ORIGINAL ARTICLE

Relationship between soil CO2 flux and volcanic tremorat Mt. Etna: Implications for magma dynamics

Andrea Cannata • Gaetano Giudice • Sergio Gurrieri •

Placido Montalto • Salvatore Alparone • Giuseppe Di Grazia •

Rocco Favara • Stefano Gresta • Marco Liuzzo

Received: 23 April 2009 / Accepted: 3 November 2009 / Published online: 25 November 2009

� Springer-Verlag 2009

Abstract Large variations of the CO2 flux through the soil

were observed between November 2002 and January 2006 at

Mt. Etna volcano. In many cases, the CO2 flux was strongly

influenced by changes in air temperature and atmospheric

pressure. A new filtering method was then developed to

remove the atmospheric influences on soil CO2 flux and, at

the same time, to highlight the variations strictly related to

volcanic activity. Successively, the CO2 corrected data were

quantitatively compared with the spectral amplitude of the

volcanic tremor by cross correlation function, cross-wavelet

spectrum and wavelet coherence. These analyses suggested

that the soil CO2 flux variations preceded those of volcanic

tremor by about 50 days. Given that volcanic tremor is

linked to the shallow (a few kilometer) magma dynamics

and soil CO2 flux related to the deeper (*12 km b.s.l.)

magma dynamics, the ‘‘delayed similarity’’ between the

CO2 flux and the volcanic tremor amplitude was used to

assess the average speed in the magma uprising into the

crust, as about 170–260 m per day. Finally, the large amount

of CO2 released before the onset of the 2004–2005 eruption

indicated a deep ingression of new magma, which might

have triggered such an eruption.

Keywords Mt. Etna � Soil CO2 flux � Volcanic tremor �Cross correlation function � Cross-wavelet spectrum �Wavelet coherence

Introduction

Time variations of geochemical parameters and volcanic

tremor have important implications in monitoring volcanic

activity and understanding magma dynamics.

Over the last decades, among numerous geochemical

parameters, carbon dioxide (CO2) flux diffusely released

from soils has prompted scientific interest and several

studies have been carried out in order to understand the link

between volcanic activity and the space–time distribution

of this parameter on the flanks of volcanic edifices (Allard

et al. 1991). In general, CO2 gas species has some useful

characteristics for modelling the magma plumbing system:

(1) excluding water, CO2 is the most abundant gas dis-

solved in magma; but, with respect to water, it is more

conservative; (2) CO2 isotopes can be used as markers of

the fluid origin and to study the fluid–rock interaction

processes in the shallow crust; (3) due to its low solubility

in basaltic melts (Pan et al. 1991), CO2 is the first volatile

released from magma, thus strong increases in the CO2 flux

rate are in general related to the arrival of fresh, gas-rich

magma in the feeding conduits of basaltic volcanoes.

Volcanic tremor is a seismic signal with a non-impulsive

nature observed worldwide on most volcanoes (McNutt

1994). The dominant frequency in tremor waveforms has

been observed to vary between 0.1 and 10 Hz at various

A. Cannata (&) � P. Montalto � S. Alparone � G. Di Grazia

Istituto Nazionale di Geofisica e Vulcanologia,

Sezione di Catania, Piazza Roma 2, 95123 Catania, Italy

e-mail: cannata@ct.ingv.it

G. Giudice � S. Gurrieri � R. Favara � M. Liuzzo

Istituto Nazionale di Geofisica e Vulcanologia,

Sezione di Palermo, Via Ugo La Malfa 153,

90146 Palermo, Italy

P. Montalto

Dipartimento di Ingegneria Elettrica, Elettronica e dei Sistemi,

Universita di Catania, Viale Andrea Doria 6,

95125 Catania, Italy

S. Gresta

Dipartimento di Scienze Geologiche,

Universita di Catania, Corso Italia 57, 95129 Catania, Italy

123

Environ Earth Sci (2010) 61:477–489

DOI 10.1007/s12665-009-0359-z

volcanoes (i.e., Kubotera 1974). The source of volcanic

tremor consists of volumetric sources in which magmatic

fluids are dynamically coupled with the surrounding rock,

and the elastic radiation is the result of multiphase flow

through cracks and conduits (Chouet 1996). The relation-

ship between volcanic tremor and eruptive activity has

been studied in different volcanoes by various authors. In

many cases, increases of tremor amplitude were observed

to coincide with increased volcanic activity up to lava

fountaining (Pavlof: McNutt 1986; Hekla: Brandsdottir and

Einarsson 1992; Etna: Gresta et al. 1991; Cannata et al.

