An approach for prospective forecasting of rock slope failure time

Abstract

Rock slope failures globally account for most single-event landslide disasters. Climatic changes in mountain areas boost failure activity and the demand for reliable failure time forecasts. State-of-the-art prediction models are often confused with high-frequency slope deformation data. Prospectively, they provide ambiguous forecasts as data filtering, starting point definition and forecast uncertainty remain arbitrary. Here, we develop a prospective failure time forecast model that applies multiple filtering and inverse velocity percentiles to minimize subjective decisions. We test the concept with 14 historic slope failures of 102-108 m3 including 46 displacement datasets from different sensors. After automatic detection of the onset of acceleration, the failure time of all events is forecasted to within −1 ± 17 h for higher-frequency data and −1 ± 4 d for daily data with a final mean uncertainty of 1 ± 1 d and 7 ± 4 d that is estimated in real-time. This prospective approach overcomes previous long-standing problems by introducing a robust and uniform concept across various types of catastrophic slope failures and sensors.

Brief Communication: Monitoring slope acceleration and impending failure with very high spatial and temporal resolution space borne Synthetic Aperture Radars

“…When velocity increases exponentially, its inverse approaches progressively lower values, and the linear projection of the time series to zero is expected 105 to provide an indication of the ultimate stages towards an impending slope failure (Fukuzono, 1985). Despite different versions and ad-hoc adaptations developed over the years (Sharifi et al, 2024), the inverse velocity approach is widely used in operational scenarios to infer the time of slope failure and to manage early warning scenarios (Rose and Hungr, 2007;Loew et al, 2017;Leinauer et al, 2023). Our results show that the inverse velocity values retrieved from PO applied to the ICEYE images converge comparably to the GB-SAR values in the week before the failure event occurred at Brienz on Jun-15, 2015.…”

Landslide Regime Shift Detector (LRSD) for Landslide Early Warning Systems

“…In this context, escalating displacements serve as a precursor indicating imminent failure (Fukuzono 1985;Kawamura 1985;Voight 1988;Zvelebill and Moser 2001). More recently, research has focused on the improvement of existing methods to pinpointing the precise time of failure (Segalini et al, 2018;Ju et al, 2020Leinauer et al, 2023, yet an uncertainty persists regarding the appropriate timing for applying these methods. There remains a challenge in determining when to deploy such techniques, as they might be employed even when failure is not imminent, rendering them ineffective in such instances (Catani and Segoni, 2022).…”