A statistical-based approach for determining the intensity of unrest phases at Stromboli volcano (Southern Italy) using one-step-ahead forecasts of displacement time series

Abstract

The evaluation of the intensity of unrest phases at active volcanoes is a crucial topic in volcano hazard studies. This is particularly troublesome in the case of persistently active volcanoes like Stromboli (Southern Italy), where intense eruptive summit activity (overflows, strong spattering, powerful explosions) has in some cases anticipated a flank eruption. In this context, a new approach for the analysis of displacement data is introduced. Daily displacements of the Stromboli crater terrace measured between January 1, 2010, and August 7, 2014, by a ground-based interferometric synthetic aperture radar system were compared, in retrospect, to displacement predictions provided by an autoregressive integrated moving average-based model. The methodology consisted in assessing when the actual displacements exceeded a fixed probability threshold for the forecasts (*95 %). Two sets of data were consequently produced: (1) series of residuals between actual displacements and model threshold (''anomalies'') and (2) series of normalized residuals between actual displacements and model threshold (''normalized anomalies''). This permitted to statistically identify and quantify the anomalous deformation at the crater terrace over the reference time interval of the analysis. Anomalies started to occur before each period of intense volcanic activity, highlighting the possibility to discern between background activity and unrest. Moreover, results indicated that the inflation of the crater terrace during the preparatory phase of the 2014 flank eruption was characterized by the greatest amount of anomalous deformation.

Big data managing in a landslide early warning system: experience from a ground-based interferometric radar application

“…In this application values have been selected according to the gathered data, the first threshold being just above the instrumental noise; the remaining have been set based on expert judgement waiting for a more robust calibration, which is possible only after at least a partial mobilization of the slope. Anyway, the system is open to any method for determining thresholds (Crosta and Agliardi, 2003;Du et al, 2013;Carlà et al, 2016a) and also to the use of other parameters (acceleration for example). Besides information from sensors and models, other information is obtained from meteorological and hydrological models (named "indicators").…”

The Importance of Monitoring Interval for Rockfall Magnitude‐Frequency Estimation

“…Given the apparent difficulty in identifying rockfall triggers, some have explored the possibility of lagged effects (e.g., Lim et al, 2010;Strunden et al, 2015). Our findings suggest that monitoring at higher frequencies has the potential both to capture such lagged responses and to observe indicators of the underlying mechanisms, such as small-scale precursory rockfalls, prefailure deformation, or fracture dilation (Carlà et al, 2016;D'Amato et al, 2016;Rosser et al, 2007;Royán et al, 2015). While broad seasonal rockfall patterns or the rockfall response to distinct events can be obvious, even in low-frequency monitoring data, without data captured at a high temporal resolution it will remain difficult to distil a mechanically meaningful understanding of rockfall triggers from time-averaged data alone.…”

The 2014 Effusive Eruption at Stromboli: New Insights from In Situ and Remote-Sensing Measurements

“…The last flank eruption started at Stromboli on 7 August 2014, preceded by 2 months of increased Strombolian activity and several lava overflows from the craters expanding along the SdF [2,26]. Overflows were often accompanied by landslides along the SdF [7], described as rock-falls and/or gravel slides, evolving down slope to gravel flows (Figure 2a).…”