2008; Patane et al. 2008). Nevertheless, in other cases no

relationship between observable activity and tremor

amplitude were found, suggesting that time variations of

the tremor amplitude are related to the magma flow rate at

great depths inside the crust (Kilauea: Ferrazzini and Aki

1992; Etna: Di Grazia et al. 2006).

Mt. Etna volcano is one of the largest contributors of

magmatic gases, mainly CO2 and SO2, to the atmosphere

(Allard et al. 1991) and is characterized by a volcanic

tremor as a continuous background seismic signal (e.g.

Gresta et al. 1987; Alparone et al. 2007).

In the past, scientists investigated the relationships

between time variations of volcanic tremor and anomalies

of geochemical parameters at Mt. Etna. Leonardi et al.

(2000) considered the SO2 flux (measured by means of

COSPEC technique) emitted from the main vents of the

volcano during 1987–1995: they obtained a strong corre-

lation between the amplitude of volcanic tremor and the

SO2 flux during periods of enhanced volcanic activity. The

correlation between these signals was interpreted as due to

gas-induced turbulent magma flow in the shallow conduits

of the volcano producing high tremor amplitude. A cross

correlation analysis was also performed between soil

Radon activity and Reduced Displacement (RD) of the

volcanic tremor (a parameter used to quantify its intensity)

by Alparone et al. (2005) and revealed radon activity

increases about 58 ± 12 h before maximum values of

variations of RD of tremor collected during some parox-

ysmal summit activity. These data confirm the existence of

a temporal relation between anomalous gas emissions and a

specific type and/or level of summit activity at Mt. Etna.

The aim of this paper is the quantitative investigation of

the relationship between volcanic tremor amplitude and

CO2 flux from soil, after having developed and applied a

new suitable filter to remove the atmospheric effects from

the CO2 data.

Soil CO2 flux data

CO2 flux data series (uCO2) were acquired in the period

November 2002–January 2006 at an hourly sampling rate

by the remote station P78, located on the eastern flank of

the volcano (Fig. 1), and installed in November 2002.

uCO2 measurements were based on the dynamic concen-

tration method (Camarda et al. 2006a), which allows cal-

culating soil uCO2 by measuring CO2 concentration in a

gas mixture obtained by a specially designed probe and by

respecting specific measuring conditions such as the probe

insertion depth, the surface of the soil gas and of the air

inlets, and the pump flux. The influence of these parameters

and soil permeability on the uCO2 measurement has

recently been evaluated and the most convenient working

conditions for a specific probe geometry were also

described (Camarda et al. 2006b). Atmospheric tempera-

ture and pressure, rainfall, air humidity, wind speed and

direction were simultaneously measured together with soil

uCO2.

During the time period when the uCO2 data were col-

lected, the most important volcanic activity was an effusive

eruption lasting from 7 September 2004 to 8 March 2005

(Burton et al. 2005). It was characterised by outpouring of

degassed lava from two vents within Valle del Bove

(Fig. 1).

Atmospheric influences on soil CO2 flux emissions

Meteorological parameters, such as rainfall, air tempera-

ture and atmospheric pressure, can influence soil-degassing

rates (Hinkle 1990). Rainfall can cause an increase in the

moisture content in the upper part of the soil layer, espe-

cially in the case of low permeable media; this reduces soil

permeability thereby causing a temporary decrease of the

Fig. 1 Sketch map of Mt. Etna with location of the monitoring

stations: P78—soil CO2 flux (white square); ECPN, EMPL and

ECBD—seismic data (triangle)

478 Environ Earth Sci (2010) 61:477–489

123

gas emissions. Recent studies on this topic highlighted that

in the case of permeable media, such as ash in volcanic

areas, rainfall has a negligible influence on diffuse degas-

sing; only abundant rainfall can lead to appreciable varia-

tions in the soil gas rate and their effect is short-lived

(Camarda et al. 2006a). This behaviour was observed in the

uCO2 discussed in this paper where rainfall influences

were recognized in very few cases, typical of the low

rainfall conditions characterizing the south of Italy. Simi-

larly, the air humidity did not affect the uCO2; in any case,

the variations of this parameter strongly depend on the air

temperature, that we take into account to develop the

correction method. Finally, we also noted that wind speed

and direction had no effect on the uCO2.

According to these evidences, the analyses of the

meteorological effects discussed in this paper were focused

on the relationships between uCO2 and air temperature (T)

and atmospheric pressure (P) (Fig. 2a, c, d).

To evaluate the atmospheric influences on uCO2, a

spectral analysis was performed on the P, T and uCO2 time

series. The series were initially split into 85-day-long time

windows (2,048 measurements for each), overlapped by

75 days and the Fast Fourier Transform spectrum (FFT)

was calculated for each window. Then, the average spectra

were calculated to enhance the common spectral peaks to

each time window. All spectra showed peaks at the same

frequencies: 0.042, 0.084, 0.125, 0.167, 0.209, 0.250, 0.292

and 0.375 h-1 which correspond to the periods of 24.0,

12.0, 8.0, 6.0, 4.8, 4.0, 3.4 and 2.7 h, respectively (Fig. 3c).

