Towards prospective failure time forecasting of slope failures

Forecasting the time of imminent slope failures is a powerful component in local early warning systems. Different prediction methods have been developed and applied successfully since the 1960s, but the most used and commonly accepted is the inverse velocity method after Fukuzono (1985). Technical developments in real-time and remote monitoring in the last decade offer new possibilities to monitor the displacement of unstable slopes with high accuracy and high frequency. However, state-of-the-art failure time forecasting methods are not yet ready to simply use such data for prospective predictions. The inverse velocity method has not been developed with high-frequency and therefore usually noisy measurement data which require automatism and filtering which in turn influences the outcome of the forecasts. Also, it does not indicate the uncertainty of its forecasts by default. Furthermore, defining the starting point for the calculation of reasonable forecasts (onset of acceleration) in real time remains challenging while many studies in literature used the method retrospectively in post-event analyses.

We developed a prospective failure time forecasting model (PFTF model) based on the linear inverse velocity method which can handle high frequency data in real time or simulated real time. The model uses multiple smoothing windows for the input data and the inverse velocity calculation. This minimizes the influence of subjective decisions on the sensitive smoothing process and enables a statistical quantification of uncertainties. The onset of acceleration is detected automatically and in real time by using different quantiles of inverse velocities. The model runs a new calculation with every new available datapoint. The completely open-source code is written in R and will be available online after publication. To perform sensitivity analyses and calibrate the model, we used GNSS and inclinometer observations from before the acceleration phase until failure of a rock block at the Grabengufer (Randa, CH). We also tested the model with data from other historical events characterized by different geological settings, measurement techniques, and sampling rates ranging from 2 minutes to multiple hours.

Here, we show the potential of the developed PFTF model as a tool for prospective slope failure time forecasting. Our multiple smoothing approach minimizes subjective decisions, improves forecasting after automatic detection of the onset of acceleration, and enables a statistical evaluation of the forecasts´ uncertainty. The most essential pattern here is the transition from diverging, unreliable and unstable forecasts to converging, reliable and certain forecasts. After further validation with multiple datasets, the model will be applicable to many slope failure processes (slide, topple, fall, flow), different materials (rock, earth, ice, other) and different scales (m³-km³).

Reference: Fukuzono, T. (1985): A Method to Predict the Time of Slope Failure Caused by Rainfall Using the Inverse Number of Velocity of Surface Displacement. – Journal of Japan Landslide Society, 22, 2: 8–14.