Displacement updates in dynamic areas

动态区域的位移更新

• 批准号: 543428-2019
• 负责人: Cheng, LinOiIrene
• 金额: $ 9.06万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=720710
• 关键词: Displacement; updates; dynamic; areas

Geological displacements can be caused by natural or man-made events, e.g., earthquakes, volcanic erruptions and mining activities. Interferometric Synthetic Aperture Radar (InSAR) technology, based on the analysis of microwave signals reflected back from the earth surface, is able to compute ground displacements with millimeter precision. In the past, researchers have applied computer vision, signal processing and other traditional algorithms to analyze InSAR. Our industrial partner 3vGeomatics Inc's optimization algorithm PtSel outperforms related work. However, the rapidly increasing volume of InSAR data has overloaded the processing pipeline. The amount of undesirable noise has also increased and affected the accuracy of displacement computation. Since July 2017, 3vG has been working with the University of Alberta (UA) team. We have introduced novel deep learning techniques to significantly reduce the amount of data required, while preserving similar accuracy. The goal of this CRD proposal is knowledge translation and operationalization by generalizing our findings on displacement updates in dynamic areas. Our three-year research plan aims to preserve high accuracy using reduced number of images (reducing from 12 to 6 to 3 to 1 month of data). Extreme weather conditions have caused more frequent occurrences of geological changes. Early prediction of ground displacements can help in preparation before disasters happen and thus save lives. The economic and technological impacts not only benefit Canada, but also the global community, and will strengthen Canada's leadership in the monitoring of geological activities.

地质位移可以由自然或人为事件引起，例如，地震、火山喷发和采矿活动。干涉合成孔径雷达（干涉合成孔径雷达）技术是基于对地面反射微波信号的分析，能够计算毫米级精度的地面位移。过去，研究人员应用计算机视觉、信号处理等传统算法对干涉合成孔径雷达进行分析。我们的工业合作伙伴3vGeomatics Inc的优化算法PtSel优于相关工作。然而，干涉合成孔径雷达数据量的迅速增加已经使处理管道超负荷。不需要的噪声量也增加了，影响了位移计算的准确性。自2017年7月以来，3vG一直与阿尔伯塔大学（UA）团队合作。我们引入了新的深度学习技术，以显著减少所需的数据量，同时保持类似的准确性。这个CRD提案的目标是通过概括我们在动态领域的位移更新的研究结果来实现知识转化和操作化。我们的三年研究计划旨在使用减少的图像数量（从12到6减少到3到1个月的数据）来保持高精度。极端的气候条件导致地质变化更加频繁地发生。对地面位移的早期预测有助于在灾害发生前做好准备，从而挽救生命。经济和技术影响不仅使加拿大受益，而且使全球社会受益，并将加强加拿大在监测地质活动方面的领导地位。