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Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study
VOLGEO-06053; No of Pages 10 Journal of Volcanology and Geothermal Research xxx (2017) xxx–xxx
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Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study Andrea Cannata a,b,⁎, Flavio Cannavò b, Placido Montalto b, Maurizio Ercoli a, Paolo Mancinelli a, Cristina Pauselli a, Giuseppe Leto c a b c
Dipartimento di Fisica e Geologia, Università degli Studi di Perugia, Italy INGV, Osservatorio Etneo – Sezione di Catania, Italy INAF - Osservatorio Astrofisico di Catania, Italy
a r t i c l e
i n f o
Article history: Received 11 December 2016 Received in revised form 16 March 2017 Accepted 20 March 2017 Available online xxxx Keywords: Seismic noise interferometry Mt. Etna Meteorological parameters Volcanic tremor
a b s t r a c t In this work, we analysed the seismic noise recorded at Mt. Etna by 18 stations during the interval 2007–2015 in the frequency band 0.1–0.3 Hz, chosen to avoid contamination from volcanic tremor. Variations in time of medium seismic velocity in the range −0.8 to 0.8% were found, mostly affecting the stations located on the volcano summit and flanks. Based on the investigated frequency content, the Δv/v changes took place from the surface to a depth of ~4.5–6.5 km. To identify the source mechanism of the observed medium changes, the variations were quantitatively compared by wavelet transform coherence with volcano-tectonic and meteorological parameters. A significant relationship with meteorological parameters with seasonal periodicity (especially air temperature and snow loading) was found, probably caused by thermo-elastic strain and increasing-decreasing surface loading cycles. Moreover, a sharp medium velocity decrease, taking place in mid-December 2009 and clearly time-related to the largest volcano-tectonic strain release phenomenon of the investigated period, was also found. Such a velocity decrease was interpreted as resulting from ascent of fluids and gas exsolution taking place at the same time as the volcano-tectonic swarm. © 2017 Elsevier B.V. All rights reserved.
1. Introduction The transport of magma towards the surface, which precedes the eruptive events, is generally accompanied by intrusion mechanisms and pressurization of the plumbing system that can modify the velocity structure of the volcano (e.g. Brenguier et al., 2016). The intense and large-scale variations in time of the rock velocity can be detected and mapped by 4-D tomography (e.g. Patanè et al., 2006; Kasatkina et al., 2014). However, the aforementioned intrusion and pressurization mechanisms generally produce very small changes in rock velocity (b1%), that cannot be tracked by classical tomography investigations. In order to detect such small rock properties variations, two different types of seismic interferometric techniques are used: coda wave interferometry and ambient noise interferometry. The former technique needs to be performed on repeatable sources such as repeating earthquakes (e.g. Poupinet et al., 1984), artificial sources (e.g. Ratdomopurbo and Poupinet, 1995) and Strombolian explosions (e.g. Grêt et al., 2005). The direct arrivals only sample the medium once and are thus not very sensitive to velocity changes in the ⁎ Corresponding author at: Dipartimento di Fisica e Geologia, Via Alessandro Pascoli, 06123 Perugia, Italy. E-mail address: [email protected] (A. Cannata).
medium. Multiply-reflected waves, making up the earthquakes coda, are more heavily influenced by localized velocity changes. The coda wave interferometry method exploits this characteristic to quantify the medium velocity changes with very high precision, much higher than classic tomography techniques (e.g. Grêt et al., 2005; Snieder, 2006). The coda wave interferometry has been successfully used to detect very small velocity changes in volcanic environments (e.g. Ratdomopurbo and Poupinet, 1995; Cannata, 2012; Hotovec-Ellis et al., 2014). In spite of its very good sensitivity, the coda wave interferometry technique has a drawback: the temporal resolution of the medium change monitoring strictly depends on the occurrence of repeating sources (such as repeating earthquakes). On the other hand, the ambient noise interferometry is based on the fact that the Earth is not static but permanently vibrating (due to many continuous noise sources such as ocean waves, storms, anthropic activities; e.g. Brenguier et al., 2016), even when no strong energetic sources of vibration occur. For this reason, the ambient noise interferometry allows a continuous monitoring of the medium velocity changes, overcoming the drawback of the coda wave interferometry technique. Indeed, it has been recently shown how the Green's function of a medium between two receivers can be rebuilt by cross-correlating a diffuse wave field recorded at these two receivers. Once the Green's functions are known, the medium
http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023 0377-0273/© 2017 Elsevier B.V. All rights reserved.
Please cite this article as: Cannata, A., et al., Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study, J. Volcanol. Geotherm. Res. (2017), http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023
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A. Cannata et al. / Journal of Volcanology and Geothermal Research xxx (2017) xxx–xxx
velocity changes can be calculated by cross-correlating them (e.g. Mordret et al., 2010, and references therein). In addition, according to Hadziioannou et al. (2009), it has been shown how it is enough to reconstruct the Cross-Correlation Functions (hereafter referred to as CCF) between seismic signals recorded by station pairs to accurately monitor the temporal changes in seismic velocity. In the light of this, ambient noise interferometry has been largely applied to investigate volcanoes. For instance, at Piton de la Fournaise, Brenguier et al. (2008) detected decreases in seismic velocity a few weeks before eruptions, suggesting pre-eruptive inflation of the volcanic edifice. Brenguier et al. (2014) used records of seismic noise from the Japanese network to measure the crustal seismic velocity changes below volcanic regions caused by the 2011 Mw = 9.0 Tohoku-Oki earthquake. Bennington et al. (2015) found velocity decreases temporally related to a rapid inflation event at Okmok volcano, highlighted also by GPS measurements. In this work, in order to investigate the relationships between crustal velocity changes and volcano-tectonic activities at Mt. Etna volcano, we apply ambient noise interferometry on seismic signals recorded during 2007–2015. Thanks to both the dense seismic network and the intense eruptive and seismic activities, Mt. Etna can be considered as a very good natural laboratory to investigate such relationships.
Fig. 2. Data availability for the seismic stations used in the noise study. Black areas indicate periods when data were successfully recorded, white areas show periods when the stations were down.
2. Data analysis In order to perform seismic noise analysis, we used signals recorded by the vertical components of 18 stations equipped with broadband (40s cut off period), 3-component Trillium seismometers (Nanometrics™) acquiring in real-time at a sampling rate of 100 Hz (Fig. 1). It is worth noting that EBEL station was destroyed during the paroxysmal episode of 28 February 2013. Fig. 2 shows the data gaps of the used stations due to malfunctioning. Before computing the CCF, seismic noise features were characterized in terms of spectral content. In particular, in order to perform spectral analysis, the seismic signal acquired by EMFO station was chosen because of the fewest and shortest gaps due to station malfunctioning. The Short Time Fourier Transform (STFT) was calculated by putting
together the daily spectra, each of which was obtained by averaging spectra computed over all the sliding 40.96-s-long windows (Fig. 3a). In the STFT plot, it is possible to recognize two different kinds of temporal spectral variations within two distinct frequency ranges: i) above 0.5 Hz, such changes are mainly due to variations in the volcanic tremor features, whose frequency content at Mt. Etna is generally comprised between 0.5 and 5.5 Hz (e.g. Cannata et al., 2013); ii) below 0.5 Hz, the changes are clearly seasonal and are associated with microseism variations. According to the most accredited theories on the source of microseism, it is generated by direct ocean waves in shallow seafloor interacting with the sloping seafloor and by standing or colliding waves within the ocean wave field near the coast or in the deep ocean
Fig. 1. Digital elevation model of Mt. Etna with the location of seismic stations (triangles), GPS stations (squares), and the meteorological station (star) used for seismic noise analysis. The white dashed line indicates the baseline EDAM-EMGL. The black and grey concentric lines indicate the elevation contours at 500-m intervals.
