  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.