Only three exceptions in the spectrum of T were found

(0.337, 0.417 and 0.459 h-1). Moreover, the uCO2 spec-

trogram (Fig. 3b) shows that the spectral amplitudes

strongly increase with the increment of uCO2 or, in other

words, the amplitude of the uCO2 variations due to the

atmospheric effects strictly depends on the uCO2 regimen.

In fact, the first period (November 2002–April 2003),

characterised by very low uCO2, showed very low spectral

amplitude; on the contrary, during the remaining period,

when a strong increase of the uCO2 was recorded, the

spectral amplitudes at all frequencies increased.

The strict relationship between uCO2 and P–T is also

confirmed by the results of the cross correlation analysis

performed on time windows of different length of the data

series (Fig. 4a, b). The analysis shows a high number of

periods characterized by negative values of cross correla-

tion between uCO2 and P (\ -0.6, black line and

Fig. 2 a uCO2 not-corrected

for atmospheric effects, b uCO2

corrected for atmospheric

effects, c air temperature (the

black line indicates the moving

average over 23 points),

d atmospheric pressure. The

black rectangle on the top of the

figure indicates the eruptive

period (7 September 2004–8

March 2005). The dotted linesand the Roman numbers in

a indicate the six time windows

representative of ‘‘volcanic

steady state conditions’’

(see text for details)

Environ Earth Sci (2010) 61:477–489 479

123

rectangles in Fig. 4a) and by positive values between

uCO2 and T ([0.6, black line and rectangles in Fig. 4b);

moreover, the number of correlated windows rapidly

decreases as the window time length increases. To remove

the highest frequency components which have no corre-

sponding components in the P and uCO2 spectra, T data

series was previously processed by moving averages; the

moving average of T over 23 h showed the highest number

of windows with cross correlation coefficient, calculated

with uCO2, larger than 0.6 (black line and rectangles in

Fig. 4b).

The main results of the spectral and cross correlation

analyses can be summarized as follows: (1) uCO2 was

influenced by the variations of T and P; (2) the amplitude

of the uCO2 variation due to atmospheric effects was

proportional to the uCO2 regimen; (3) the correlation can

be found for short periods because variations of the uCO2

regimen do not allow to compare long time periods of the

series. According to these evidences, we developed a filter

to remove the uCO2 component related to the atmospheric

variations.

The filtering procedure

In previous works, carried out in the same area and based

on discrete surveys of the uCO2 from soils (e.g., Giam-

manco et al. 1995; Bruno et al. 2001), the correction of the

uCO2 from the atmospheric effects was performed con-

sidering a linear relationship between air temperature and

uCO2. We tried to correct our uCO2 data by following this

method, but the obtained results were not good: the number

of correlated windows between uCO2 and T–P increased,

Fig. 3 a uCO2, b the

spectrogram and c the average

spectrum of the uCO2.

(A.U. = arbitrary unit)

Fig. 4 a Number of windows characterised by cross correlation

coefficient values lower than -0.6, calculated between not-corrected

uCO2 and P (black line and rectangles) and between corrected uCO2

and P (grey line and triangles). b Number of windows characterised

by cross correlation coefficient values larger than 0.6, calculated

between not-corrected uCO2 and moving average of T (black line and

rectangles) and between corrected uCO2 and moving average of T(grey line and triangles). The moving average of T was calculated

over 23 h

480 Environ Earth Sci (2010) 61:477–489

123

instead of decreasing. In fact, as previously shown, the

amplitude of the fluctuations of the uCO2, related to the

atmospheric effects, not only depends on the amplitude of

T and P variations but also on the uCO2 regimen. In this

regard, some general considerations can be made. uCO2

released in volcanic areas is mainly due to the gas exso-

lution processes occurring during the magma ascent toward

the surface. CO2 is characterized by very low solubility in

basaltic magmas and several authors suggest that the

largest content of magmatic CO2 is released at high pres-

sure (about 400 MPa corresponding to 12 km depth;