Please cite this article as: Cannata, A., et al., Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study, J. Volcanol. Geotherm. Res. (2017), http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023
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Fig. 3. (a) STFT of the seismic signal recorded by the vertical component of EMFO station from 2007 to 2015. (b) RMS of the seismic signal recorded by the vertical component of EMFO station in the band 0.1–0.3 Hz. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
(e.g. Hasselmann, 1963; Grob et al., 2011). In the light of it, the observed seasonal changes can be explained by the seasonal increase of nearby storms (Stutzmann et al., 2009). To focus on the microseism changes and to highlight the periodicity of these variations, the root mean square (RMS) of the seismic signal in the band 0.1–0.3 Hz was calculated (Fig. 3b). Since the computation of Green's functions can be biased by persistent localized signals, such as the volcanic tremor (Ballmer et al., 2013), we decided to focus on the frequency band 0.1–0.3 Hz, allowing us to strongly minimize contamination by tremor. Standard data processing was performed on the seismic signals (e.g. Meier et al., 2010, and references therein): i) removal of the instrument response; ii) downsampling from 100 to 20 Hz; iii) spectral whitening in the band 0.1–0.3 Hz; iv) one-bit normalization; v) computation of daily CCFs for all the 153 interstation pairs for time lags ± 150 s by averaging the 24 hourly CCFs. Successively, a reference CCF was obtained for each interstation pair by stacking all the daily CCFs. An example of CCFs for EMFO-ESPC interstation pair is shown in Fig. 4. The reference CCFs for all the 153 interstation pairs were plotted against the interstation distance (Fig. 5) showing a slightly increasing traveltime moveout. As evidenced by Ballmer et al. (2013), this confirms that the analysed
Fig. 4. (a) CCFs of the EMFO-ESPC interstation pair calculated from 2007 to 2015, and (b) corresponding stacked CCFs (grey line) and reference CCF (black line).
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Fig. 5. Reference CCFs for all the 153 interstation pairs plotted against the interstation distance.
frequency band 0.1–0.3 Hz has almost no contamination from volcanic tremor. Finally, in order to calculate the variation of velocity over time, we followed the approach applied by Snieder et al. (2002). They demonstrated that a relative and progressive shift in time between two signals (Δt/t; in our case the reference CCF and a generic CCF) can be used to estimate the relative change in the medium seismic velocity (Δv/v), according to the equation: Δt Δv ¼− t v Following the approach described in Bennington et al. (2015), we cross correlated the reference CCF with the current CCF obtained by stacking 30-day CCFs. The comparison was performed by using a 40-slong moving window shifting by 0.1 s, for each window the maximum of the cross correlation was computed and, if this value was higher than 0.8, the associated time lag (Δt) and the corresponding center time (t) were determined. Once the time lags were obtained for the windows from 20 to 100 s, the time lags versus the center times were plotted in the y- and x-axis, respectively, and a linear regression was computed to estimate the slope equal to Δt/t and then to − Δv/v. In order to evaluate the goodness of the regression fit, we calculated the R2 parameter. The Δt/t and Δv/v computations were considered reliable if R2 was higher than 0.6. The error estimation was performed by the method suggested by Liu et al. (2010). Moreover, we also estimated the cross correlation coefficient (hereafter referred to as CCC) between the whole reference CCF and the whole current CCF. Once Δv/v and CCC were determined, a new current CCF was obtained by stacking 30-day CCFs, whose center time was one day after the previous 30day CCFs. An example of Δv/v and CCC, calculated for the ECZM-EMPL interstation pair, is shown in Fig. 6. Successively, in order to have an idea of the overall temporal variations of Δv/v and CCC, we plotted the distributions of both these parameters calculated on 3-months-long moving windows for all the 153 interstation pairs in Fig. 7a, b. We also computed the median time series and the interquartile range, used in place of average and standard deviation to minimize the effects of outliers. There is a clear seasonal periodicity in the median pattern, confirmed by the spectra computed on the median time series of both Δv/v and CCC. Clear spectral peaks are shown at frequency of 0.0027 day− 1 corresponding to about 1 year (Fig. 7c, d).
Please cite this article as: Cannata, A., et al., Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study, J. Volcanol. Geotherm. Res. (2017), http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023
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Fig. 6. Variation in time of (a) Δv/v with the error bar, and (b) CCC calculated for ECZMEMPL interstation pair.
Then, in order to understand the spatial variability of such variations, we showed the changes of Δv/v and CCC as a function of time within each year for 3 different groups of stations (Fig. 8): i) summit stations (EBEL, ECPN, EPLC, EPDN); ii) intermediate stations (EMFO, ESPC, EMPL, ECZM, ESVO, EMNR, ECBD); iii) low stations (EFIU, EPOZ, ESML,
ESCV, EPZF, ECAN, EZPO). While the temporal variations of CCC series are comparable in all three cases, the Δv/v changes are maximum and clear at the summit stations, and minimum and not as clear at the low stations. In order to better spatially constrain the area showing the maximum variations of velocity Δv/v during winters and summers, we applied the method of Brenguier et al. (2014). On its basis, we simply assigned to each station a Δv/v value that is the median of the velocity changes measured for all the interstation pairs, with which the station was involved, during the winter seasons (in particular from November to January) and during the summer seasons (in particular from June to August). The maps of medium velocity changes in winter and summer were obtained by linearly interpolating velocity changes at each station on a grid with 0.5 km of spacing (Fig. 9). Such maps confirm our previous observations, derived from the changes of Δv/v and CCC at the 3 groups of stations at different altitudes: the stations located on the volcano edifice suffer from greater Δv/v changes than the stations located at the volcano base. The depth of such medium changes can be roughly estimated by using the investigated frequency band (0.1–0.3 Hz). Indeed, in the assumption that the investigated seismic wavefield is composed of Rayleigh waves (e.g. Brenguier et al., 2007), the penetration depth of the Rayleigh waves is equal to the ratio between wavelength and the Euler's number (Bennington et al., 2015, and references therein). Considering a velocity of S-waves in the range 2.5–3.5 km/s (e.g. Patanè et al., 2006) and a frequency range 0.1–0.3 Hz (with center frequency 0.2 Hz), the penetration depth is ~ 4.5–6.5 km. In the light of it, the observed Δv/v medium changes took place from the surface to a depth of ~ 4.5– 6.5 km. Obviously, this depth estimation does not exclude that the velocity changes can affect also deeper portions of the crust.
Fig. 7. (a) Distributions of Δv/v and (b) CCC calculated on 3-months-long moving windows of all the 153 interstation pairs. The continuous and dashed black lines indicate the median time series and the interquartile range, respectively. (c) Spectrum of median time series of Δv/v with the indication of the yearly peak. (d) Spectrum of median time series of CCC with the indication of the yearly peak. The white and yellow arrows in (a) show sharp Δv/v reductions taking place in mid-December 2009 and during the end of 2013 - beginning of 2014, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: Cannata, A., et al., Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study, J. Volcanol. Geotherm. Res. (2017), http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023
A. Cannata et al. / Journal of Volcanology and Geothermal Research xxx (2017) xxx–xxx
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Fig. 8. Changes of Δv/v and CCC as a function of time within each year for 3 different groups of stations: summit stations (EBEL, ECPN, EPLC, EPDN); intermediate stations (EMFO, ESPC, EMPL, ECZM, ESVO, EMNR, ECBD); low stations (EFIU, EPOZ, ESML, ESCV, EPZF, ECAN, EZPO). The grey lines indicate the time series for the different stations; the black line represents the median.
