Tordesillas Antoinette, Kahagalage Sanath, Campbell Lachlan, Bellett Pat, Intrieri Emanuele, Batterham Robin
School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.
GroundProbe, Orica, Australia.
Sci Rep. 2021 May 6;11(1):9729. doi: 10.1038/s41598-021-88836-x.
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.
粒状土坡即将发生的灾难性破坏在空间和时间上表现出独特的运动学模式。虽然边坡破坏灾害的风险评估通常依赖于对地面运动的监测，但这种前兆破坏模式仍知之甚少。一个关键挑战是时空尺度和动力学机制的多样性。特别是，存在一种前兆破坏机制，其中两种中尺度机制共同演化，即力和损伤的优先传播路径。尽管进行了广泛的研究，但一种不仅能在实验室测试中，而且能在大型、不受控制的野外环境中处理它们共同演化的公式却难以捉摸。在这里，我们通过开发一个边坡稳定性分析框架来解决这个问题，该框架使用网络流理论和中观科学来模拟这种共同演化，并仅从地表地面运动数据预测出现的运动学聚类。我们在四个数据集上测试了这个框架：一个是在实验室规模上使用单个颗粒位移数据；三个是在野外规模上使用边坡表面的视线位移，分别来自两个矿山的地基雷达和2017年新磨滑坡的星载雷达。运动学聚类的动力学能够对破坏的几何形状、位置和时间进行早期预测。