Caracausi et al. 2003). Gas is drained into the surface by

structural discontinuities which determine the presence of

anomalies in several parts of the volcanic edifice. In this

scenario, the pressure gradient between the magmatic gas

source and the surface is very high and the influence of

atmospheric conditions is entirely negligible. However, if

we consider the soil layer close to the surface, where the

pressure gradient is generally less than a few mbar, the

variation of atmospheric conditions can influence the uCO2

(as we observed for the data discussed in this paper). An

increase of P determines a decrease of uCO2 and the mass

difference is stored in the upper part of soils until the

pressure gradient conditions are re-established. Likewise, a

decrease of P determines an increase of uCO2 but the mass

difference in this case is supplied from the upper part of the

crust. On other hand, the link between air temperature and

uCO2 can be explained as follows: an increase in the air

temperature produces an increase in the soil temperature

(very shallow portion of soil) and then in the microbial

activity, with consequent greater production of organic

CO2. This model can explain the mainly negative and

positive values of cross correlation coefficient between

uCO2/P and uCO2/T, respectively. Moreover, it also takes

into account the dependence of the amplitude of the uCO2

variations, due to atmospheric effects, on uCO2 regimen

(as shown in ‘‘Atmospheric influences on soil CO2 flux

emissions’’). As a consequence of these general consider-

ations and of the results shown in the previous paragraph,

the measured uCO2 from soil (uCO2m) can be described by

the following equation:

uCO2m ¼ uCO2ðMÞ þ uCO2ðT ;uCO2ðMÞÞþ uCO2ðP;uCO2ðMÞÞ ð1Þ

where uCO2(M) is the uCO2 related to the magma exso-

lution processes, uCO2(T,uCO2(M)) and uCO2(P,uCO2(M)) are the positive or negative fluctuations related,

respectively, to the variations of T and P. Calculating the

last two terms of Eq. 1, uCO2(M) can be known.

In the filtering algorithm (see Table 1), we can distin-

guish three different phases: (1) ‘‘steady state’’ time win-

dow selection; (2) uCO2/T and uCO2/P dependence

analysis; (3) uCO2(M) computation.

Regarding the first phase, it was previously highlighted

that the amplitude of the uCO2 fluctuations, second and

third terms of Eq. 1, depends on the T and P variations but

also on the uCO2 regimen: at different uCO2 regimens, the

same T and P variations (hereafter called DT and DP,

respectively), determine different fluctuations of uCO2

(DuCO2). Therefore, first of all it was necessary to select

Table 1 Scheme summarising

the filtering procedure (see

‘‘The filtering procedure’’ for

details)

Environ Earth Sci (2010) 61:477–489 481

123

time windows of the uCO2 series (hereafter called STW)

representative of ‘‘volcanic steady state conditions’’ which

can be used to calculate the dependence of uCO2 from T

and P. STWs were found by a moving window procedure

(time length = 7.5 days) applied on the smoothed uCO2

data series (moving average of 151 terms): all the windows

showing uCO2/time slope \0.018 kg m-2 day-2 were

selected. Six STWs were found which are representative of

different uCO2 regimens (I–VI in Fig. 2a).

In the second phase, we calculated the inverse of P

(Pinv = 1/P); the respective uCO2m, Pinv and T mean val-

ues in each STW were subtracted from the original data

series to obtain stochastic signals oscillating around zero

(CO2_s, Pinv_s and T_s). The fluctuations of CO2_s, observed

in each STW, are due to both DP and DT. Therefore, in

order to quantify the relative influences of these atmo-

spheric parameters on CO2_s, and then to assign weights to

T and P, we followed this procedure: we assigned to the T

weights ranging between 0 and 1 (and then to the P weights

ranging between 1 and 0) with step of 0.1; therefore, 11

different corrected-uCO2 series (each of which represents a

couple of weights of P and T) were obtained and were

compared with P and T by using cross correlation function;

for each corrected-uCO2 series, and then for each couple of

weights, we calculated the total number of windows cor-

related with P and T (Fig. 5); the aim was to find the couple

of weights that minimise the number of correlated win-

dows. The couple T = 0.3 and P = 0.7 was chosen,

granting that uCO2m was corrected in the most effective

way (Fig. 5).

Then, the CO2_s data falling in each STW were multi-

plied by these weights (CO2_sP and CO2_sT series), thereby

splitting the variations of the CO2_s due to the DP and DT;

the root mean square (RMS) of the two new series and of

T_s and Pinv_s (CO2P,RMS, CO2T,RMS and TRMS, PRMS) were

also calculated for each STW with the following equation:

RMS ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

PHj

i¼1 lið Þ2

Hj

s

ð2Þ

where l represents CO2_sP, CO2_sT, T_s and Pinv_s and Hj is

the number of points of the jth selected window. In this

way, we calculated the ratios CO2T,RMS/TRMS and

CO2P,RMS/PRMS for every STW. We performed two

logarithmical regressions which gave a better result with

respect to the linear regression: in the first regression, we

considered the uCO2m mean values (hereafter called

uCO2m,mean) and the CO2P,RMS/PRMS values, obtained for

each STW, on the x and y axes, respectively (Fig. 6a); in

the second regression, we considered the uCO2m,mean and

the CO2T,RMS/TRMS values on the x and y axes, respectively

(Fig. 6b). The equations, representing the regression

curves, are the following ones (Fig. 6):

