基于Sentinel-1 DInSAR的北美和欧亚大陆形变反演:大数据方法、处理方法和挑战

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2023-08-10 DOI:10.1080/07038992.2023.2247095
Sergey V. Samsonov, Wanpeng Feng
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引用次数: 0

摘要

加拿大遥感中心开发了一套利用DInSAR处理技术逐帧测量长期地面变形时间序列和变形率的全自动处理系统。利用现有的22万多张Sentinel-1图像,计算了2017年至2023年北美和欧亚大陆大片地区的地面变形率,并分析了开发的处理系统的性能和不足。在这里,我们提出了处理方法和几个例子的变形率地图和时间序列产生了这个自动化系统。例子包括加拿大两个地区缓慢移动的深层滑坡的变形,俄罗斯北极地区的Komsomolskoe油田的下沉,哈萨克斯坦的Tengiz油田,伊朗西北部的多个大型沉降区和滑坡,以及黄河三角洲和中国新疆的两个大型沉降区。在这些变形率图中观察到的许多变形过程,包括大型滑坡,以前是研究界所不知道的。在多个地区观测到系统雷达侵彻深度变化,并对1个欧亚地区进行了详细研究。北美和欧亚大陆的计算变形率可供研究界使用,并可从数据存储库下载。
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Deformation Retrievals for North America and Eurasia from Sentinel-1 DInSAR: Big Data Approach, Processing Methodology and Challenges
A fully automated processing system for measuring long-term ground deformation time series and deformation rates frame-by-frame using DInSAR processing technique was developed at the Canada Center for Remote Sensing. Ground deformation rates from 2017 to 2023 were computed over a large territory of North America and Eurasia from more than 220,000 readily available Sentinel-1 images, and the performance and shortcomings of the developed processing system were analyzed. Here, we present the processing methodology and several examples of deformation rate maps and time series produced with this automated system. Examples include the deformation of slow- moving deep-seated landslides in two regions of Canada, subsidence at the Komsomolskoe oil field in the Russian Arctic, the Tengiz oil field in Kazakhstan, multiple large subsiding regions and landslides in northwestern Iran, and two large subsiding regions in the Yellow River Delta and Xinjiang, China. Many deformation processes observed in these deformation rate maps, including large landslides, have previously been unknown to the research community. Systematic radar penetration depth changes were observed in multiple regions and were investigate in detail for 1 Eurasian region. Computed deformation rates for North America and Eurasia are available to the research community and can be downloaded from the data repository.
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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