Exposing the potential of XAI-based causal discovery for analysing unstable rock slopes

Unstable rock slopes pose significant challenges in geohazard analysis due to their complex nature. Traditional approaches to understanding displacement drivers on unstable rock slopes limit conclusive statements on process drivers, focusing on statistical analyses and expert knowledge. eXplainable Artificial Intelligence (XAI) based Causal Discovery enables the unbiased construction of causal graphs, depicting the cause-and-effect relations between system variables, beyond statistical correlations. In this study, we use Multi-Window Causal Discovery (MWCD) for creating causal graphs from pre-failure displacement and weather monitoring data applied to three different sites. At the Stampa rock section 4a (Norway), MWCD identifies a temporal shift from an indirect to a direct effect of precipitation and infiltration on the displacement, corresponding to a final destabilization. In Preonzo (Switzerland), causal connections change considerably during the pre-failure acceleration from temperature and precipitation dominated effects to a gravitationally driven system. At Veslemannen (Norway), MWCD identifies spatial causality with lower sections of the instability strongly influencing the deformation dynamics of the upper sections. Our results show that incorporating XAI-based methods alongside traditional geological analysis drastically improves the understanding of displacement dynamics in unstable rock slopes. This improved understanding can be used to build more robust and accurate forecasting methods.