CO2P;RMS

PRMS

¼ m1 � ln /CO2m;mean

� �

þ b1 ð3Þ

CO2T ;RMS

TRMS¼ m2 � ln /CO2m;mean

� �

þ b2 ð4Þ

Therefore, we obtained the slopes (called m1 and m2 for

CO2P,RMS/PRMS and CO2T,RMS/TRMS, respectively) and the

Fig. 5 Total number of windows of corrected uCO2 correlated with

P (cross correlation coefficient \ -0.6) (a) and T (cross correlation

coefficient [0.6) (b) for each couple of weights assigned to P and T

Fig. 6 a Logarithmical regressions between uCO2m,mean and

CO2P,RMS/PRMS and b between uCO2m,mean and CO2T,RMS/TRMS

(see text for details)

482 Environ Earth Sci (2010) 61:477–489

123

y intercept (called b1 and b2 for CO2P,RMS/PRMS and

CO2T,RMS/TRMS, respectively) of these regression curves

(Fig. 6). We also calculated the R2 values, that indicate the

goodness of the logarithmical regression fit and were equal

to about 0.9 (Fig. 6).

We calculated uCO2(P,uCO2(M)) and uCO2(T,uCO2(M)), the second and the third terms of Eq. 1, with the

following equations, that were obtained by Eqs. 3 and 4:

uCO2ðP;uCO2ðMÞÞi

¼ m1 ln

PiþðK�1Þ=2

h¼i�ðK�1Þ=2uCO2mh

K

0

@

1

Aþ b1

2

4

3

5

1

Pi � Pm

� �

ð5Þ

uCO2ðT;uCO2ðMÞÞi

¼ m2 ln

PiþðK�1Þ=2

e¼i�ðK�1Þ=2uCO2me

K

0

@

1

Aþ b2

2

4

3

5

�PiþðG�1Þ=2

j¼i�ðG�1Þ=2Tj

G

0

@

1

A� Tm

2

4

3

5 ð6Þ

where i is the index of the time series; Pm and Tm are the

mean values of the P and of the T; K is assumed equal to

151; G is considered equal to 23. Finally, we calculated

uCO2(M) subtracting uCO2(T,uCO2(M)) and uCO2

(P,uCO2(M)) from uCO2m.

The corrected uCO2 resulted less correlated with the DT

and DP series than the original uCO2 signal; this is evident

in Fig. 4 where the number of windows with uCO2/P cross

correlation coefficient lower than -0.6 decreased by ca.

40% (Fig. 4a) and, similarly, the number of windows with

uCO2/T cross correlation coefficient larger than 0.6

decreased by ca. 30% (Fig. 4b).

The original uCO2 and the corrected uCO2 for the

atmospheric effects are plotted in Fig. 2a and b, respec-

tively: the most evident variations of the uCO2 did not

show strong changes after correction (such as November

2004–April 2005), while the most significant differences

fell in the periods April–August 2003 and May–September

2005 when the maximum deviations between the signals

was close to ±50%. This suggests that the main uCO2

variations are not due to the atmospheric effects but to the

volcanic activity.

Although this filtering procedure represents a step for-

ward in our ability to reduce the atmospheric influences

from the uCO2 time series, it has two drawbacks. Firstly, it

was not possible to entirely remove the atmospheric effects

from the uCO2 time series by this method. Moreover, to be

properly applied in a new site, this technique needs long

time series of recordings of uCO2 and atmospheric

parameters.

Volcanic tremor data

Tremor data used in this work were acquired from 25

November 2003 to 17 April 2005 at three seismic stations,

ECPN, EMPL and ECBD (Fig. 1) belonging to the per-

manent seismic network run by the Istituto Nazionale di

Geofisica e Vulcanologia, Sezione di Catania. These three

stations were located at distances ranging from 1 to 7.5 km

from the summit area and were equipped with a broadband

(40 s cut off period), 3-component Trillium seismometer

(NanometricsTM) in real time acquisition. The sampling

rate was 100 Hz and digitized data were transmitted by

satellite radio modem to the acquisition laboratory in

Catania.

In order to study the features of the volcanic tremor

during the aforementioned period, we analysed: (1) the

overall spectral amplitude and (2) the spectral features.