It is also worth noting that, in addition to such seasonal variations, it is possible to identify shorter period Δv/v changes, among which the most evident are two: i) the first one took place in mid-December 2009 and consisted of a sharp Δv/v reduction that temporally interrupted the 2009–2010 winter positive semicycle (white arrow in Fig. 7a); ii) the second one occurred during the end of 2013 - beginning of 2014 and implied a sharp decrease in Δv/v (yellow arrow in Fig. 7a). 3. Comparison between Δv/v and volcanic/meteorological parameters To identify the possible source mechanisms of the observed changes, we compared the Δv/v time series with volcanic and meteorological parameters. As for the volcano activity, we distinguished two different groups of eruptions (Fig. 10b): (i) short lasting intense Strombolian activities and lava fountains from summit craters (duration of hours-days); (ii) long lasting Strombolian and effusive activities from both summit craters and eccentric fissures (weeks-months). Detailed descriptions of the volcano activity during the investigated time span can be found in Patanè
et al. (2013), Behncke et al. (2014), De Beni et al. (2015) and Spina et al. (2015). A qualitative comparison between these eruptions and the observed Δv/v changes (Figs. 10a, b) does not highlight any significant and clear link between eruptive activity and medium velocity variations. The activity of the volcano was also tracked by analyzing volcanic tremor amplitude, ground deformation and volcano-tectonic (VT) activity. Concerning the former, it has been shown how at Mt. Etna volcanic tremor amplitude changes often reflect variations in the eruptive (especially explosive) activity (e.g. Viccaro et al., 2014; Cannata et al., 2015). Thus, the RMS of the vertical component of the seismic signal recorded in the band 0.5–5.5 Hz at ECPN station was calculated on 1-hour-long moving windows (Fig. 10c). ECPN station was chosen because of both the small distance from the summit area (and hence from the volcanic tremor sources) and the relatively short gaps due to station malfunctioning (Figs. 1 and 2). The frequency band 0.5–5.5 Hz was fixed because most of the energy from volcanic tremor at Mt. Etna is radiated in this band (e.g. Cannata et al., 2013). The RMS time series shows energetic peaks due to the increase of volcanic tremor amplitude at the same time as explosive eruptions.
Fig. 9. Space distribution of the Δv/v variations during summer (a) and winter (b). The black concentric lines indicate the elevation contours at 125-m intervals. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: Cannata, A., et al., Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study, J. Volcanol. Geotherm. Res. (2017), http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023
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Fig. 10. Comparison between temporal variations of Δv/v (a), eruptive activity (b) classified into short lasting intense Strombolian activity/lava fountain (red diamonds) and long lasting Strombolian/effusive activity (red rectangles), seismic RMS computed on the vertical component of the seismic signal recorded at ECPN station and filtered in the band 0.5–5.5 Hz (c), daily baseline changes for the sites EDAM-EMGL (d), daily number of VT earthquakes (histogram) and corresponding cumulative strain release (red area) (e), air temperature (f), atmospheric pressure (g), and snow load (h) in the reference period. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
The ground deformation time series was calculated by measuring the daily baseline changes for the sites EDAM-EMGL (see Fig. 1 for the map). Such a baseline, chosen because of the location in the westernnorthern flank at intermediate elevation, thus not affected by the sliding dynamics that characterizes the eastern flank (e.g. Mattia et al., 2015), was detrended to enhance the shorter-period variations (Fig. 10d). The baseline shows several oscillations, most of which are related to
inflation and deflation phenomena preceding and accompanying, respectively, the eruptions. Finally, as for the VT activity, we considered the VT earthquakes taking place at Mt. Etna (data from Gruppo Analisi Dati Sismici, 2016, http://sismoweb.ct.ingv.it/maps/eq_maps/sicily/). The boundaries considered to extract Mt. Etna VT earthquakes were: latitude ranging from 37.55°N to 37.90°N, longitude from 14.77°E to 15.23°E, depth
Please cite this article as: Cannata, A., et al., Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study, J. Volcanol. Geotherm. Res. (2017), http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023
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from 0 to 36.5 km (VT location maps at Mt. Etna can be found in Alparone et al., 2015, and Tuvè et al., 2015). In particular, we calculated the daily rate of occurrence and the corresponding cumulative released strain (Fig. 10e). The cumulative strain release plot shows a fairly constant rate, with several steps of different amplitudes corresponding to VT swarms taking place on different areas of the volcano edifice at various depths. Concerning the meteorological parameters, we took into account air temperature and pressure data recorded at a station located on the southern volcano flank (INAF-OA in Fig. 1) run by Istituto Nazionale di Astrofisica, Osservatorio Astrofisico di Catania (Fig. 10f, g). Finally, we also considered the snow load (Fig. 10h) calculated following the method by Larson et al. (2009) and Larson and Nievinski (2013) using the GPS signal acquired by the antenna installed in ESLN (Fig. 1). Among the GPS permanent stations deployed close to the summit area, we chose this station because of its reliability, result coherence, minimum changes in equipped instruments and uptime percentage during the years 2007–2015. In order to perform a quantitative comparison between the above cited time series and Δv/v, we applied the Wavelet Transform Coherence (hereafter referred to as WTC), evaluating the intensity of the covariance in time–frequency domain normalized in the range 0–1. Such a technique allows us to determine also information on the phase relationship between time series (e.g. Torrence and Compo, 1998; Grinsted et al., 2004). WTC has been used to compare time series in seismology (e.g. Cannata et al., 2010, 2013), as well as in other disciplines such as meteorology (e.g. Jevrejeva et al., 2003), epidemiology (e.g. Yang et al., 2008) and astrophysics (e.g. Donner and Thiel, 2007). When comparing time series, the advantage of using WTC instead of the simple Pearson's correlation is the ability to provide information not only in the time but also in the frequency domain at different observation scales. Since the wavelet transform has edge artefacts, it is useful to introduce a cone of influence (COI) in which edge effects cannot be ignored. Finally, also
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the disturbances affecting the considered time series have to be taken into account. Commonly, power spectra of geophysical time series are characterized by increasing power at lower frequencies and show many distinctive red noise features. Following previous studies (e.g. Grinsted et al., 2004; Jevrejeva et al., 2003), a 5% statistical significance level against red noise is considered in this work. As expected, the time series most related to Δv/v changes, in terms of significantly high values of coherence for long time intervals, is the air temperature at periods in between 200 and 400 days, confirming the seasonality of the medium changes (Fig. 11a). The two series, air temperature and Δv/v changes, turned out to be almost in anti-phase with delay (from the perfect anticorrelation) of about 60 days (temperature variations precede Δv/v changes), estimated by the phase difference shown by the arrows. In addition, the atmospheric pressure shows good coherence with the Δv/v changes with similar delays to the ones observed comparing air temperature and Δv/v (Fig. 11b). Moreover, the snow load also shows very high values of coherence with the Δv/v changes at period of 250–500 days with a delay of about 50 days (Fig. 11c). However, since in this case the Δv/v positive changes precede the snow-load increase, the crustal variations cannot be entirely due to such a parameter. Significant values of coherence at similar periods are also shown in seismic RMS and to a lesser extent in the EDAMEMGL deformation baseline (Fig. 11d, e). While the presence of a seasonal periodicity is expected for the meteorological time series, it is not so trivial for the other two. On the other hand, in spite of the considered frequency band 0.5–5.5 Hz, the computed seismic RMS is likely to be partially contaminated by the microseism noise. As for the EDAMEMGL baseline reflecting the ground deformation of the volcano edifice, it is probably affected by thermo-elastic changes or groundwater level variations with seasonal periodicity, as highlighted in California by Prawirodirdjo et al. (2006) and Ji and Herring (2012). In addition, also the WTC calculated between Δv/v and VT released strain shows significant high coherence values during December 2009
