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，因为如果没有充分准备，可能会造成社会破坏、经济后果和可能的生命损失。这不仅需要在与社会有关的时间范围内对这些过程及其危害进行长期估计，而且还需要评估人类活动对地球表面和内部的影响。这些估计和评估取决于精确测量地球表面如何随时间变化和变形的能力。例如，知道地震力矩在圣安德烈亚斯断层系统上积累的速度有多快是很重要的，因为这将告诉我们下一次破坏性地震发生的地点和时间。这要求我们能够以0.5毫米/年的精度测量数百公里的变形，分辨率优于10公里。干涉合成孔径雷达（InSAR）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大范围、低成本、不受天气影响和定期覆盖的特点。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。该项目的主要创新是启用云计算功能，向新一代编程语言转移，并不断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所需的支持，以获得最先进的处理技术，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）是一种强大的技术，用于测量地球表面的小位移（1-10厘米），包括由构造荷载、地震、火山、滑坡、冰川、地面流体注入/提取和地下核试验引起的位移。在过去的十年里，人们开发了一个免费的开源软件来利用这些有价值的数据集，这个软件被称为GMTSAR。在过去的调查中，该软件已经配备了利用由欧洲空间局操作的哨兵1号卫星每年免费提供的约1200 TB数据的能力，并作为用户基础的强大研究工具提供。即将到来的NISAR任务将由NASA和ISRO运营，将大大增加可用SAR数据量，每年超过30,000 tb。虽然这对InSAR科学来说是一个福音，但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的生产力，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些数据宝库。该项目通过(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中的使用。利用增强的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数据集推进他们的跨学科研究，包括全球火山观测、通过应变率测绘估计地震危险、监测城市基础设施、跟踪冰盖运动和探测沿海沉降。InSAR科学的全球覆盖范围将通过专家和学生都可以使用的简化处理模块来推进。此外，我们建议对特定模块进行改进，包括开发与全球导航卫星系统（GNSS）数据的常规集成，以及来自右侧Sentinel-1和左侧NISAR卫星的视线（LOS） InSAR测量的组合，这将提高测量的准确性，从而实现完整的3D矢量位移时间序列分析。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.