用于破坏估计的时空边坡稳定性分析（SSSAFE）：将雷达数据与颗粒破坏的基本动力学联系起来
Spatiotemporal slope stability analytics for failure estimation (SSSAFE): linking radar data to the fundamental dynamics of granular failure
摘要：
粒状土边坡即将发生的灾难性破坏在空间和时间上表现出不同的运动学模式。虽然边坡失稳灾害的风险评估通常依赖于地面运动的监测，但对这种间接的失稳模式仍然知之甚少。一个关键的挑战是时空尺度和动力机制的多样性。特别是，存在着一个偶然的故障制度，两个中尺度机制共同发展，即，力和损伤的首选传输路径。尽管进行了广泛的研究，但事实证明，一种不仅可以在实验室测试中而且可以在大型不受控制的野外环境中解决它们的共同进化问题的配方是难以捉摸的。在这里，我们解决这个问题，通过开发一个边坡稳定性分析框架，它使用网络流理论和中观科学建模这种协同进化和预测紧急的运动集群只从地面运动数据。我们在四个数据集上测试了这个框架：一个是实验室规模的，使用单个颗粒位移数据;三个是现场规模的，使用坡面的视线位移，来自两个矿山的地基雷达和2017年新莫滑坡的星载雷达。运动学簇的动力学提供了对故障的几何形状、位置和时间的早期预测。
英文摘要：
Impending catastrophic failure of granular earth slopes manifests distinct kinematic patterns in space and time. While risk assessments of slope failure hazards have routinely relied on the monitoring of ground motion, such precursory failure patterns remain poorly understood. A key challenge is the multiplicity of spatiotemporal scales and dynamical regimes. In particular, there exist a precursory failure regime where two mesoscale mechanisms coevolve, namely, the preferred transmission paths for force and damage. Despite extensive studies, a formulation which can address their coevolution not just in laboratory tests but also in large, uncontrolled field environments has proved elusive. Here we address this problem by developing a slope stability analytics framework which uses network flow theory and mesoscience to model this coevolution and predict emergent kinematic clusters solely from surface ground motion data. We test this framework on four data sets: one at the laboratory scale using individual grain displacement data; three at the field scale using line-of-sight displacement of a slope surface, from ground-based radar in two mines and from space-borne radar for the 2017 Xinmo landslide. The dynamics of the kinematic clusters deliver an early prediction of the geometry, location and time of failure.
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