Fig. 11. Wavelet transform coherence between Δv/v and air temperature (a), Δv/v and atmospheric pressure (b), Δv/v and snow load (c), Δv/v and seismic RMS (d), Δv/v and baseline EDAM-EMGL (e), Δv/v and VT earthquakes released strain (f). The 5% significance level against red noise is shown as a thick black contour. The vectors indicate the phase difference between time series (a horizontal arrow pointing from left to right signifies in phase and an arrow pointing vertically upward means the first series lags the second one by 90°). The cone of influence (COI), where the edge effects might distort the picture, is shown as a lighter shade. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: Cannata, A., et al., Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study, J. Volcanol. Geotherm. Res. (2017), http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023
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(Fig. 11f). In the Δv/v plot a sharp decrease, that as aforementioned temporarily interrupted the positive winter semicycle, can be observed in mid-December 2009 (white arrow in Fig. 7a). In the VT earthquake plot, it is possible to note that in mid-December 2009 the largest strain release phenomenon of the entire 9-year-long analysed period took place (Fig. 10e). In particular, such a release was related to a swarm of VT earthquakes, composed of ~ 100 located events with magnitude ranging from 1.8 to 4.8, with epicenters in the north-western sector of the volcano and focal depth between 20 and 30 km (Fig. 12; Gruppo Analisi Dati Sismici, 2016, http://sismoweb.ct.ingv.it/maps/eq_maps/ sicily/). 4. Discussion and conclusions In this work, we analysed the seismic noise recorded at Mt. Etna by 18 stations during the interval 2007–2015 in the frequency band 0.1– 0.3 Hz, chosen to avoid contamination from volcanic tremor. Variations in time of Δv/v in the range −0.8 to 0.8% were found mostly affecting the stations located on the volcano flanks and summit region, and to a lesser extent the stations located at low altitudes (Figs. 7, 8, 9). Based on the investigated frequency content, the observed Δv/v medium changes took place from the surface to a depth of ~4.5–6.5 km. In order to identify the possible source mechanism of the observed medium changes, the Δv/v variations were quantitatively compared by WTC with volcanic and meteorological parameters (Figs. 10, 11). A significant relationship with the air temperature with seasonal periodicity was found, showing a delay of about 60 days from the perfect anticorrelation. As also stated by Meier et al. (2010), it has been shown by Prawirodirdjo et al. (2006) that changes in surface
temperature can induce thermo-elastic strain variations that persist down to depth of 15–20 km, well beyond the depth at which most of the observed changes in seismic velocity took place (from surface to ~ 4.5–6.5 km). The thermo-elastic strain origin of the Δv/v variations is also suggested by the phase shift with the air temperature, that, based on the computations performed by Richter et al. (2014), should be equal to tens of days, in agreement with the 60 days delay observed at Mt. Etna. Air temperature variations could also contribute to generate the different Δv/v change patterns observed at stations at distinct altitudes (Fig. 8). Indeed, although the annual temperature ranges are similar in the whole investigated area and equal to about 14–15 °C (Chester et al., 1986), only at high altitudes seasonal temperature variations imply freezing of the ground during winter. Such a phenomenon can produce time varying site effects, that can affect our Δv/v measurements at high-altitude stations. Among the analysed meteorological parameters, another factor that could cause distinct amounts of Δv/v changes at stations located on the volcano edifice with respect to the stations located at the volcano base is the snow load. Indeed, although it cannot entirely generate the observed Δv/v changes as the onsets of Δv/v increases precede the snowload increments, it can contribute to intensify the crustal seasonal variations at high altitudes. Indeed, as supposed also by Hotovec-Ellis et al. (2014) to explain different amount of Δv/v changes observed at distinct stations on Mt. St. Helens, at high altitudes the snow load increases the seismic velocity by closing cracks due to the increased surface loading. In addition, the atmospheric pressure showed a good coherence with the Δv/v changes. However, since the pressure mostly ranged in between 815 and 830 hPa (and then a variation of b2 kPa), it can be inferred that the effect of the pressure changes is secondary with respect
Fig. 12. Locations in map (a), sections (b, c) and 3D view (d) of the seismic VT swarm taking place at Mt. Etna during 18–21 December 2009. The dot colors indicate the depth of the earthquake locations. The dot size is proportional to the magnitude of the VT earthquakes (see the legend in the lower right corner of the map). The black concentric lines indicate the elevation contours at 250-m intervals. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: Cannata, A., et al., Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study, J. Volcanol. Geotherm. Res. (2017), http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023
A. Cannata et al. / Journal of Volcanology and Geothermal Research xxx (2017) xxx–xxx
to the influence of the variable snow load, especially at very high altitudes (where thicker snow layers are expected). Indeed, the snow load of 1 m at ESLN, located at mid-altitude, is equal to ~3 kPa (with a snow density of 300 kg/m3); hence, the effect of the variable snow loads on the volcano summit is expected to be higher than the atmospheric pressure variations. Another meteorological parameter, that strictly depends on precipitation and then on snow load on Mt. Etna, is the meteoritic water diffusion at depth. Indeed, if the fraction of water in partially filled cracks changes, this can influence seismic velocity (e.g. Grêt et al., 2006; Wegler et al., 2009). At Mt. Etna, such a phenomenon takes mainly place during the spring, due to the snow melt. We cannot exclude that also the meteoritic water diffusion at depth plays a role in accelerating the Δv/v decrease during springtime. As for the sharp Δv/v decrease taking place in mid-December 2009 (white arrow in Fig. 7a), it is possible to note a clear time relationship with the largest seismic strain release phenomenon observed during the entire 9-year-long analysed period. In particular, such a release was related to a swarm of VT earthquakes, among which ~ 100 events were located and had magnitude ranging from 1.8 to 4.8, with epicenters in the north-western sector of the volcano at focal depth between 20 and 30 km (Fig. 12; Gruppo Analisi Dati Sismici, 2016, http:// sismoweb.ct.ingv.it/maps/eq_maps/sicily/). Medium Δv/v changes, taking place on volcanoes and time-related to earthquakes, have been sometimes reported in the literature. For instance, Hotovec-Ellis et al. (2014) observed a Δv/v decrease at Mount St. Helens at the same time as a M = 6.8 earthquake occurring 113 km away from the volcano. Such a variation was inferred to be due to a dynamic yet permanent response to shaking. Similarly, Battaglia et al. (2012) observed at Yasur volcano a medium velocity drop time-related to a M = 7.3 earthquake, that occurred 80 km away from the volcano summit. In this case, a dynamic stress transfer, causing opening of cracks and gas exsolution below the volcano summit, was inferred to be the origin of the Δv/v decrease. The mid-December 2009 VT swarm at Mt. Etna was interpreted by Patanè et al. (2013) to be the seismic response of local structures to the stress field induced from the ascent of magmatic fluids, that fed the 2011 lava fountain activities. Deep earthquakes in Etna's western sector are thought to be early markers of future volcanic activity, and this process has already been observed before several eruptions (Gresta et al., 1990; Bonaccorso et al., 1996; Patanè et al., 2004). On the basis of this and in agreement with the interpretation of Battaglia et al. (2012), the Δv/v decrease observed in mid-December 2009 could be due to ascent of fluids and gas exsolution taking place at the same time as the VT swarm. The other evident Δv/v change, taking place during the end of 2013 beginning of 2014 (yellow arrow in Fig. 7a), seems not to be associated with any considered volcanic and meteorological parameters. In spite of the many eruptive activities taking place during the analysed period (Fig. 10b), the mid-December 2009 Δv/v decrease was the only observed medium velocity change related to the volcano activity during 2007–2015. The variations due to the other intrusions, eruptions and VT swarms were likely to be smaller and affected only a part of the volcano edifice. Hence, such changes were probably below the level of noise and then not observable by our method, that is heavily influenced by seasonal changes affecting the whole volcano edifice up to several km of depth. A similar behaviour was also noted by Hotovec-Ellis et al. (2014) who clearly observed seasonal changes due to meteorological patterns, but did not detect variations due to magma injections. In our study, the lack of evidences of other relationships between Δv/v changes and the volcano activity was probably also caused by the low frequency band used to investigate the medium changes (0.1–0.3 Hz), corresponding with very long wavelength, in the order of several kilometers. Only meteorological variations (i.e. temperature changes, snow loading cycles) or important tectonic events and swarms could be so widespread to have measurable effects on the seismic velocity of the entire volcano edifice.
