  Slope failures possess destructive power that can cause significant damage to
both life and infrastructure. Monitoring slopes prone to instabilities is
therefore critical in mitigating the risk posed by their failure. The purpose
of slope monitoring is to detect precursory signs of stability issues, such as
changes in the rate of displacement with which a slope is deforming. This
information can then be used to predict the timing or probability of an
imminent failure in order to provide an early warning. In this study, a more
objective, statistical-learning algorithm is proposed to detect and
characterise the risk of a slope failure, based on spectral analysis of
serially correlated displacement time series data. The algorithm is applied to
satellite-based interferometric synthetic radar (InSAR) displacement time
series data to retrospectively analyse the risk of the 2019 Brumadinho tailings
dam collapse in Brazil. Two potential risk milestones are identified and signs
of a definitive but emergent risk (27 February 2018 to 26 August 2018) and
imminent risk of collapse of the tailings dam (27 June 2018 to 24 December
2018) are detected by the algorithm. Importantly, this precursory indication of
risk of failure is detected as early as at least five months prior to the dam
collapse on 25 January 2019. The results of this study demonstrate that the
combination of spectral methods and second order statistical properties of
InSAR displacement time series data can reveal signs of a transition into an
unstable deformation regime, and that this algorithm can provide sufficient
early warning that could help mitigate catastrophic slope failures.
