Coupled Short- and Medium-Term Geophysical Signals at Etna Volcano: Using Deformation and Strain to Infer Magmatic Processes From 2009 to 2017

Abstract

In active volcanoes a main challenge is to identify and characterize the dynamics of magmatic sources from deformation and strain data. This task is of primary importance in frequently eruptive volcanoes, such as Etna. After the main flank eruption of [2008][2009] and until 2017, Etna volcano was characterized by a lively eruptive activity of different phases. These comprised 44 lava fountain episodes from the New South East Crater (NSEC), two sequences of close episodes of lava fountains from the Voragine crater (VOR), as well as some periods of summit effusive activity with a more prolonged supply of lava flows. Several studies have described and modeled single lava fountains episodes of the NSEC and VOR, in particular through high precision data from borehole strain-meters. In this study, we broaden the analysis also considering the medium-term volcano recharging/discharging periods preceding/accompanying the different eruptive phases during 2009-2017 by constraining the source positions through deformation recorded by the permanent GPS network. Together with the modeling deduced from the strain-meter data we produce a more complete representation of the different sources that characterized the different periods both in the medium-term (i.e., the preparatory phases showing inflation and the eruptive phases showing deflation) and in the short-term (i.e., the fast discharge associated with eruptive events). Our modeling explains the pathway of magma from the intermediate-shallow plumbing system to the surface and highlights a clear separation between the inflation and the deflation source depths, coherently with petrological constraints on the spatio-temporal evolution of magma transfer and storage.

Tracking Magma Storage: New Perspectives From 40 Years (1980–2020) of Ground Deformation Source Modeling on Etna Volcano

“…Besides representing a uniform set of measurements, baseline changes are not affected by possible biases in the choice of a local reference system. In addition, for the GPS case they show lower levels of noise than the single components, and therefore directly inverting baseline changes can represent a robust method to constrain a model (Aloisi et al, 2018). In our inversions we used: only the EDM baseline for the period from 1980 to 1991, both EDM and periodic GPS measurements for the period from 1993 to 2001 and, finally, the data of the GPS permanent network for the period from 2003 to 2018 (Table 3).…”

“…Its frequent eruptions make it an ideal natural laboratory for observing and understanding the processes of magma recharge and the final intrusions (for an overview see . In particular, during the last 40 years the monitoring of ground deformation has been implemented and improved over time, providing a powerful tool to monitor the final intrusions accompanying the numerous eruptions (for a review see Bonaccorso, 2001;Allard et al, 2006;Bonforte et al, 2008;Aloisi et al, 2018) and also detect the phases of recharging before the eruptions and discharging during their course.…”

“…However, the choice of long period for analysis is conditioned by the frequent eruptions that clearly end the recharge phase. Also for this reason, the majority of source modeling studies in the past have focused on the numerous single eruptive episodes by carrying out short-term analyses related to the period astride the single eruptions (for a review see Bonaccorso, 2001;Allard et al, 2006;Bonforte et al, 2008;Aloisi et al, 2018).…”

“…These explosive episodes, although lasting a few hours and as single episodes not as considerable as major eruptions, again led to difficulties for air traffic and also substantial damage to infrastructures. In this period, during the recharging periods before the eruptive activity and those of discharging during the lava fountain activity, an interesting aspect was investigated by Aloisi et al (2018). The authors highlighted that the position of the centroid of the recharge source (pre-eruption inflation) was deeper than that of the subsequent discharge source (deflation during the lava fountain phases), testifying to a separation between the positions of the inflation and deflation sources, respectively acting before and during the eruption.…”

Classification of Mt Etna (Italy) Volcanic Activity by Machine Learning Approaches

“…A wide data set ranging from seismic, geodetic, gravimetry, geochemical, video etc., most of them collected in real time, were able to set strict constraints on the timing of the paroxysmal events which occurred in the last decade at Etna volcano [i.e. Aloisi et al, 2018;Bonaccorso et al, 2011;Greco et al, 2016].…”