Lukas Schild.

A. Maschler, P. Snook, L. Schild, S. F. Samnøy, L. Kristensen, H. Dahle, J. H. Aalbu, H. Henriksen, S. D. Nerhus & T. Scheiber

Landslides 23, 851-869 (2026)

In 2023, a 54,000 m 3 large rock section failed catastrophically from the unstable rock slope Stampa (Western Norway). The failure occurred in a multistage process with two major failure events on July 1 st and 3 rd . In this study, we present a detailed analysis of the pre-failure displacement patterns, the failure mechanisms, and failure events. After exponential acceleration, the base (12,200 m 3 ) of the rock section failed, leading to a further destabilization of the remaining rock column (41,800 m 3 ), which failed 2 days later. The monitoring data includes in situ displacement sensors, a robotic total station, and close range and remote sensing data spanning over 14 years. The rock section showed seasonal displacement patterns clearly influenced by meteorological factors: (1) late spring (May–July) accelerations, controlled by positive temperatures, thawing ground, and meltwater infiltration, followed by (2) summer stabilizations characterized by low displacement, and (3) autumn (October–November) accelerations driven by precipitation events over a longer period, followed by (4) winter stabilizations approaching zero displacement. The deformation rates increased from 0.06 m a −1 in 1991–2019 more than a tenfold to 0.78 m a −1 in 2022 and indicate progressive damage and weakening of the rock section, which increased its sensitivity to rainfall and infiltration. Our findings highlight the importance of long-term, high-resolution monitoring using different, independent sensors, alongside detailed failure analyses, in understanding the evolution of unstable rock slopes. This study contributes to the understanding of progressive medium-scale rock slope failures, aiding in the prediction and mitigation of potential failures.

L. Schild, T. Scheiber, P. Snook, A. Maschler & R. Arghandeh

EGU General Assembly 2025

Geohazards such as landslides, rock avalanches or rock falls from unstable slopes can seriously threaten human life and infrastructure. Monitoring unstable slopes coupled with real-time data analyses to assess the risk they pose and mitigate this risk is thus indispensable. Machine learning-based methods for analysing monitoring data recently significantly improved the forecasting possibilities for failure events. However, one major limitation of Machine Learning-based methods is that they primarily provide "Black Box"-models. These models can, for example, transform arbitrary input into a sequence of predictions, albeit without a transparent explanation of how the output is derived from the input. Even though State-of-the-Art Machine Learning often outperforms traditional failure forecasting methods, such as the Inverse Velocity method, this limitation greatly hampers the application of these methods in practice. Recent advances in eXplainable Artificial Intelligence (XAI) have led to the development of the field of Causal Artificial Intelligence. As opposed to many Machine Learning approaches which are based on Deep Neural Networks, XAI aims to offer transparent models that provide explanations for model outputs. We therefore propose a novel forecasting approach based on XAI, leveraging Graph Neural Networks and Kolmogorov-Arnold Networks. Our approach aims to learn a causal model of an unstable slope or one particular section of it, including slope-internal and meteorological factors that can be represented as a graph, visualising cause-and-effect relationships between the variables. As such, our goal is twofold, and we aim at (1) providing insight into the mechanisms driving slope displacement, and (2) using this information for explainable short-term forecasting by selecting only causally related features from all available data. We apply our method to two case study sites for displacement driver analysis and short-term displacement prediction and compare the model performance to recent State-of-the-Art models. Our method not only aligns with but even outperforms existing models in terms of prediction accuracy and offers, in addition, superior interpretability. The proposed framework provides crucial support for geohazard assessment and monitoring network design. Furthermore, the displacement prediction has great potential as standalone predictive network as well as for hybrid failure prediction methods, for example in combination with traditional long-term failure predictions such as the Inverse Velocity method. While developed with medium-scale rock sections in mind, the method may be adapted to larger rock volumes as well as slow-moving mass movements with failure potential in general. The usage of accurate and interpretable prediction models represents a significant advancement, overcoming the transparency issues of models generated by complex Artificial Neural Networks, ultimately contributing to improving Early Warning Systems.

L. Schild, T. Scheiber, P. Snook, R. Arghandeh, S. F. Samnøy, A. Maschler & L. Kristensen

Geomatics, Natural Hazards and Risk, 14:1, 2272575

Unstable rock slopes pose a hazard to inhabitants and infrastructure in their vicinity, necessitating advanced monitoring methodologies for timely risk assessment and mitigation. Recent geotechnical monitoring techniques often rely on sensor data fusion to enhance forecasting for imminent failures. Our investigation extends beyond a single sensor type to data fusion for heterogeneous sensor networks using a Multimodal Asynchronous Kalman Filter. We illustrate the application of the proposed method on a case study data set consisting of data from an on-site sensor network enriched by remote sensing data. Employing a Multimodal Asynchronous Kalman Filter, we capitalise on the distinct resolutions inherent in each sensor input. The outcome was a combined dataset with a high spatiotemporal resolution. Our approach facilitates the estimation of essential physical attributes for monitored objects, encompassing translation, rotation, velocities and accelerations. The case study site was an unstable rock section of ca. 50.000 m3 in Aurland, Norway, which collapsed as a multi-stage failure in July 2023. Our method can be transposed to various sites with distinct sensor networks, enhancing state estimations for objects on unstable rock slopes. These estimations can significantly improve applications such as risk assessment and robust early-warning systems, enhancing predictions of critical failure points.