Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses

Abstract

Predicting the time of failure is a topic of major concern in the field of geological risk management. Several approaches, based on the analysis of displacement monitoring data, have been proposed in recent years to deal with the issue. Among these, the inverse velocity method surely demonstrated its effectiveness in anticipating the time of collapse of rock slopes displaying accelerating trends of deformation rate. However, inferring suitable linear trend lines and deducing reliable failure predictions from inverse velocity plots are processes that may be hampered by the noise present in the measurements; data smoothing is therefore a very important phase of inverse velocity analyses. In this study, different filters are tested on velocity time series from four case studies of geomechanical failure in order to improve, in retrospect, the reliability of failure predictions: Specifically, three major landslides and the collapse of an historical city wall in Italy have been examined. The effects of noise on the interpretation of inverse velocity graphs are also assessed. General guidelines to conveniently perform data smoothing, in relation to the specific characteristics of the acceleration phase, are deduced. Finally, with the aim of improving the practical use of the method and supporting the definition of emergency response plans, some standard procedures to automatically setup failure alarm levels are proposed. The thresholds which separate the alarm levels would be established without needing a long period of neither reference historical data nor calibration on past failure events.

Landslide forecasting and factors influencing predictability

“…In fact, other than the limitation of working only with creep behaviour, sometimes the tertiary creep can evolve so rapidly that a sufficient lead time for evacuation is simply not possible (IEEIRP, 2015). In other cases natural or instrumental noise can hamper the predictions and require postprocessing to allow for effective warnings (more details on the types and effects of noise can be found in Carlà et al, 2016). Other authors also contributed to methodologies for exploiting and optimizing the classic forecasting methods (Crosta and Agliardi, 2003;Dick et al, 2015;Manconi and Giordan, 2015).…”

Susceptibility of intrusion-related landslides at volcanic islands: the Stromboli case study

“…5c). The areas with higher probability of intrusion-related landslides are located in the upper part of the SdF, the same area characterized by the occurrence of mass-wasting during the 1985flank eruptions (De Fino et al 1988Calvari et al 2005Calvari et al , 2010Carlà et al 2016). The other area showing moderate susceptibility to intrusion-related landslides is the Rina Grande-Le Schicciole depression.…”

“…2) are common and frequently associated with the effusive phases. At the onset of the 1985, 2002-2003, 2007 and 2014 flank eruptions, gravel/debris slides evolved in dry gravel/debris flows along the SdF following the opening of eruptive fissures on the NE flank of the summit cone (De Fino et al 1988;Calvari et al 2005Calvari et al , 2010Carlà et al 2016). During the 2012-2013 high-intensity strombolian activity, overflows occurred and were often associated with the sliding of the NEC craterrim (Di Traglia et al 2014b;Calvari et al 2016).…”

The Maoxian landslide as seen from space: detecting precursors of failure with Sentinel-1 data

“…In particular, a slope might incur in a hyperbolic acceleration, called tertiary creep, that typically precedes collapse and from which a time of failure can be forecasted (Saito 1969;Fukuzono 1985;Rose and Hungr 2007;Mufundirwa et al 2010;Carlà et al 2017); the most popular method to achieve this prediction has been developed by Fukuzono (1985) and consists in plotting the inverse of the slope velocity against time: the point where the extrapolation of this plot intersects the time axis (corresponding to a theoretical infinite velocity) is an estimation of the time of failure.…”