Spatiotemporal slope stability analytics for failure estimation (SSSAFE): linking radar data to the fundamental dynamics of granular failure

TL;DR

This paper introduces a novel spatiotemporal analytics framework that leverages network flow theory and mesoscience to predict slope failure by analyzing ground motion data across multiple scales, improving early failure detection.

Contribution

It develops a new analytical approach linking radar data to the fundamental dynamics of granular failure, addressing coevolving mesoscale mechanisms in slope failure prediction.

Findings

Successfully predicts failure geometry, location, and timing.

Validated on laboratory and field data, including satellite radar.

Provides early warning capabilities for slope failures.

Abstract

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.