New Insights into the Spatiotemporal Precursory Failure Dynamics of the 2017 Xinmo Landslide and Its Surrounds

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

“…Guided by lessons learned from the physics and dynamics of granular failure, these delivered a reliable early prediction of the location and geometry of the failure region 4,24,25,27,28 , as well as regime change points in PFR 4,25,26,28 . In this study, we build on these efforts to develop a holistic data-driven framework which eliminates the uncertainties associated with a postulated stress-strain model for the slope, yet holds explicit connections to the first principles of fracture and failure mechanics of heterogeneous and disordered granular solids (e.g., [13][14][15][16] and references therein).…”

“…The landslides in Xinmo (China, 2017) and the dam collapse in Brumadinho (Brazil, 2019) are recent reminders of the devastating impact of slope failure on human lives and livelihoods, infrastructure, and the environment [1][2][3][4][5] . Here the term failure is used from an operative point of view and is the moment when the slope totally or partially collapses, displaying a paroxysmal acceleration and a disintegration of the mobilized material.…”

“…Concurrent sites of instability may also interact and induce stress redistributions that lead a landslide to "self-stabilize" [22][23][24] . Efforts 1, 7 to improve the INV approach give prima facie evidence to suggest that more accurate forecasts can be achieved when the spatial characteristics of slope displacements are incorporated in the temporal analysis of monitoring data.Accordingly, recent work focused on the spatiotemporal evolution of landslide kinematics in PFR in two case studies using: (a) ground-based radar data of a rockfall in an open pit mine (Mine 1) where two sites of instability emerged, leading one to self-stabilize before the larger one collapsed; and (b) satellitebased Sentinel 1 radar data (Xinmo) of the catastrophic collapse in Xinmo, which led to 83 fatalities 4, 24-28 .Guided by lessons learned from the physics and dynamics of granular failure, these delivered a reliable early prediction of the location and geometry of the failure region 4,24,25,27,28 , as well as regime change points in PFR 4,25,26,28 . In this study, we build on these efforts to develop a holistic data-driven framework which eliminates the uncertainties associated with a postulated stress-strain model for the slope, yet holds explicit connections to the first principles of fracture and failure mechanics of heterogeneous and disordered granular solids (e.g., [13][14][15][16] and references therein).…”

Dynamics of undeforming regions in the lead up to failure: jumping scales from lab to field

“…That is, we examine the evolution of zones of vanishing deformation in the lead up to, at incipient and during failure -at both laboratory and field levels. Data on the kinematics of a well-studied discrete element simulation of a planar biaxial compression test [6][7][8][9] and two open pit mine slopes are examined in search of a common dynamical signature [9][10][11]: see Biax, M1 and M2 in Fig. 1.…”

“…1. The aim is to improve fundamental knowledge of granular failure and help advance data-driven tools for geohazard forecasting and mitigation (e.g., [9][10][11]).…”