PREEVENTS Track 2: Collaborative Research: Defining precursors of ground failure: a multiscale framework for early landslide prediction through geomechanics and remote sensing

预防事件轨道 2：协作研究：定义地面破坏的前兆：通过地质力学和遥感进行早期滑坡预测的多尺度框架

• 批准号: 1854977
• 负责人: Karen Daniels
• 金额: $ 36.23万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1854977&HistoricalAwards=false
• 关键词: PREEVENTS; Track; Collaborative; Research; Defining

Population growth, urban expansion, and extreme weather are contributing more than ever to hazard vulnerability. Among the hazards driven by weather patterns, ground deformation due to landslides has immense global impacts. Large portions of the Earth's surface are at risk to ground failures, affecting a considerable fraction of the world's population. Landslides cause a global annual death toll of several thousand and financial losses of more than $1B per year in the United States alone. The most formidable challenge in predicting ground failures derives from their ability to suddenly accelerate despite the lack of observed precursors. In fact, natural slopes can deform in multiple ways, sometimes by displaying slow movements, while at other times moving rapidly in a fluidized state. Such modes of deformation coexist at the same site, affect portions of terrain proximal to one another, and may be experienced by the same hillslopes at different times. The current scarcity of predictive large-scale ground deformation models is largely a consequence of the poor spatial coverage of ground-based monitoring data. To overcome these obstacles, this project will rely on technological advances in remote sensing that allow the detection of rainfall patterns and ground movements at spatiotemporal resolutions that were unthinkable just a decade ago. These observational advances have the potential to unleash new formulation, calibration and validation possibilities for hydrologic and ground deformation models by means of abundant, openly-available, spatially-distributed information. Specifically, the project is motivated by the idea that pre-failure deformations have much to say about when, how, and why ground failure occurs, and aims to demonstrate that analyzing the deformation signature of the ground is the key to explain why hillslopes fail in different ways when subjected to variable weather patterns. If successful, this project will lead to new ways to decode the physical origin of ground instability and define measurable precursors of catastrophic landslide triggering, thus potentially inspiring the design of innovative real-time early warning systems able to better protect human life and infrastructure. During the course of this project, the interaction between the Earth's surface and the atmosphere will be studied from a new multi-disciplinary perspective. Specifically, the project will formulate: (1) rheological laws for geomaterials able to explain variations in landslide velocity resulting from dynamically changing environmental conditions; (2) a multiscale weather-hydrology simulation platform able to quantify spatially heterogeneous rainfall inputs and soil moisture at the scale of mountain ranges; (3) landscape-scale geomechanical models able to reproduce the evolution of remotely sensed deformations via force-transfer laws between proximal portions of terrain; (4) a network theory for surface processes based on the physics of complex systems, by which patterns can be identified and precursors of runaway instability defined. Such a combination of methods will provide a comprehensive representation of landslide dynamics, thus improving our ability to forecast landslides and mitigate hazards at the landscape scale. Most importantly, it will provide innovative tools to address open questions in the domain of hazard forecasting, such as: (i) Can we use landscape-scale observations to infer the rheology of hillslopes? (ii) Which landscape-scale measurements are most useful for predicting the fate of incipient landslides? (iii) Is the concurrent collection of spatially-distributed data of rainfall patterns and displacement rates sufficient to identify landslide precursors? These questions will be answered by combining data from state-of-the-art remote sensing tools (e.g., satellite and airborne interferometric synthetic aperture radar, high-resolution digital elevation models, and weather radar) with physics-based constitutive laws for soil and rock deformation, atmospheric-hydrologic models, and complex network theories. Rich datasets available for a variety of geological settings and earthen materials within and outside the United States will be used to test the predictive capabilities of the proposed approaches. This strategy will offer unique opportunities to validate the concepts at the core of the project and test their applicability to a wide range of geomorphic and climatic contexts.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.

人口增长、城市扩张和极端天气比以往任何时候都更容易造成灾害脆弱性。在由天气模式驱动的灾害中，山体滑坡引起的地面变形具有巨大的全球影响。地球表面的大部分地区都面临着地面故障的风险，影响到世界上相当一部分人口。滑坡每年造成全球数千人死亡，仅在美国每年就造成超过10亿美元的经济损失。预测地面破坏最艰巨的挑战来自于它们在缺乏观测前兆的情况下突然加速的能力。事实上，自然斜坡可以以多种方式变形，有时通过显示缓慢的运动，而在其他时间以流化状态快速移动。这种变形模式在同一地点共存，影响彼此邻近的地形部分，并且可能在不同时间由同一山坡经历。目前缺乏预测性的大规模地面变形模型，主要是地面监测数据的空间覆盖面差的结果。为了克服这些障碍，该项目将依靠遥感技术的进步，从而能够以时空分辨率探测降雨模式和地面运动，这在十年前是不可想象的。这些观测进展有可能通过丰富的、开放的、空间分布的信息，为水文和地面变形模型释放新的公式、校准和验证可能性。具体而言，该项目的动机是，破坏前变形有很多关于何时，如何以及为什么发生地面破坏的想法，并旨在证明分析地面的变形特征是解释为什么山坡在不同的天气模式下以不同的方式失败的关键。如果成功，该项目将导致新的方法来解码地面不稳定的物理根源，并确定灾难性山体滑坡触发的可测量的前兆，从而可能激发设计能够更好地保护人类生命和基础设施的创新实时预警系统。在该项目实施过程中，将从新的多学科角度研究地球表面与大气层之间的相互作用。具体而言，该项目将制定：（1）地质材料的流变学规律，能够解释动态变化的环境条件导致的滑坡速度变化;（2）多尺度天气水文模拟平台，能够量化山脉尺度上空间不均匀的降雨量和土壤湿度;（3）能够通过地形近端部分之间的力传递定律再现遥感变形演变的大尺度地质力学模型;（4）以复杂系统物理学为基础的表面过程网络理论，通过该理论可以识别模式并定义失控不稳定性的前兆。这种方法的组合将提供一个全面的滑坡动力学的代表性，从而提高我们的能力，预测滑坡和减轻灾害的景观尺度。最重要的是，它将提供创新的工具，以解决悬而未决的问题，在灾害预测领域，如：（一）我们可以使用的尺度观测来推断山坡的流变？(ii)哪一种尺度的测量方法对预测早期滑坡的命运最有用？(iii)同时收集降雨模式和位移速率的空间分布数据是否足以识别滑坡前兆？这些问题将通过结合最先进的遥感工具（例如，卫星和机载干涉合成孔径雷达、高分辨率数字高程模型和气象雷达），以及基于物理的土壤和岩石变形本构律、大气水文模型和复杂网络理论。丰富的数据集可用于各种地质环境和土材料在美国境内和境外将被用来测试所提出的方法的预测能力。这一战略将提供独特的机会来验证该项目的核心概念，并测试其在广泛的地貌和气候背景下的适用性。这一奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。