Overall spectral amplitude

Defined as the cumulative of spectral amplitudes of the

seismic signal within a frequency band, the overall spectral

amplitude (OSA, Fig. 7) was calculated as follows: the

long tremor time series, recorded at the vertical component

of the three used stations, was split into roughly 40-s-long

non-overlapping windows (4,096 points); for each window,

we calculated the spectra by a FFT algorithm, with a fre-

quency resolution of 0.025 Hz; the hourly average spectra

(90 spectra for each hour) were obtained and the OSA was

calculated by the following equation:

OSA ¼X

f 2

k¼f 1

Sk ð7Þ

where S is the hourly average spectrum and f1 and f2 are

the boundaries of the chosen frequency band (in this case:

f1 = 0.5 Hz and f2 = 10 Hz).

Generally, all the stations used showed very similar

trends of the OSA. During November 2003–February 2004

the volcanic tremor was characterised by very low ampli-

tude. Between February 2004 and September 2004, two

gentle bell-shaped fluctuations can be noted, bounded by

three minima falling in February, May and September. The

beginning of the eruption, occurring on September 7 2004,

coincides with the last one. Successively, at the end of

September the OSA increased and the time period October

2004–March 2005, roughly coinciding with the effusive

activity, was characterised by higher amplitude values and

by the main variations of the OSA. In February 2005, the

maximum amplitude values of the entire studied period

were reached. Finally, during the remaining time period the

volcanic tremor showed amplitude values comparable to

ones observed during November 2003–February 2004.

Environ Earth Sci (2010) 61:477–489 483

123

Spectral analysis

In order to study the spectral features of the volcanic tre-

mor recorded during November 2003–April 2005, we cal-

culated daily average spectra, by averaging 24 hourly

average spectra (obtained as described in ‘‘Overall spectral

amplitude’’). Then, by these spectra the spectrograms of

the vertical component and the daily dominant spectral

peak frequencies of the three components were obtained

(Fig. 8).

Most of the dominant spectral peak frequencies are

comprised in the band 0.2–6 Hz at ECPN and 0.2–2 Hz at

EMPL and ECBD. As ECPN is much closer to the summit

area (about 1 km) than EMPL and ECBD (about 7 km),

this difference of the frequency content may be due to the

propagation effects. On the contrary, the similarity of the

dominant spectral peak frequency obtained at EMPL and

ECBD may be attributed to source effects. ECPN showed

higher variability of the dominant spectral peak frequency

than EMPL and ECBD, probably related to the very shal-

low volcano dynamics; moreover, ECPN also showed

higher variability among the three components.

Taking into account only ECBD and EMPL, showing

the long-term spectral variations of the volcanic tremor

more clearly than ECPN, we can distinguish some time

periods characterised by different spectral content. A first

period, lasting from November 2003 to February 2004 and

showing very low OSA values, was characterised by low

values (\0.3 Hz) of dominant spectral peak frequency,

probably related to sea microseism. Successively, an

increase of dominant spectral peak frequency occurred: the

vertical components showed almost stable values of about

0.7 and 1.1 Hz at ECBD and EMPL, respectively, until

September 2004; during this time interval the horizontal

components were characterised by values of 0.5–1.1 Hz. A

slight increase of the dominant spectral peak frequency of

the horizontal components is also evident in May 2004. In

September 2004, an increase was observed in almost all the

components, followed by a decrease in March 2005; these

two spectral variations coincided with the beginning time

and the end time of the eruption.

Relationship between volcanic tremor and soil CO2 flux

To investigate the relationship between the volcanic tremor

OSA and the uCO2 (both corrected and not-corrected for

atmospheric effects), the OSA series of EMPL station was

chosen; this station was preferred because it recorded the

longest data set. Therefore, in order to compare the two

time series we used two different methods based on the

cross correlation function and the wavelet transform.

Following the first method, the OSA series of EMPL

was divided into different sets of windows, each of which

were compared by cross correlation analysis with the uCO2

series. The features of each OSA set were: time length of

the windows ranging between 80 and 380 days with a

10-day-long step (31 different values), window overlapping

equal to 50, 66 or 85% of the respective time length (three

different values): in all, we considered 31 times three (that

is 93) window sets. Figure 9 shows an example of the

resulting cross correlation analysis. The highest values of

the cross correlation and the most stable time lag values

were found with the uCO2 data series corrected for the

atmospheric influences. We selected for each OSA window

set the window characterised by the highest cross correla-

tion coefficient and plotted the cross correlation coeffi-

cients (Fig. 10a, c, e) and the time lags (Fig. 10b, d, f)

versus the length of the OSA windows. The OSA window

sets with an overlap of 85% showed the highest values of

cross correlation coefficient (Fig. 10e, f); in particular, the

windows characterised by length greater than 160 days

depicted the most stable time lag values of the volcanic

tremor with respect to the uCO2 (Fig. 10f). The mean

values and the standard deviation of the time lags calcu-

lated with these OSA data sets (overlap equal to 85% and

window length greater than 160 days) resulted equal to

56.1 and 3.5 days, respectively, and the corresponding

Fig. 7 Overall spectral amplitude (OSA) of the vertical component

of the signal acquired by the three seismic stations indicated in the

plots. The overall spectral amplitude was obtained by integrating

the hourly averages of spectra (90 spectra of 40.96 s for each hour), in

the frequency range 0.5–10 Hz. The black solid lines indicate the

moving average over 151 h, sliding 1 h. The black dashed linesindicate the beginning and the end of the eruption

484 Environ Earth Sci (2010) 61:477–489

123

cross correlation coefficient values were larger than 0.6.

