Hazard monitoring from Space: Next generation InSAR time series analysis

雷达干涉测量（InSAR）是一种从太空提供地表位移测量的技术，可能达到毫米级的精度。这些测量在自然灾害界用于地震分析和监测火山和滑坡，以及监测人为活动，如石油和天然气开采以及地下水储存的减少。图1所示。2017年3月厄瓜多尔加拉帕戈斯群岛Cerro Azul火山爆发前48小时的干涉图（Bagnardi和Hooper， 2018）每条彩色条纹对应2.8厘米朝向或远离卫星的位移。随着岩浆从地下抽出并注入到该地区南部，导致那里的隆起，Cerro Azul下面的地区正在下沉。单个“干涉图”（图1）提供了两个图像采集日期之间的地表位移图，但也包括由于大气中不同的传播延迟、地表散射特性的变化和数据处理问题而产生的噪声项。时间序列分析技术通过将多个干涉图一起处理，在一定程度上减少了这些误差项（Hooper et al, 2012）。然而，这些技术被设计为处理10次的采集序列，而不是现代传感器可能的100次采集序列，因为它们的重访时间很短。此外，近实时的危害监测需要在不完全重新分析时间序列的情况下快速获取新图像。在这个方向上已经取得了进展（Spaans和Hooper， 2016），但挑战仍然存在。其他问题包括：1)去相关事件；InSAR只有在地面散射特性没有显著变化的情况下才能工作，因此新的建筑和耕作方式可能导致某些地区完全失去测量。2)含水率和植被变化对地面散射特性的影响（De Zan et al ., 2015）；这种影响以前被认为是随着时间的推移而平均的，但最近的研究表明，当从较短长度的干涉图构建长时间序列时，这种噪声源可以系统地累积。在这个项目中，学生将与利兹、美国国家航空航天局和SatSense有限公司的顶尖科学家合作，开发一种新的时间序列分析算法，该算法可以快速获取新的数据，在各种分辨率下工作，处理土壤湿度和植被的变化，处理去相关事件。学生将把这种新方法应用于选定的自然灾害，例如变形的火山和山体滑坡，以及SatSense有限公司提供的人为变形的案例研究。

Radar Interferometry (InSAR) is a technique that provides measurements of surface displacement from Space, potentially with millimetric accuracy. These measurements are used in the natural hazards community for earthquake analysis and monitoring of volcanoes and landslides, as well as for monitoring anthropogenic activities such as oil and gas extraction, and drawdown of underground water storage.Figure 1. Interferogram spanning the first ~48 hr of volcanic unrest in March 2017 at Cerro Azul Volcano, Gala'pagos Islands, Ecuador (Bagnardi and Hooper, 2018) Each color fringe corresponds to 2.8 cm of displacement towards or away from the satellite. The area beneath Cerro Azul is subsiding as magma is withdrawnfrom beneath and injected beneath the area to the south, causing uplift there.A single "interferogram" (Figure 1) provides a map of the surface displacement between two image acquisition dates, but also includes noise terms due to variable propagation delays through the atmosphere, changes of scattering properties of the surface, and data processing issues. Time series analysis techniques reduce these error terms to some extent by processing multiple interferograms together (Hooper et al, 2012). However, these techniques were designed to deal with sequences of 10's of acquisitions rather than the 100's of acquisitions that are possible with modern sensors, due to their short revisit times. In addition, hazard monitoring in close-to-real time requires rapid ingestion of new images without complete reanalysis of the time series. Progress in this direction has been made (Spaans and Hooper, 2016) but challenges still remain.Other issues include: 1) Decorrelation events; InSAR only works when the ground scattering properties do not change significantly, so new construction and farming practices can lead to a complete loss of measurement for some areas. 2) Changes in the scattering properties of the ground due to changes in moisture content and vegetation (De Zan et al, 2015); this effect was previously assumed to average out over time, but it has been shown recently that this noise source can accumulate systematically when long times series are built from interferograms of shorter length.In this project the student will work with leading scientists at Leeds, NASA and SatSense Ltd to develop a novel time series analysis algorithm that:Ingests new acquisitions rapidlyWorks at a variety of resolutionsHandles variation in soil moisture and vegetationHandles decorrelation eventsThe student will apply the new method to selected natural hazards, e.g deforming volcanoes and landslides, and also to a case study of anthropogenic deformation supplied by SatSense Ltd.