据处理策略。开发人员将确保全球各地的用户获得获得最先进处理技术所需的支持，并将继续改进文件、样本数据集和教程，以加强空间大地测量领域的教育基础。干涉合成孔径雷达(InSAR)是一种有效的测量地表微小位移(1-10 cm)的技术，包括由构造载荷、地震、火山、滑坡、冰川、地下流体注入/释放和地下核试验引起的位移。在过去的十年里，已经开发了一种免费可用的开源软件来利用这些宝贵的数据集，这被称为GMTSAR。在过去的调查中，该软件配备了利用欧洲航天局运营的哨兵一号卫星每年免费提供的~1200 TB数据的能力，并作为一个强大的研究工具提供给用户基础。即将到来的由NASA和ISRO运营的Nisar任务将极大地将可用SAR数据量增加到每年超过3万TB。虽然这对InSAR科学是一个福音，但它提出了两个主要的处理障碍：如何在保持测量精度的同时，利用这些不断增加的大型数据集实现最大生产力，以及如何最好地促进广泛用户访问这些数据宝库。该项目通过以下方式解决这些挑战：(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以简化用户交互，进一步扩大这些InSAR数据在研究和学生社区的使用。通过使用增强的GMTSAR软件简化对大型InSAR数据集的处理，将使固体地球和冰冻圈科学家能够利用海量InSAR数据集来推进他们的跨学科研究，包括全球火山观测、通过应变率绘图估计地震危险、监测城市基础设施、跟踪冰盖移动和探测海岸沉陷。简化的处理模块将促进干涉合成孔径雷达科学的全球覆盖，专家和学生都可以使用这些模块。此外，我们建议对特定模块进行的改进，包括开发与全球导航卫星系统(GNSS)数据的常规集成，以及结合右视哨兵1号和左视Nisar卫星的视线(LOS)InSAR测量，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势和更广泛的影响审查标准进行评估，被认为值得支持。

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.