This value was considered by Leonardi et al. (2000) as the

minimum meaningful value for the cross correlation coef-

ficient. To test the stability of our results, we applied the

so-called jackknifing procedure (e.g., Efron 1982) and the

standard deviation values obtained from the time lag group

of 22 values, resulted equal to 1.3 days. Therefore, not-

withstanding the not very high values of the cross corre-

lation coefficient, the steadiness of the obtained time lag

values suggested that such results were meaningful.

The second method is based on the wavelet transform

that is used to analyze nonstationary time series (Foufoula-

Georgiou and Kumar 1995). The continuous wavelet

transform (CWT) of time series is its convolution with the

Fig. 8 Normalised

spectrograms of the vertical

component and dominant

spectral peak frequency of the

three components of the signals

recorded by the stations

reported in the plots. These

results were obtained by using

daily average spectra. The blackdashed lines indicate the

beginning time and the end time

of the eruption

Fig. 9 Cross correlation between the time series of the uCO2,

corrected for the atmospheric effects, and a 310-day-long window of

the OSA of the volcanic tremor at EMPL station (29 May 2004 to 04

April 2005). The lag value is negative when the uCO2 precedes the

volcanic tremor

Environ Earth Sci (2010) 61:477–489 485

123

local basis functions, called mother wavelets, which can be

stretched and translated with flexible resolution in both

frequency and time (Jevrejeva et al. 2003). When using

wavelets for feature extraction purposes the Morlet wavelet

is a good choice, since it provides a good balance between

time and frequency localization, as well as information

about phase (Grinsted et al. 2004). For analysis of the

covariance of two time series two tools based on the

wavelet transform can be used: cross-wavelet spectrum and

wavelet coherence. The former is a measure of the com-

mon power of two time series, while the latter evaluates the

intensity of the covariance in time–frequency domain

normalized in the range 0–1 (Jevrejeva et al. 2003). Both

the aforementioned techniques can also provide informa-

tion on the phase relationship between time series. The

wavelet transform has edge artefacts because the wavelet is

not completely localized in time. It is therefore useful to

introduce a cone of influence (COI) in which edge effects

cannot be ignored (Grinsted et al. 2004). Finally, also the

noise affecting our data has 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 pre-

vious studies (e.g. Grinsted et al. 2004; Jevrejeva et al.

2003) 5% statistical significance level against red noise is

considered in this study. The cross-wavelet spectrum and

the wavelet coherence between the OSA of EBEL station

and the uCO2 are reported in Fig. 11c and d, respectively.

Both these analyses indicate a good similarity of the two

time series at long period (over 80 days). In particular, at

such periods the coherence wavelet plot shows coherence

values greater than 0.8. Moreover, the phase angle of cross-

wavelet spectrum within the 5% significant regions and

outside the COI has a mean value of -143 ± 5�(where ± indicates the circular standard deviation). If we

consider a period of 3,000 h, the afore mentioned phase

angle roughly corresponds to -50 ± 2 days. This time lag,

indicating that uCO2 time series precedes the tremor one,

is consistent with the value obtained by the other method

based on the cross correlation function.

Discussion

The comparison between time variations of geophysical

and geochemical parameters can provide information about

magma dynamics within volcanoes. Generally, variations

in atmospheric conditions determine fluctuations of the

amplitude of geochemical parameters such as water tem-

perature, soil degassing and others. For this reason, an

accurate data analysis should be carried out before any

comparisons with other monitored parameters in order to

reduce the atmospheric effects and, at the same time,

amplify the component related to the volcanic activity. By

performing the cross correlation analysis and comparing

the spectral features, we noted that the uCO2 data series

was affected by such atmospheric interferences, in partic-

ular, due to air temperature and pressure variations. The

relationship between variations of atmospheric parameters

and fluctuations of uCO2 was not linear; in particular,

variations of atmospheric parameters of similar amplitude

determined large fluctuations of the soil degassing when

the CO2 soil regimen was high and vice versa. A suitable

filtering procedure (Table 1) was developed to reliably

reduce such influences; the procedure was not tuned for a

specific site but can be used for filtering any uCO2 flux

series acquired in other sites of other volcanoes.

