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：
2209808
负责人：
David Sandwell
金额：
$ 15.49万
依托单位：
University of California-San Diego Scripps Inst of Oceanography
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=2209808&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，火山和氢变化的过程以及它们相关的危害是固体地球研究界和USGS的重中之重，因为如果没有充分准备，社会破坏，财务后果和可能的生命丧失可能会造成。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的长期估计，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如何随着时间变化和变形的能力。例如，重要的是要知道地震时刻在圣安德烈亚斯断层系统上积累了多快，这将告诉我们我们在哪里以及何时期待下一次破坏性地震。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，分辨率高于10 km。干涉合成孔径雷达（INSAR）是这项关键任务的最佳技术，因为当前的遥感卫星观测值将这项技术告知该技术，无论天气和定期天气如何，都以较低的范围覆盖。但是，即将到来的新内在任务正在提出一个新的挑战：如何有效地处理大量数据（Nisar Mission每天约80 TB）。为了应对这一挑战，自由利用的内在处理软件GMTSAR正在开发强大而有效的方法，以充分利用科学研究和社交应用的卫星生成的数据。该项目的主要创新是启用云计算功能，转移到新产生的编程语言，并继续吸引更多用户使用此软件来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最先进处理技术所需的支持，并将继续改进文档，示例数据集和教程，以增强在太空土生领域的教育基础。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较小位移（1-10厘米），包括由构造载荷，地震，火山，山地滑坡，冰川，地下流体/戒断和地下核测试引起的。在过去的十年中，已经开发了一种免费的可用的开源软件来利用这些有价值的数据集，这称为GMTSAR。在过去的调查中，该软件与欧洲航天局经营的Sentinel-1卫星数据的免费可用性相同，可以利用〜1200 TB的数据，并作为用户群提供了强大的研究工具。 NASA和ISRO执行的即将到来的Nisar任务将显着将可用的SAR数据数量增加到每年超过30,000个TBYTE。尽管这是Insar科学的福音，但它提出了两个主要的处理障碍：如何通过这些增加的大型数据集实现最大的生产力，同时仍然保留了测量的准确性，以及如何最好地促进广泛的用户访问此数据的数据。该项目通过（1）通过（1）使GMTSAR允许在云计算环境中快速处理非常大的数据集，并且（2）进一步扩展了研究和学生社区中这些InsAR数据的使用，通过与Python进行集成，并通过整合Python，并简化了与较大的用户相互作用，从而使GRADISS稳固地进行了整个软件。利用大量的内在数据集来推进其跨学科投资，包括全球对火山的观察，通过应变率图估计地震危害，监测城市基础设施，跟踪冰片运动以及检测沿海的沉积。 InSAR科学的全球覆盖范围将通过简化专家和学生都可以访问的处理模块来提高。此外，我们提出的改进是为特定模块进行的，包括与全球导航卫星系统（GNSS）数据的日常整合以及从右现在的Nisar-1和剩下的Nisar卫星均可提高相对的统计信息的准确性，从右前哨1和左前哨卫星中均可提高3DDD vector的准确性。使命，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准通过评估值得支持的。