Temporal Changes of Seismic Velocity Caused by Volcanic Activity at Mt. Etna Revealed by the Autocorrelation of Ambient Seismic Noise

Abstract

On active volcanoes, ambient noise-based seismic interferometry can be a very useful monitoring tool as it allows to detect very slight variations in seismic velocity associated with magma transported toward the surface. However, the classical cross-station approach occasionally fails to detect seismic velocity changes related to eruptive activity, even on very active, well-instrumented volcanoes such as Mt. Etna. In this work, we explored an improved ambient noise-based monitoring strategy by performing the autocorrelation of seismic noise recorded at Mt. Etna volcano, by three stations located close to the active summit craters, during April 2013-October 2014. Such an interval was chosen because of the number and variety of eruptions. In place of the classical cross-correlation, we implemented the phase cross-correlation of each component with itself, which does not require normalization of the signals. The detected seismic velocity variations were very consistent for all three stations throughout the study period, mainly ranging between 0.3 and −0.2%, and were time-related to both sequences of paroxysmal eruptions and more effusive activities. In particular, we observed seismic velocity decreases accompanying paroxysmal eruptions, suggesting an intense pressurization within the plumbing system, which created an area of extensional strain with crack openings. In addition, seismic velocity variations over time were analyzed in the light of ground deformation data recorded by GPS stations and volcanic tremor centroid locations and displayed a particularly strong correlation with the former. Finally, we showed that, although the investigated frequency band (1-2 Hz) contained most of the volcanic tremor energy, our results did not indicate a particular contamination of seismic velocity variation measurements by variations of tremor sources. Ultimately, our investigation highlights a better way to implement noise-based seismic monitoring techniques. The near-field sensitivity of the autocorrelation helped De Plaen et al. Seismic Velocity Variation at Etnaimprove our understanding of the relationship between variations of seismic velocity, ground deformation and the pressurization dynamics of volcanic plumbing systems which, in turn, allows for better monitoring implementations of seismic interferometry on other volcanoes.

Chronicle of Processes Leading to the 2018 Eruption at Mt. Etna As Inferred by Seismic Ambient Noise Along With Geophysical and Geochemical Observables

“…This phase was specifically associated with the deep recharge of the reservoir at 7-12 km b.s.l. as evidenced by a progressive increase of CO 2 flux (Figure 2d), of the CO 2 / SO 2 ratio and of 3 He/ 4 He ratios (Gurrieri et al, 2021;Paonita et al, 2021) in agreement with the pressure source estimated by deformation (Cannavo' et al, 2019). The seismicity in the corresponding period is scattered down to 30 km of depth (Figure 1c; Alparone et al, 2020).…”

“…De Plaen et al. (2019) suggested that seismic velocity decreases result from intense pressurization of magma within the plumbing system for 2013–2014 paroxysmal eruptions. Patanè et al.…”

Deciphering the Whisper of Volcanoes: Monitoring Velocity Changes at Kamchatka's Klyuchevskoy Group With Fluctuating Noise Fields

“…Many studies linked volcanic inflation to dv / v decreases rather than increases (e.g., Brenguier et al., 2011; Brenguier et al., 2016; De Plaen et al., 2016, 2019; Machacca‐Puma et al., 2019; Takano et al., 2017). However, local velocity increases could occur if a shallow magma, gas, or hydrothermal reservoir, as it may exist beneath Bezymianny (Ivanov et al., 2016; Koulakov et al., 2017, 2021), inflates and, thereby, causes local compaction through increased pressure (Caudron et al., 2021, 2022; Donaldson et al., 2017, 2019; Hotovec‐Ellis et al., 2015; Yates et al., 2019).…”

“…Sens‐Schönfelder and Wegler (2006) overcame this limitation by computing dv / v from ambient seismic noise using a technique called passive image interferometry (PII). Along with other factors, such as hydrological fluctuations (Clements & Denolle, 2018; Illien et al., 2021; Mao et al., 2022; Rodríguez Tribaldos & Ajo‐Franklin, 2021), seasonal variations due to meteorological changes (Hillers et al., 2015; Sens‐Schönfelder & Wegler, 2006; Wang et al., 2017), freezing‐thawing cycles (Gassenmeier et al., 2014; James et al., 2019; Lindner et al., 2021; Steinmann et al., 2021), and earthquake‐induced subsurface damages (Bonilla et al., 2019; Boschelli et al., 2021; Brenguier, Campillo, et al., 2008; Hobiger et al., 2016; Viens et al., 2018; Wegler et al., 2009; Wegler & Sens‐Schönfelder, 2007), velocity changes due to volcanic inflation cycles have been detected by PII (Bennington et al., 2015; Brenguier, Shapiro, et al., 2008; De Plaen et al., 2016, 2019; Donaldson et al., 2017, 2019; Hirose et al., 2017; Machacca‐Puma et al., 2019; Obermann, Planès, Larose, & Campillo, 2013; Olivier et al., 2019; Ruiz et al., 2022; Sens‐Schönfelder et al., 2014).…”

Variable Daily Autocorrelation Functions of High-Frequency Seismic Data on Mars

“…The autocorrelation function (ACF), a form of seismic interferometry, is considered an approximation of the zero-offset reflection seismogram beneath a site (Claerbout 1968). Since it only requires a single station, ACF provides an efficient tool for imaging and monitoring temporal changes of seismic properties below the surface (Richter et al 2014, Bonilla et al 2019, De Plaen et al 2019, Kim & Lekic 2019, Lu & Ben-Zion 2022, especially when a limited number of seismic stations are available. These studies shed light on properties and susceptibility of subsurface materials to failure, which are of great importance to interpreting observed seismic motion, reliability of underground facilities, and other applications.…”