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Acknowledgements Wavelet coherence software was kindly provided by A. Grinsted. The authors thank the INGV-CT Gruppo Analisi Dati Sismici for providing earthquakes data. The authors are indebted to the technicians of the INGV–Osservatorio Etneo for maintaining the monitoring systems of Mt. Etna. The authors also acknowledge Istituto Nazionale di Astrofisica, Osservatorio Astrofisico di Catania for providing meteorological data. This work was partially supported by the Programma Nazionale Ricerche in Antartide (ICE-VOLC project, PNRA14_00011). A.C. thank the project AEOLUS funded by the Fondo di Ricerca di Base of Department of Physics and Geology, University of Perugia. We are grateful to two anonymous reviewers for their useful suggestions that greatly improved the paper. References Alparone, S., Maiolino, V., Mostaccio, A., Scaltrito, A., Ursino, A., Barberi, G., D'Amico, S., Di Grazia, G., Giampiccolo, E., Musumeci, C., Scarfì, L., Zuccarello, L., 2015. 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Please cite this article as: Cannata, A., et al., Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study, J. Volcanol. Geotherm. Res. (2017), http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023
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Journal of Volcanology and Geothermal Research journal homepage: www.elsevier.com/locate/jvolgeores
Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study Andrea Cannata a,b,⁎, Flavio Cannavò b, Placido Montalto b, Maurizio Ercoli a, Paolo Mancinelli a, Cristina Pauselli a, Giuseppe Leto c a b c
Dipartimento di Fisica e Geologia, Università degli Studi di Perugia, Italy INGV, Osservatorio Etneo – Sezione di Catania, Italy INAF - Osservatorio Astrofisico di Catania, Italy
a r t i c l e
i n f o
Article history: Received 11 December 2016 Received in revised form 16 March 2017 Accepted 20 March 2017 Available online xxxx Keywords: Seismic noise interferometry Mt. Etna Meteorological parameters Volcanic tremor
a b s t r a c t In this work, we analysed the seismic noise recorded at Mt. Etna by 18 stations during the interval 2007–2015 in the frequency band 0.1–0.3 Hz, chosen to avoid contamination from volcanic tremor. Variations in time of medium seismic velocity in the range −0.8 to 0.8% were found, mostly affecting the stations located on the volcano summit and flanks. Based on the investigated frequency content, the Δv/v changes took place from the surface to a depth of ~4.5–6.5 km. To identify the source mechanism of the observed medium changes, the variations were quantitatively compared by wavelet transform coherence with volcano-tectonic and meteorological parameters. A significant relationship with meteorological parameters with seasonal periodicity (especially air temperature and snow loading) was found, probably caused by thermo-elastic strain and increasing-decreasing surface loading cycles. Moreover, a sharp medium velocity decrease, taking place in mid-December 2009 and clearly time-related to the largest volcano-tectonic strain release phenomenon of the investigated period, was also found. Such a velocity decrease was interpreted as resulting from ascent of fluids and gas exsolution taking place at the same time as the volcano-tectonic swarm. © 2017 Elsevier B.V. All rights reserved.
1. Introduction The transport of magma towards the surface, which precedes the eruptive events, is generally accompanied by intrusion mechanisms and pressurization of the plumbing system that can modify the velocity structure of the volcano (e.g. Brenguier et al., 2016). The intense and large-scale variations in time of the rock velocity can be detected and mapped by 4-D tomography (e.g. Patanè et al., 2006; Kasatkina et al., 2014). However, the aforementioned intrusion and pressurization mechanisms generally produce very small changes in rock velocity (b1%), that cannot be tracked by classical tomography investigations. In order to detect such small rock properties variations, two different types of seismic interferometric techniques are used: coda wave interferometry and ambient noise interferometry. The former technique needs to be performed on repeatable sources such as repeating earthquakes (e.g. Poupinet et al., 1984), artificial sources (e.g. Ratdomopurbo and Poupinet, 1995) and Strombolian explosions (e.g. Grêt et al., 2005). The direct arrivals only sample the medium once and are thus not very sensitive to velocity changes in the ⁎ Corresponding author at: Dipartimento di Fisica e Geologia, Via Alessandro Pascoli, 06123 Perugia, Italy. E-mail address: [email protected] (A. Cannata).
medium. Multiply-reflected waves, making up the earthquakes coda, are more heavily influenced by localized velocity changes. The coda wave interferometry method exploits this characteristic to quantify the medium velocity changes with very high precision, much higher than classic tomography techniques (e.g. Grêt et al., 2005; Snieder, 2006). The coda wave interferometry has been successfully used to detect very small velocity changes in volcanic environments (e.g. Ratdomopurbo and Poupinet, 1995; Cannata, 2012; Hotovec-Ellis et al., 2014). In spite of its very good sensitivity, the coda wave interferometry technique has a drawback: the temporal resolution of the medium change monitoring strictly depends on the occurrence of repeating sources (such as repeating earthquakes). On the other hand, the ambient noise interferometry is based on the fact that the Earth is not static but permanently vibrating (due to many continuous noise sources such as ocean waves, storms, anthropic activities; e.g. Brenguier et al., 2016), even when no strong energetic sources of vibration occur. For this reason, the ambient noise interferometry allows a continuous monitoring of the medium velocity changes, overcoming the drawback of the coda wave interferometry technique. Indeed, it has been recently shown how the Green's function of a medium between two receivers can be rebuilt by cross-correlating a diffuse wave field recorded at these two receivers. Once the Green's functions are known, the medium
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Please cite this article as: Cannata, A., et al., Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study, J. Volcanol. Geotherm. Res. (2017), http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023
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velocity changes can be calculated by cross-correlating them (e.g. Mordret et al., 2010, and references therein). In addition, according to Hadziioannou et al. (2009), it has been shown how it is enough to reconstruct the Cross-Correlation Functions (hereafter referred to as CCF) between seismic signals recorded by station pairs to accurately monitor the temporal changes in seismic velocity. In the light of this, ambient noise interferometry has been largely applied to investigate volcanoes. For instance, at Piton de la Fournaise, Brenguier et al. (2008) detected decreases in seismic velocity a few weeks before eruptions, suggesting pre-eruptive inflation of the volcanic edifice. Brenguier et al. (2014) used records of seismic noise from the Japanese network to measure the crustal seismic velocity changes below volcanic regions caused by the 2011 Mw = 9.0 Tohoku-Oki earthquake. Bennington et al. (2015) found velocity decreases temporally related to a rapid inflation event at Okmok volcano, highlighted also by GPS measurements. In this work, in order to investigate the relationships between crustal velocity changes and volcano-tectonic activities at Mt. Etna volcano, we apply ambient noise interferometry on seismic signals recorded during 2007–2015. Thanks to both the dense seismic network and the intense eruptive and seismic activities, Mt. Etna can be considered as a very good natural laboratory to investigate such relationships.
Fig. 2. Data availability for the seismic stations used in the noise study. Black areas indicate periods when data were successfully recorded, white areas show periods when the stations were down.
2. Data analysis In order to perform seismic noise analysis, we used signals recorded by the vertical components of 18 stations equipped with broadband (40s cut off period), 3-component Trillium seismometers (Nanometrics™) acquiring in real-time at a sampling rate of 100 Hz (Fig. 1). It is worth noting that EBEL station was destroyed during the paroxysmal episode of 28 February 2013. Fig. 2 shows the data gaps of the used stations due to malfunctioning. Before computing the CCF, seismic noise features were characterized in terms of spectral content. In particular, in order to perform spectral analysis, the seismic signal acquired by EMFO station was chosen because of the fewest and shortest gaps due to station malfunctioning. The Short Time Fourier Transform (STFT) was calculated by putting
together the daily spectra, each of which was obtained by averaging spectra computed over all the sliding 40.96-s-long windows (Fig. 3a). In the STFT plot, it is possible to recognize two different kinds of temporal spectral variations within two distinct frequency ranges: i) above 0.5 Hz, such changes are mainly due to variations in the volcanic tremor features, whose frequency content at Mt. Etna is generally comprised between 0.5 and 5.5 Hz (e.g. Cannata et al., 2013); ii) below 0.5 Hz, the changes are clearly seasonal and are associated with microseism variations. According to the most accredited theories on the source of microseism, it is generated by direct ocean waves in shallow seafloor interacting with the sloping seafloor and by standing or colliding waves within the ocean wave field near the coast or in the deep ocean
Fig. 1. Digital elevation model of Mt. Etna with the location of seismic stations (triangles), GPS stations (squares), and the meteorological station (star) used for seismic noise analysis. The white dashed line indicates the baseline EDAM-EMGL. The black and grey concentric lines indicate the elevation contours at 500-m intervals.
