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.

翻译：边坡破坏具有巨大的破坏力，可能对生命和基础设施造成重大损害。因此，监测易于失稳的边坡对于降低其破坏风险至关重要。边坡监测的目的是检测稳定问题的前兆迹象，例如边坡变形位移速率的变化。这些信息随后可用于预测即将发生破坏的时间或概率，以提供早期预警。本研究提出了一种更客观的统计学习算法，基于序列相关位移时间序列数据的频谱分析来检测和表征边坡破坏风险。该算法应用于基于卫星的干涉合成孔径雷达（InSAR）位移时间序列数据，以回溯分析2019年巴西布鲁马迪纽尾矿坝崩塌的风险。算法识别出两个潜在风险里程碑，并检测到尾矿坝明确但新兴风险（2018年2月27日至2018年8月26日）和即将崩塌风险（2018年6月27日至2018年12月24日）的迹象。重要的是，这种破坏风险的前兆指示最早在2019年1月25日大坝崩塌前至少五个月就被检测到。本研究结果表明，频谱方法与InSAR位移时间序列数据的二阶统计特性相结合，可以揭示进入不稳定变形状态的迹象，且该算法能够提供足够的早期预警，有助于减轻灾难性边坡破坏。