SBIR Phase I: Autonomous Interferometric Synthetic Aperture Radar (InSAR) for surface deformation monitoring

SBIR 第一阶段：用于表面变形监测的自主干涉合成孔径雷达 (InSAR)

• 批准号: 2213289
• 负责人: Claudia Hulbert
• 金额: $ 25.47万
• 依托单位: GEOLABE LLC
• 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: 2023-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213289&HistoricalAwards=false
• 关键词: SBIR; Phase; Autonomous; Interferometric; Synthetic

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to enable the global autonomous detection of surface deformation. Measuring Earth surface deformation is fundamental to detect and analyze surface and subsurface changes due to anthropogenic activity, with a myriad of industrial applications that includes the monitoring of oil and gas extraction fields and storage reservoirs, mining operations, carbon dioxide sequestration, and/or infrastructure integrity. Illustrating the economic and social impact of its uses, the market for analyzing Interferometric Synthetic Aperture Radar (InSAR) data is expected to double within 5 years. Beyond the dramatic economic growth of InSAR, its far-ranging applications have broad social and scientific impacts, in particular related to natural hazards and climate change. Advances in InSAR processing and improved signal-to-noise ratios will translate into improved monitoring of earthquake activity, landslides, water supplies, deforestation, floods, ice sheets, etc.This Small Business Innovation Research (SBIR) Phase I project aims at tackling the lack of automation in InSAR processing and improving detection thresholds in InSAR time series analysis. While the technique can potentially measure millimeter-scale changes in deformation over periods of days to years, atmospheric effects can wreak havoc on repeat-pass InSAR interpretation by introducing errors that may mask small surface deformations. These effects, which are fundamentally due to pressure, temperature and relative humidity variations in the troposphere, can lead to errors that are larger than most of the deformation signals of interest. Current algorithms are not suited for automated, large-scale monitoring without a priori data because they require time-consuming manual intervention, and the final product requires exhaustive expert interpretation. Through the development of machine learning and artificial intelligence methods this project aims at: (i) further automating and accelerating the processing of InSAR time series, via the automation of some key sections of the processing pipeline that still rely on extensive and costly human intervention; and (ii) developing a new methodology to generate InSAR time series, that is robust to noise and allows for a finer temporal and spatial resolution compared to the state-of-the-art.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.

小企业创新研究 (SBIR) 第一阶段项目的更广泛影响是实现表面变形的全球自主检测。测量地球表面变形对于检测和分析人类活动引起的地表和地下变化至关重要，具有多种工业应用，包括监测石油和天然气开采领域和储层、采矿作业、二氧化碳封存和/或基础设施正直。干涉合成孔径雷达 (InSAR) 数据分析市场预计将在 5 年内翻一番，这说明了其用途的经济和社会影响。除了 InSAR 显着的经济增长之外，其广泛的应用还具有广泛的社会和科学影响，特别是与自然灾害和气候变化相关的影响。 InSAR 处理的进步和信噪比的提高将转化为对地震活动、山体滑坡、供水、森林砍伐、洪水、冰盖等的改进监测。这个小企业创新研究 (SBIR) 第一阶段项目旨在解决InSAR 处理缺乏自动化，且 InSAR 时间序列分析中的检测阈值不够完善。虽然该技术可以测量数天到数年期间毫米级的变形变化，但大气效应可能会引入可能掩盖微小表面变形的误差，从而对重复通过的 InSAR 解释造成严重破坏。这些效应基本上是由于对流层中的压力、温度和相对湿度变化造成的，可能导致比大多数感兴趣的变形信号更大的误差。目前的算法不适合在没有先验数据的情况下进行自动化、大规模监测，因为它们需要耗时的人工干预，并且最终产品需要详尽的专家解释。通过机器学习和人工智能方法的发展，该项目的目标是：（i）通过处理流程中仍然依赖广泛且昂贵的人工干预的一些关键部分的自动化，进一步实现 InSAR 时间序列处理的自动化和加速； (ii) 开发一种新的方法来生成 InSAR 时间序列，该方法对噪声具有鲁棒性，并且与最先进的技术相比，可以提供更精细的时间和空间分辨率。该奖项反映了 NSF 的法定使命，并被认为是值得的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来获得支持。