Please cite this article as: Cannata, A., et al., Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study, J. Volcanol. Geotherm. Res. (2017), http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023
A. Cannata et al. / Journal of Volcanology and Geothermal Research xxx (2017) xxx–xxx
Fig. 3. (a) STFT of the seismic signal recorded by the vertical component of EMFO station from 2007 to 2015. (b) RMS of the seismic signal recorded by the vertical component of EMFO station in the band 0.1–0.3 Hz. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
(e.g. Hasselmann, 1963; Grob et al., 2011). In the light of it, the observed seasonal changes can be explained by the seasonal increase of nearby storms (Stutzmann et al., 2009). To focus on the microseism changes and to highlight the periodicity of these variations, the root mean square (RMS) of the seismic signal in the band 0.1–0.3 Hz was calculated (Fig. 3b). Since the computation of Green's functions can be biased by persistent localized signals, such as the volcanic tremor (Ballmer et al., 2013), we decided to focus on the frequency band 0.1–0.3 Hz, allowing us to strongly minimize contamination by tremor. Standard data processing was performed on the seismic signals (e.g. Meier et al., 2010, and references therein): i) removal of the instrument response; ii) downsampling from 100 to 20 Hz; iii) spectral whitening in the band 0.1–0.3 Hz; iv) one-bit normalization; v) computation of daily CCFs for all the 153 interstation pairs for time lags ± 150 s by averaging the 24 hourly CCFs. Successively, a reference CCF was obtained for each interstation pair by stacking all the daily CCFs. An example of CCFs for EMFO-ESPC interstation pair is shown in Fig. 4. The reference CCFs for all the 153 interstation pairs were plotted against the interstation distance (Fig. 5) showing a slightly increasing traveltime moveout. As evidenced by Ballmer et al. (2013), this confirms that the analysed
Fig. 4. (a) CCFs of the EMFO-ESPC interstation pair calculated from 2007 to 2015, and (b) corresponding stacked CCFs (grey line) and reference CCF (black line).
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Fig. 5. Reference CCFs for all the 153 interstation pairs plotted against the interstation distance.
frequency band 0.1–0.3 Hz has almost no contamination from volcanic tremor. Finally, in order to calculate the variation of velocity over time, we followed the approach applied by Snieder et al. (2002). They demonstrated that a relative and progressive shift in time between two signals (Δt/t; in our case the reference CCF and a generic CCF) can be used to estimate the relative change in the medium seismic velocity (Δv/v), according to the equation: Δt Δv ¼− t v Following the approach described in Bennington et al. (2015), we cross correlated the reference CCF with the current CCF obtained by stacking 30-day CCFs. The comparison was performed by using a 40-slong moving window shifting by 0.1 s, for each window the maximum of the cross correlation was computed and, if this value was higher than 0.8, the associated time lag (Δt) and the corresponding center time (t) were determined. Once the time lags were obtained for the windows from 20 to 100 s, the time lags versus the center times were plotted in the y- and x-axis, respectively, and a linear regression was computed to estimate the slope equal to Δt/t and then to − Δv/v. In order to evaluate the goodness of the regression fit, we calculated the R2 parameter. The Δt/t and Δv/v computations were considered reliable if R2 was higher than 0.6. The error estimation was performed by the method suggested by Liu et al. (2010). Moreover, we also estimated the cross correlation coefficient (hereafter referred to as CCC) between the whole reference CCF and the whole current CCF. Once Δv/v and CCC were determined, a new current CCF was obtained by stacking 30-day CCFs, whose center time was one day after the previous 30day CCFs. An example of Δv/v and CCC, calculated for the ECZM-EMPL interstation pair, is shown in Fig. 6. Successively, in order to have an idea of the overall temporal variations of Δv/v and CCC, we plotted the distributions of both these parameters calculated on 3-months-long moving windows for all the 153 interstation pairs in Fig. 7a, b. We also computed the median time series and the interquartile range, used in place of average and standard deviation to minimize the effects of outliers. There is a clear seasonal periodicity in the median pattern, confirmed by the spectra computed on the median time series of both Δv/v and CCC. Clear spectral peaks are shown at frequency of 0.0027 day− 1 corresponding to about 1 year (Fig. 7c, d).
Please cite this article as: Cannata, A., et al., Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study, J. Volcanol. Geotherm. Res. (2017), http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023
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Fig. 6. Variation in time of (a) Δv/v with the error bar, and (b) CCC calculated for ECZMEMPL interstation pair.
Then, in order to understand the spatial variability of such variations, we showed the changes of Δv/v and CCC as a function of time within each year for 3 different groups of stations (Fig. 8): i) summit stations (EBEL, ECPN, EPLC, EPDN); ii) intermediate stations (EMFO, ESPC, EMPL, ECZM, ESVO, EMNR, ECBD); iii) low stations (EFIU, EPOZ, ESML,
ESCV, EPZF, ECAN, EZPO). While the temporal variations of CCC series are comparable in all three cases, the Δv/v changes are maximum and clear at the summit stations, and minimum and not as clear at the low stations. In order to better spatially constrain the area showing the maximum variations of velocity Δv/v during winters and summers, we applied the method of Brenguier et al. (2014). On its basis, we simply assigned to each station a Δv/v value that is the median of the velocity changes measured for all the interstation pairs, with which the station was involved, during the winter seasons (in particular from November to January) and during the summer seasons (in particular from June to August). The maps of medium velocity changes in winter and summer were obtained by linearly interpolating velocity changes at each station on a grid with 0.5 km of spacing (Fig. 9). Such maps confirm our previous observations, derived from the changes of Δv/v and CCC at the 3 groups of stations at different altitudes: the stations located on the volcano edifice suffer from greater Δv/v changes than the stations located at the volcano base. The depth of such medium changes can be roughly estimated by using the investigated frequency band (0.1–0.3 Hz). Indeed, in the assumption that the investigated seismic wavefield is composed of Rayleigh waves (e.g. Brenguier et al., 2007), the penetration depth of the Rayleigh waves is equal to the ratio between wavelength and the Euler's number (Bennington et al., 2015, and references therein). Considering a velocity of S-waves in the range 2.5–3.5 km/s (e.g. Patanè et al., 2006) and a frequency range 0.1–0.3 Hz (with center frequency 0.2 Hz), the penetration depth is ~ 4.5–6.5 km. In the light of it, the observed Δv/v medium changes took place from the surface to a depth of ~ 4.5– 6.5 km. Obviously, this depth estimation does not exclude that the velocity changes can affect also deeper portions of the crust.
Fig. 7. (a) Distributions of Δv/v and (b) CCC calculated on 3-months-long moving windows of all the 153 interstation pairs. The continuous and dashed black lines indicate the median time series and the interquartile range, respectively. (c) Spectrum of median time series of Δv/v with the indication of the yearly peak. (d) Spectrum of median time series of CCC with the indication of the yearly peak. The white and yellow arrows in (a) show sharp Δv/v reductions taking place in mid-December 2009 and during the end of 2013 - beginning of 2014, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: Cannata, A., et al., Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study, J. Volcanol. Geotherm. Res. (2017), http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023
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Fig. 8. Changes of Δv/v and CCC as a function of time within each year for 3 different groups of stations: summit stations (EBEL, ECPN, EPLC, EPDN); intermediate stations (EMFO, ESPC, EMPL, ECZM, ESVO, EMNR, ECBD); low stations (EFIU, EPOZ, ESML, ESCV, EPZF, ECAN, EZPO). The grey lines indicate the time series for the different stations; the black line represents the median.
It is also worth noting that, in addition to such seasonal variations, it is possible to identify shorter period Δv/v changes, among which the most evident are two: i) the first one took place in mid-December 2009 and consisted of a sharp Δv/v reduction that temporally interrupted the 2009–2010 winter positive semicycle (white arrow in Fig. 7a); ii) the second one occurred during the end of 2013 - beginning of 2014 and implied a sharp decrease in Δv/v (yellow arrow in Fig. 7a). 3. Comparison between Δv/v and volcanic/meteorological parameters To identify the possible source mechanisms of the observed changes, we compared the Δv/v time series with volcanic and meteorological parameters. As for the volcano activity, we distinguished two different groups of eruptions (Fig. 10b): (i) short lasting intense Strombolian activities and lava fountains from summit craters (duration of hours-days); (ii) long lasting Strombolian and effusive activities from both summit craters and eccentric fissures (weeks-months). Detailed descriptions of the volcano activity during the investigated time span can be found in Patanè
et al. (2013), Behncke et al. (2014), De Beni et al. (2015) and Spina et al. (2015). A qualitative comparison between these eruptions and the observed Δv/v changes (Figs. 10a, b) does not highlight any significant and clear link between eruptive activity and medium velocity variations. The activity of the volcano was also tracked by analyzing volcanic tremor amplitude, ground deformation and volcano-tectonic (VT) activity. Concerning the former, it has been shown how at Mt. Etna volcanic tremor amplitude changes often reflect variations in the eruptive (especially explosive) activity (e.g. Viccaro et al., 2014; Cannata et al., 2015). Thus, the RMS of the vertical component of the seismic signal recorded in the band 0.5–5.5 Hz at ECPN station was calculated on 1-hour-long moving windows (Fig. 10c). ECPN station was chosen because of both the small distance from the summit area (and hence from the volcanic tremor sources) and the relatively short gaps due to station malfunctioning (Figs. 1 and 2). The frequency band 0.5–5.5 Hz was fixed because most of the energy from volcanic tremor at Mt. Etna is radiated in this band (e.g. Cannata et al., 2013). The RMS time series shows energetic peaks due to the increase of volcanic tremor amplitude at the same time as explosive eruptions.
