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

预防措施：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: 1854975
• 负责人: Alexander Handwerger
• 金额: $ 26.06万
• 依托单位: Middlebury College
• 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 2020-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1854975&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造成危害脆弱性。在天气模式驱动的危害中，由于滑坡引起的地面变形具有巨大的全球影响。地球表面的很大一部分面临着地面故障的风险，影响了世界人口的相当一部分。滑坡造成全球年度死亡人数数千人，仅在美国，每年的财务损失超过$ 1B。尽管缺乏观察到的前体，但预测地面故障的最巨大挑战是由于它们突然加速的能力。实际上，自然斜率可以通过多种方式变形，有时通过显示缓慢的运动，而在其他时候则以流化状态迅速移动。这种变形模式在同一地点共存，影响彼此近端的地形部分，并且在不同时间的同一山坡可能会经历。当前预测性大规模地面变形模型的稀缺性在很大程度上是地面监测数据空间覆盖率不佳的结果。为了克服这些障碍，该项目将依赖于遥感的技术进步，这些技术允许在十年前在时空分辨率下检测降雨模式和地面运动。这些观察性进步有可能通过丰富的，公开的，可空间分布的信息来释放水文和地面变形模型的新公式，校准和验证可能性。具体而言，该项目的动机是由于预付费变形的何时，如何以及为何发生地面故障有太多要说的要说，并旨在证明分析地面的变形签名是解释为什么山坡失败的关键。遭受可变天气模式时的不同方式。如果成功的话，该项目将导致解码地面不稳定性的物理起源的新方法，并定义灾难性的滑坡触发的可测量前体，从而有可能激发创新的实时预警系统的设计，能够更好地保护人类的生活和基础设施。在这个项目的过程中，将从新的多学科角度研究地球表面与大气之间的相互作用。具体而言，该项目将制定：（1）能够解释由动态变化的环境条件引起的滑坡速度变化的土地材料的流变定律； （2）一个多尺度的天气流水学模拟平台，能够在山脉的规模上量化空间异质的降雨投入和土壤水分； （3）景观规模的地质力学模型能够通过近端地形部分之间的力转移定律来重现远程感知的变形的演变； （4）基于复杂系统物理的表面过程的网络理论，可以通过该理论确定模式并定义了失控的不稳定性的前体。这种方法的组合将提供对滑坡动态的全面表示，从而提高了我们在景观规模上预测滑坡和减轻危害的能力。最重要的是，它将提供创新的工具来解决危险预测领域的开放问题，例如：（i）我们可以使用景观规模的观察来推断山坡的流变学吗？ （ii）哪些景观尺度测量最有用，可预测初期滑坡的命运？ （iii）同时收集了降雨模式和流离失所率的空间分布数据，足以识别滑坡前体？这些问题将通过将最先进的遥感工具的数据（例如，卫星和空气中的干涉合成孔径，高分辨率数字高程模型和天气雷达）与物理学的构成法和土壤和土壤的构成法相结合（例如，卫星和空气中的干涉量合成孔径，高分辨率的数字高程模型和天气雷达）来回答这些问题。岩石变形，大气流质模型和复杂的网络理论。可用于美国内部和外部各种地质环境和泥土材料的丰富数据集将用于测试拟议方法的预测能力。该策略将提供独特的机会来验证项目核心的概念，并在广泛的地貌和气候环境中测试其适用性。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子的评估来支持的。和更广泛的影响审查标准。