Digital Environment: Dynamic Ground Motion Map of the UK

数字环境：英国动态地面运动地图

• 批准号: NE/S016104/1
• 负责人: Juliet Biggs
• 金额: $ 21.71万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FS016104%2F1
• 关键词: Digital; Environment; Dynamic; Ground; Motion

The ground surface in the UK is far from stable. For example, there are more that 15,000 recorded landslides in the UK, and the average annual cost of ground movement to the insurance industry is £250M. Landslides affecting critical infrastructure, such as mainline railways or dams, can be associated with multi-million pound remediation costs even for a single slope failure event. Furthermore, there are tens of thousands of kilometres of engineered slopes in our transportation, utilities and flood defence infrastructure networks - many of which were built in Victorian times and are in poor condition. Satellite technology, specifically ESA's Sentinel-1 constellation, has the potential to produce a dynamic, high resolution map of ground motion which can be used for monitoring and planning. The proposed feasibility study will explore whether UK expertise can be used to integrate Sentinel-1 data with sensors on the ground and embedded in the built environment to contribute to the Digital Environment. The study will leverage existing RCUK investments, map the requirements of potential stakeholders and explore cutting edge approaches to data handling, analysis, fusion and decision making. In addition to the core of InSAR experts, our team comprises a) specialists in image processing and machine learning, b) specialists in landslides, subsidence and onshore energy production and c) two knowledge exchange fellows. A wide-ranging network of potential stakeholders has already been identified, and our selected project partners (Environment Agency, Network Rail, TerraDat, Bridgeway Consulting) represent the needs of key governmental and commercial beneficiaries. The output of the feasibility study will be a peer-reviewed white paper detailing the requirements for a Sentinel-1 based UK ground motion map to be incorporated into a Digital Environment.

英国的地面远非稳定。例如，英国有15,000个记录的山体滑坡，而保险业的平均地面运动成本为2.5亿英镑。影响关键基础设施（例如主线铁路或大坝）的滑坡也可能与数百万英镑的补救成本有关，即使在一次坡度故障事件中。此外，我们的运输，公用事业和防洪基础设施网络中有成千上万公里的工程插槽 - 其中许多是在维多利亚时代建造的，状况较差。卫星技术，尤其是ESA的Sentinel-1星座，有可能生成动态的高分辨率地面运动图，可用于监视和计划。拟议的可行性研究将探讨是否可以使用英国专业知识将Sentinel-1数据与地面传感器集成并嵌入建筑环境中以贡献数字环境。该研究将利用现有的RCUK投资，绘制潜在利益相关者的要求，并探讨用于数据处理，分析，融合和决策的最前沿方法。除了INSAR专家的核心外，我们的团队还包括a）图像处理和机器学习的专家，b）山体滑坡，沉降和陆上能源生产的专家，以及c）两个知识交换伙伴。已经确定了一个潜在利益相关者的大型网络，我们选定的项目合作伙伴（环境机构，网络铁路，Terradat，Bridgeway Consulting）代表了关键的政府和商业善良的需求。可行性研究的输出将是一份经过同行评审的白皮书，详细介绍了基于Sentinel-1的英国地面运动图的要求，该图将纳入数字环境中。

项目成果

Benchmarking and inter-comparison of Sentinel-1 InSAR velocities and time series

• DOI: 10.1016/j.rse.2021.112306
• 发表时间: 2021-01-30
• 期刊: REMOTE SENSING OF ENVIRONMENT
• 影响因子: 13.5
• 作者: Sadeghi, Z.;Wright, T. J.;Biggs, J.
• 通讯作者: Biggs, J.

Blind Source Separation for MT-InSAR Analysis With Structural Health Monitoring Applications

用于结构健康监测应用的 MT-InSAR 分析的盲源分离

• DOI: 10.1109/jstars.2022.3190027
• 发表时间: 2022
• 期刊: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• 影响因子: 5.5
• 作者: Martin G

Detecting ground deformation in the built environment using sparse satellite InSAR data with a convolutional neural network

• DOI: 10.1109/tgrs.2020.3018315
• 发表时间: 2020-01-01
• 期刊: IEEE Trans Geosci. Remote Sens
• 作者: Anantrasirichai, N.

Time-Series Prediction Approaches to Forecasting Deformation in Sentinel-1 InSAR Data

• DOI: 10.1029/2020jb020176
• 发表时间: 2021-03-01
• 期刊: JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
• 影响因子: 3.9
• 作者: Hill, P.;Biggs, J.;Bull, D.
• 通讯作者: Bull, D.

Ground and Satellite-Based Methods of Measuring Deformation at a UK Landslide Observatory: Comparison and Integration

• DOI: 10.3390/rs14122836
• 发表时间: 2022-06
• 期刊: Remote. Sens.
• 作者: K. Kelevitz;A. Novellino;A. Watlet;J. Boyd;J. Whiteley;J. Chambers;C. Jordan;T. Wright;A. Hooper;J. Biggs