Fig. 9. Space distribution of the Δv/v variations during summer (a) and winter (b). The black concentric lines indicate the elevation contours at 125-m intervals. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: Cannata, A., et al., Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study, J. Volcanol. Geotherm. Res. (2017), http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023
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Fig. 10. Comparison between temporal variations of Δv/v (a), eruptive activity (b) classified into short lasting intense Strombolian activity/lava fountain (red diamonds) and long lasting Strombolian/effusive activity (red rectangles), seismic RMS computed on the vertical component of the seismic signal recorded at ECPN station and filtered in the band 0.5–5.5 Hz (c), daily baseline changes for the sites EDAM-EMGL (d), daily number of VT earthquakes (histogram) and corresponding cumulative strain release (red area) (e), air temperature (f), atmospheric pressure (g), and snow load (h) in the reference period. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
The ground deformation time series was calculated by measuring the daily baseline changes for the sites EDAM-EMGL (see Fig. 1 for the map). Such a baseline, chosen because of the location in the westernnorthern flank at intermediate elevation, thus not affected by the sliding dynamics that characterizes the eastern flank (e.g. Mattia et al., 2015), was detrended to enhance the shorter-period variations (Fig. 10d). The baseline shows several oscillations, most of which are related to
inflation and deflation phenomena preceding and accompanying, respectively, the eruptions. Finally, as for the VT activity, we considered the VT earthquakes taking place at Mt. Etna (data from Gruppo Analisi Dati Sismici, 2016, http://sismoweb.ct.ingv.it/maps/eq_maps/sicily/). The boundaries considered to extract Mt. Etna VT earthquakes were: latitude ranging from 37.55°N to 37.90°N, longitude from 14.77°E to 15.23°E, depth
Please cite this article as: Cannata, A., et al., Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study, J. Volcanol. Geotherm. Res. (2017), http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023
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from 0 to 36.5 km (VT location maps at Mt. Etna can be found in Alparone et al., 2015, and Tuvè et al., 2015). In particular, we calculated the daily rate of occurrence and the corresponding cumulative released strain (Fig. 10e). The cumulative strain release plot shows a fairly constant rate, with several steps of different amplitudes corresponding to VT swarms taking place on different areas of the volcano edifice at various depths. Concerning the meteorological parameters, we took into account air temperature and pressure data recorded at a station located on the southern volcano flank (INAF-OA in Fig. 1) run by Istituto Nazionale di Astrofisica, Osservatorio Astrofisico di Catania (Fig. 10f, g). Finally, we also considered the snow load (Fig. 10h) calculated following the method by Larson et al. (2009) and Larson and Nievinski (2013) using the GPS signal acquired by the antenna installed in ESLN (Fig. 1). Among the GPS permanent stations deployed close to the summit area, we chose this station because of its reliability, result coherence, minimum changes in equipped instruments and uptime percentage during the years 2007–2015. In order to perform a quantitative comparison between the above cited time series and Δv/v, we applied the Wavelet Transform Coherence (hereafter referred to as WTC), evaluating the intensity of the covariance in time–frequency domain normalized in the range 0–1. Such a technique allows us to determine also information on the phase relationship between time series (e.g. Torrence and Compo, 1998; Grinsted et al., 2004). WTC has been used to compare time series in seismology (e.g. Cannata et al., 2010, 2013), as well as in other disciplines such as meteorology (e.g. Jevrejeva et al., 2003), epidemiology (e.g. Yang et al., 2008) and astrophysics (e.g. Donner and Thiel, 2007). When comparing time series, the advantage of using WTC instead of the simple Pearson's correlation is the ability to provide information not only in the time but also in the frequency domain at different observation scales. Since the wavelet transform has edge artefacts, it is useful to introduce a cone of influence (COI) in which edge effects cannot be ignored. Finally, also
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the disturbances affecting the considered time series have to be taken into account. Commonly, power spectra of geophysical time series are characterized by increasing power at lower frequencies and show many distinctive red noise features. Following previous studies (e.g. Grinsted et al., 2004; Jevrejeva et al., 2003), a 5% statistical significance level against red noise is considered in this work. As expected, the time series most related to Δv/v changes, in terms of significantly high values of coherence for long time intervals, is the air temperature at periods in between 200 and 400 days, confirming the seasonality of the medium changes (Fig. 11a). The two series, air temperature and Δv/v changes, turned out to be almost in anti-phase with delay (from the perfect anticorrelation) of about 60 days (temperature variations precede Δv/v changes), estimated by the phase difference shown by the arrows. In addition, the atmospheric pressure shows good coherence with the Δv/v changes with similar delays to the ones observed comparing air temperature and Δv/v (Fig. 11b). Moreover, the snow load also shows very high values of coherence with the Δv/v changes at period of 250–500 days with a delay of about 50 days (Fig. 11c). However, since in this case the Δv/v positive changes precede the snow-load increase, the crustal variations cannot be entirely due to such a parameter. Significant values of coherence at similar periods are also shown in seismic RMS and to a lesser extent in the EDAMEMGL deformation baseline (Fig. 11d, e). While the presence of a seasonal periodicity is expected for the meteorological time series, it is not so trivial for the other two. On the other hand, in spite of the considered frequency band 0.5–5.5 Hz, the computed seismic RMS is likely to be partially contaminated by the microseism noise. As for the EDAMEMGL baseline reflecting the ground deformation of the volcano edifice, it is probably affected by thermo-elastic changes or groundwater level variations with seasonal periodicity, as highlighted in California by Prawirodirdjo et al. (2006) and Ji and Herring (2012). In addition, also the WTC calculated between Δv/v and VT released strain shows significant high coherence values during December 2009
Fig. 11. Wavelet transform coherence between Δv/v and air temperature (a), Δv/v and atmospheric pressure (b), Δv/v and snow load (c), Δv/v and seismic RMS (d), Δv/v and baseline EDAM-EMGL (e), Δv/v and VT earthquakes released strain (f). The 5% significance level against red noise is shown as a thick black contour. The vectors indicate the phase difference between time series (a horizontal arrow pointing from left to right signifies in phase and an arrow pointing vertically upward means the first series lags the second one by 90°). The cone of influence (COI), where the edge effects might distort the picture, is shown as a lighter shade. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: Cannata, A., et al., Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study, J. Volcanol. Geotherm. Res. (2017), http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023
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(Fig. 11f). In the Δv/v plot a sharp decrease, that as aforementioned temporarily interrupted the positive winter semicycle, can be observed in mid-December 2009 (white arrow in Fig. 7a). In the VT earthquake plot, it is possible to note that in mid-December 2009 the largest strain release phenomenon of the entire 9-year-long analysed period took place (Fig. 10e). In particular, such a release was related to a swarm of VT earthquakes, composed of ~ 100 located events with magnitude ranging from 1.8 to 4.8, with epicenters in the north-western sector of the volcano and focal depth between 20 and 30 km (Fig. 12; Gruppo Analisi Dati Sismici, 2016, http://sismoweb.ct.ingv.it/maps/eq_maps/ sicily/). 4. Discussion and conclusions In this work, we analysed the seismic noise recorded at Mt. Etna by 18 stations during the interval 2007–2015 in the frequency band 0.1– 0.3 Hz, chosen to avoid contamination from volcanic tremor. Variations in time of Δv/v in the range −0.8 to 0.8% were found mostly affecting the stations located on the volcano flanks and summit region, and to a lesser extent the stations located at low altitudes (Figs. 7, 8, 9). Based on the investigated frequency content, the observed Δv/v medium changes took place from the surface to a depth of ~4.5–6.5 km. In order to identify the possible source mechanism of the observed medium changes, the Δv/v variations were quantitatively compared by WTC with volcanic and meteorological parameters (Figs. 10, 11). A significant relationship with the air temperature with seasonal periodicity was found, showing a delay of about 60 days from the perfect anticorrelation. As also stated by Meier et al. (2010), it has been shown by Prawirodirdjo et al. (2006) that changes in surface