Large variations of the uCO2 through the soil were

observed between November 2002 and January 2006 at Mt.

Etna. During this period, the most important volcanic

activity was an effusive eruption, lasting from September

7, 2004 to March 8, 2005. Petrographic analyses performed

on the collected lava samples suggested that the erupted

magma had resided in the shallow feeding system since

2000–2001 (Corsaro and Miraglia 2005). A few days after

the beginning of this eruption, we observed a general

increase and a larger variability of the OSA of the volcanic

tremor which reached maximum values in February 2005

and decreased in March 2005, at the same time as the end

of the eruption. Moreover, a slight variation of the spectral

content of the volcanic tremor was observed during the

eruption, suggesting a tremor source variation; in fact, the

Fig. 10 a, c, e Cross correlation

coefficient and b, d, f) time lag

between the uCO2, corrected

for the atmospheric effects, and

the OSA of the volcanic tremor

at EMPL station plotted versus

the window length. In a, b; c, dand e, f the window overlaps are

equal to 50, 66 and 85%,

respectively

486 Environ Earth Sci (2010) 61:477–489

123

tremor spectral content depends on the size of the resonant

structure (Chouet 1996) and on the physical–chemical

features of the fluid in the structure (Morrisey and Chouet

2001). During this period, the centroid of the tremor source

was located at elevation ranging ca. 1,700–2,400 m a.s.l.

(Di Grazia et al. 2006). On other hand, the uCO2 data

showed an increasing trend since July 2004, reaching the

highest value in December 2004. The cross correlation

analysis, the cross-wavelet spectrum and the wavelet

coherence, performed between the OSA of the volcanic

tremor and the uCO2 corrected for the atmospheric effects,

suggested that the uCO2 time series precedes the tremor by

about 50 days.

On the basis of these indications, we infer the fol-

lowing considerations. The ingression of new magma in

the deep volcanic feeding system and its subsequent

ascent toward the surface determined an increase of the

CO2 release. As suggested by previous studies (Caracausi

et al. 2003), at depth of about 12 km b.s.l. the largest

amount of CO2 initially dissolved in basaltic magmas is

exsolved and can determine anomalous uCO2 rate both

from the vents and the slopes of the edifice. The ascent of

new magma probably pushed ‘‘old’’ magma, already

residing at shallow depth (Burton et al. 2005), to rise up

through cracks and conduits located at very shallow depth

(about 2 km a.s.l.; Di Grazia et al. 2006). This caused

the increases in tremor amplitude and variations of its

spectral content. Based on this scenario, and assuming

that the magma ascent was a continuous process, the time

lag between the two time series calculated by cross cor-

relation analysis and confirmed by cross-wavelet spec-

trum, allow us to estimate the average speed of the

magma ascent as 0.002–0.003 m s-1 that means about

170–260 m day-1. This estimation does not take into

account the differences between new and old magma

bodies; however it is consistent with some previous esti-

mations carried out at Mt. Etna volcano (Allard 1997;

Caracausi et al. 2003).

Fig. 11 a overall spectral

amplitude (OSA) of the vertical

component of EBEL station (the

black solid line indicates the

moving average over 151 h,

sliding 1 h) and b uCO2

corrected for atmospheric

effects. c Cross-wavelet

spectrum and d wavelet

coherence between the time

series in a and b. The 5%

significance level against red

noise is shown as a thickcontour. The vectors indicate

the phase difference between

uCO2 and OSA (a horizontalarrow pointing from left to right

signifies in phase and an arrowpointing 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. The

black dashed lines indicate the

onset and the end of the eruption

Environ Earth Sci (2010) 61:477–489 487

123

Concluding remarks

In this paper, we analysed the soil uCO2, measured at Mt.

Etna during November 2002–January 2006, and the vol-

canic tremor, recorded from November 2003 to April 2005.

The two main results yielded by our study can be sum-

marised as follows:

1. A non-linear relationship between soil uCO2 and

atmospheric parameters was found, and consequently a

filtering procedure was developed, that enables us to

reduce such influences on the uCO2 time series.

2. The comparison of soil uCO2 and volcanic tremor

amplitude (OSA) allowed finding a link between

changes in the dynamics of the deep volcanic system,

highlighted by the former parameter, and the shallow

ones, evidenced by the latter parameter.

Finally, this multidisciplinary approach, based on the

simultaneous observation of geochemical and geophysical

data, proves to be a promising tool for volcano monitoring

and investigation.

Acknowledgments We acknowledge two anonymous reviewers for

their constructive comments that enabled us to improve the manu-

script. Crosswavelet and wavelet coherence software were kindly

provided by A. Grinsted. This research has been supported by grants

INGV-DPC 2005-2007 (Project V3_6, RUs 17 and 18).

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