Modelling Explosive Nonstationarity of Ground Motion Shows Potential for Landslide Early Warning - Michael Manthey; Guoqi Qian; Antoinette Tordesillas (Engineering Proceedings)
This work applies the rarely seen explosive version of autoregressive modelling to a novel practical context—geological failure monitoring. This approach is more general than standard ARMA or ARIMA methods in that it allows the underlying data process to be explosively nonstationary, which is often the case in real-world slope failure processes. We develop and test our methodology on a case study consisting of high-quality (in situ) line-of-sight radar displacement data from a slope that undergoes a failure event. Specifically, we first optimally estimate the characteristic roots of the autoregressive processes underpinning the displacement time series preceding the failure at each monitoring location. We then establish and utilise a pivotal quantity for the autoregressive parameter ensemble to perform simulation-based hypothesis test/s for the explosiveness of the corresponding true characteristic roots. Concluding that a true characteristic root becomes explosive at some significance level implies that the underlying displacement process is explosively nonstationary, and, hence, local geological instability is suspected at this significance level. We found that the actual location of failure (LOF) was identified well in advance of the time of failure (TOF) by flagging those locations where explosive root(s) were identified by our approach. This statistical feedback model for ground motion dynamics presents an alternative and/or addition to the velocity threshold approach to early warning of impending failure.