temperature can induce thermo-elastic strain variations that persist down to depth of 15–20 km, well beyond the depth at which most of the observed changes in seismic velocity took place (from surface to ~ 4.5–6.5 km). The thermo-elastic strain origin of the Δv/v variations is also suggested by the phase shift with the air temperature, that, based on the computations performed by Richter et al. (2014), should be equal to tens of days, in agreement with the 60 days delay observed at Mt. Etna. Air temperature variations could also contribute to generate the different Δv/v change patterns observed at stations at distinct altitudes (Fig. 8). Indeed, although the annual temperature ranges are similar in the whole investigated area and equal to about 14–15 °C (Chester et al., 1986), only at high altitudes seasonal temperature variations imply freezing of the ground during winter. Such a phenomenon can produce time varying site effects, that can affect our Δv/v measurements at high-altitude stations. Among the analysed meteorological parameters, another factor that could cause distinct amounts of Δv/v changes at stations located on the volcano edifice with respect to the stations located at the volcano base is the snow load. Indeed, although it cannot entirely generate the observed Δv/v changes as the onsets of Δv/v increases precede the snowload increments, it can contribute to intensify the crustal seasonal variations at high altitudes. Indeed, as supposed also by Hotovec-Ellis et al. (2014) to explain different amount of Δv/v changes observed at distinct stations on Mt. St. Helens, at high altitudes the snow load increases the seismic velocity by closing cracks due to the increased surface loading. In addition, the atmospheric pressure showed a good coherence with the Δv/v changes. However, since the pressure mostly ranged in between 815 and 830 hPa (and then a variation of b2 kPa), it can be inferred that the effect of the pressure changes is secondary with respect
Fig. 12. Locations in map (a), sections (b, c) and 3D view (d) of the seismic VT swarm taking place at Mt. Etna during 18–21 December 2009. The dot colors indicate the depth of the earthquake locations. The dot size is proportional to the magnitude of the VT earthquakes (see the legend in the lower right corner of the map). The black concentric lines indicate the elevation contours at 250-m intervals. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: Cannata, A., et al., Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study, J. Volcanol. Geotherm. Res. (2017), http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023
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to the influence of the variable snow load, especially at very high altitudes (where thicker snow layers are expected). Indeed, the snow load of 1 m at ESLN, located at mid-altitude, is equal to ~3 kPa (with a snow density of 300 kg/m3); hence, the effect of the variable snow loads on the volcano summit is expected to be higher than the atmospheric pressure variations. Another meteorological parameter, that strictly depends on precipitation and then on snow load on Mt. Etna, is the meteoritic water diffusion at depth. Indeed, if the fraction of water in partially filled cracks changes, this can influence seismic velocity (e.g. Grêt et al., 2006; Wegler et al., 2009). At Mt. Etna, such a phenomenon takes mainly place during the spring, due to the snow melt. We cannot exclude that also the meteoritic water diffusion at depth plays a role in accelerating the Δv/v decrease during springtime. As for the sharp Δv/v decrease taking place in mid-December 2009 (white arrow in Fig. 7a), it is possible to note a clear time relationship with the largest seismic strain release phenomenon observed during the entire 9-year-long analysed period. In particular, such a release was related to a swarm of VT earthquakes, among which ~ 100 events were located and had magnitude ranging from 1.8 to 4.8, with epicenters in the north-western sector of the volcano at focal depth between 20 and 30 km (Fig. 12; Gruppo Analisi Dati Sismici, 2016, http:// sismoweb.ct.ingv.it/maps/eq_maps/sicily/). Medium Δv/v changes, taking place on volcanoes and time-related to earthquakes, have been sometimes reported in the literature. For instance, Hotovec-Ellis et al. (2014) observed a Δv/v decrease at Mount St. Helens at the same time as a M = 6.8 earthquake occurring 113 km away from the volcano. Such a variation was inferred to be due to a dynamic yet permanent response to shaking. Similarly, Battaglia et al. (2012) observed at Yasur volcano a medium velocity drop time-related to a M = 7.3 earthquake, that occurred 80 km away from the volcano summit. In this case, a dynamic stress transfer, causing opening of cracks and gas exsolution below the volcano summit, was inferred to be the origin of the Δv/v decrease. The mid-December 2009 VT swarm at Mt. Etna was interpreted by Patanè et al. (2013) to be the seismic response of local structures to the stress field induced from the ascent of magmatic fluids, that fed the 2011 lava fountain activities. Deep earthquakes in Etna's western sector are thought to be early markers of future volcanic activity, and this process has already been observed before several eruptions (Gresta et al., 1990; Bonaccorso et al., 1996; Patanè et al., 2004). On the basis of this and in agreement with the interpretation of Battaglia et al. (2012), the Δv/v decrease observed in mid-December 2009 could be due to ascent of fluids and gas exsolution taking place at the same time as the VT swarm. The other evident Δv/v change, taking place during the end of 2013 beginning of 2014 (yellow arrow in Fig. 7a), seems not to be associated with any considered volcanic and meteorological parameters. In spite of the many eruptive activities taking place during the analysed period (Fig. 10b), the mid-December 2009 Δv/v decrease was the only observed medium velocity change related to the volcano activity during 2007–2015. The variations due to the other intrusions, eruptions and VT swarms were likely to be smaller and affected only a part of the volcano edifice. Hence, such changes were probably below the level of noise and then not observable by our method, that is heavily influenced by seasonal changes affecting the whole volcano edifice up to several km of depth. A similar behaviour was also noted by Hotovec-Ellis et al. (2014) who clearly observed seasonal changes due to meteorological patterns, but did not detect variations due to magma injections. In our study, the lack of evidences of other relationships between Δv/v changes and the volcano activity was probably also caused by the low frequency band used to investigate the medium changes (0.1–0.3 Hz), corresponding with very long wavelength, in the order of several kilometers. Only meteorological variations (i.e. temperature changes, snow loading cycles) or important tectonic events and swarms could be so widespread to have measurable effects on the seismic velocity of the entire volcano edifice.
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Acknowledgements Wavelet coherence software was kindly provided by A. Grinsted. The authors thank the INGV-CT Gruppo Analisi Dati Sismici for providing earthquakes data. The authors are indebted to the technicians of the INGV–Osservatorio Etneo for maintaining the monitoring systems of Mt. Etna. The authors also acknowledge Istituto Nazionale di Astrofisica, Osservatorio Astrofisico di Catania for providing meteorological data. This work was partially supported by the Programma Nazionale Ricerche in Antartide (ICE-VOLC project, PNRA14_00011). A.C. thank the project AEOLUS funded by the Fondo di Ricerca di Base of Department of Physics and Geology, University of Perugia. We are grateful to two anonymous reviewers for their useful suggestions that greatly improved the paper. References Alparone, S., Maiolino, V., Mostaccio, A., Scaltrito, A., Ursino, A., Barberi, G., D'Amico, S., Di Grazia, G., Giampiccolo, E., Musumeci, C., Scarfì, L., Zuccarello, L., 2015. 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Please cite this article as: Cannata, A., et al., Monitoring crustal changes at volcanoes by seismic noise interferometry: Mt. Etna case of study, J. Volcanol. Geotherm. Res. (2017), http://dx.doi.org/10.1016/j.jvolgeores.2017.03.023