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.

We demonstrate the potential of satellite Interferometric Synthetic Aperture Radar (InSAR) to identify precursors to catastrophic slope failures. To date, early-warning has mostly relied on the availability of detailed, high-frequency data from sensors installed in situ. The same purpose could not be chased through spaceborne monitoring applications, as these could not yield information acquired in sufficiently systematic fashion. Here we present three sets of Sentinel-1 constellation images processed by means of multi-interferometric analysis. We detect clear trends of accelerating displacement prior to the catastrophic failure of three large slopes of very different nature: an open-pit mine slope, a natural rock slope in alpine terrain, and a tailings dam embankment. We determine that these events could have been located several days or weeks in advance. The results highlight that satellite InSAR may now be used to support decision making and enhance predictive ability for this type of hazard.

The management of unstable slopes is one of the most critical issues when dealing with safety in open-pit mines. Suitable notice of impending failure events must be provided, and at the same time the number of false alarms must be kept to a minimum to avoid financial losses deriving from unnecessary outages of the production works. Comprehensive slope monitoring programs and early warning systems are usually implemented to this aim. However, systematic procedures for their tuning are lacking and several key factors are often overlooked. Therefore the mitigation of slope failure risk is still a topic of great concern, especially in open-pit mines excavated through hard rock masses featuring markedly brittle behavior, which supposedly provide little or no measurable precursors to failure. In this paper, 9 instabilities occurred at an undisclosed open-pit mine, and monitored by ground-based radar devices, were reviewed with the goal of characterizing the typical slope deformation behavior and defining the appropriate strategy for the setup of alarms. The estimated mass of the case studies ranged from 1500 t to 750,000 t. 5 instabilities culminated to failure, whereas the other 4, although showing considerable amounts and rates of movement, ultimately did not fail. The analysis provided critical insights into the deformation of hard rock masses of high geomechanical quality, and allowed the identification of â\u80\u9csignatureâ\u80\u9d parameters of the failure events. General operative recommendations for effective slope monitoring and early warning were consequently derived