Download Seasonal cycles of seismic velocity variations detected using coda

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Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx
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Journal of Volcanology and Geothermal Research
j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / j v o l g e o r e s
Seasonal cycles of seismic velocity variations detected using coda wave
interferometry at Fogo volcano, São Miguel, Azores, during 2003–2004
Francesca Martini a,⁎, Christopher J. Bean a,d, Gilberto Saccorotti b, Fatima Viveiros c, Nicolau Wallenstein c
a
Seismology and Computational Rock Physics Lab., School of Geological Sciences, University College Dublin, Belfield, Dublin 4, Ireland
Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, Via U. della Faggiola, 32, 56126 Pisa, Italy
Centro de Vulcanologia e Avaliação de Riscos Geológicos, Universidade dos Açores, Edifício do Complexo Científico, Rua da Mãe de Deus, Apartado 1422, 9501-801 Ponta Delgada,
Açores, Portugal
d
Complex and Adaptive Systems Lab., (CASL), University College Dublin, Dublin 4, Ireland
b
c
a r t i c l e
i n f o
Article history:
Received 16 June 2008
Accepted 9 January 2009
Available online xxxx
Keywords:
velocity changes
rainfall
volcano seismicity
triggered seismicity
Azores archipelago
a b s t r a c t
Fogo volcano is an active central volcano, with a lake filled caldera, in the central part of São Miguel Island,
Azores, whose current activity is limited to hydrothermal manifestations such as active fumarolic fields,
thermal and CO2 cold springs and soil diffuse degassing areas. It is affected by important active tectonic
structures, with high seismic activity and practically continuous micro-seismicity.
A recurrent feature from the seismicity observed in volcanic regions is the occurrence of clusters of similar
earthquakes, whose origin can be attributed to the repeated action of a similar source mechanism at the
same focal area. Doublets/multiplets were identified in this study within a catalogue of small magnitude
(usually b 3) volcano tectonic events recorded in 2003–2004 by a selection of stations around Fogo volcano.
All events have been cross-correlated and pairs whose waveforms exhibited a cross-correlation coefficient
equal to or higher than 0.9 were analysed using the coda-wave interferometry technique. Subtle velocity
variations found between events highlight a seasonal cycle of the velocity patterns, with lower velocity in
winter time and higher velocity during summer months. Those results, together with quantitative differences
between the same doublets at different stations, exhibit an excellent correlation with rainfall.
A seasonal effect can also be broadly seen in the seismicity occurrence, and some of the swarms recorded
over the two year period occur during the wettest season or close to episodes of abundant (above average)
rainfall. Moreover, temporal and spatial analysis of several swarms highlighted the lack of any mainshock–
aftershock sequence and organized migration of the hypocenters. This is suggestive of a very heterogeneous
stress field. Vp/Vs is found to be lower than usually observed in volcanic areas, an occurrence likely related to
the presence of steamy fluid associated with the geothermal system. Taken together, these observations suggest
that pore pressurisation plays a major role in controlling a considerable part of the recorded seismicity. The
geothermal fluids around Fogo massif have been identified as derived from meteoric water, which infiltrates
through Fogo Lake and the volcano flanks and flows from south to north on the northern flank.
All those elements seem to point to a role played by rainfall in triggering seismicity at São Miguel, possibly
through pressure changes at depth in response to surface rain and/or an interaction with the geothermal system.
© 2009 Elsevier B.V. All rights reserved.
1. Introduction
The Agua de Pau/Fogo Massif, usually called Fogo volcano, occupies
the central part of São Miguel Island, Azores, located near the Mid
Atlantic Ridge at the triple junction between the North American,
Eurasian and African lithospheric plates. Fogo is an active central volcano with a lake filled caldera, composed of a succession of basaltic to
trachytic lava flows, trachytic domes, cinder cones, pyroclastic flows,
⁎ Corresponding author. Tel.: +353 1 716 2137; fax: +353 1 2837733.
E-mail addresses: [email protected] (F. Martini), [email protected]
(C.J. Bean), [email protected] (G. Saccorotti), [email protected]
(F. Viveiros), [email protected] (N. Wallenstein).
lahars, pumice and ash deposits (Moore, 1991; Wallenstein, 1999:
Wallenstein et al., 2007a). The earliest stage of its activity has been
dated at 280,000 ± 140,000 years BP (Muecke et al., 1974) from a
borehole sample on the northern flank. There have been at least four
eruptions in the last 5000 years from this centre (Booth et al., 1978,
Wallenstein, 1999), mostly explosive, intercalated with more effusive
eruptions on satellite centres on its flanks. The most recent eruptions
occurred in 1563 A.D. (Weston, 1964) with a sub-plinian intracaldera
eruption followed by a basaltic flank eruption (Walker and Croasdale,
1971; Wallenstein, 1999); current activity is limited to hydrothermal
manifestations such as active fumarolic fields, thermal and CO2 cold
springs and soil diffuse degassing. The hydrothermal manifestations
of Fogo volcano are mainly present in the northern flank of the
0377-0273/$ – see front matter © 2009 Elsevier B.V. All rights reserved.
doi:10.1016/j.jvolgeores.2009.01.015
Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo
volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015
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F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx
volcano: the main fumarole grounds can be observed in Caldeira
Velha, Pico Vermelho and Caldeiras da Ribeira Grande. Their location
is associated with the NNW–SSE fault system that defines the socalled Ribeira Grande graben.
The volcano lies at the intersection of the main regional tectonic
structures with NW–SE, NNW–SSE and WNW–ESE directions, and
circular and sub-circular faults associated with the caldera collapses
(Wallenstein, 1999). It is therefore affected by important active tectonic structures: it is characterized by high seismic activity and practically continuous micro-seismicity, concentrated in very frequent
swarms (Nunes, 1991). In the period analysed within this study (2003–
2004) the swarms are characterised by hundreds of low magnitude
(Md b 3) events occurring over a few hours (Silva et al., 2005; Wallenstein
et al., 2007b). Understanding the origin of this type of seismicity is
of primary importance. In particular, it is crucial to understand whether
the periods of increased seismicity are related to episodes of magmatic
unrest, as it would provide a substantial contribution to the assessment
of volcanic hazards on the island.
As a first step in this direction, in the present study we analyse
families of repeating seismic events identified within the 2003–2004
seismic catalogue recorded in an areas of approximately 20 × 20 km
centred on Fogo volcano. The aim is to monitor velocity changes
within the volcano structure that might be associated with volcanic
activity and/or external parameters influencing the volcanic system.
2. Monitoring of volcanic activity in São Miguel Island: data available
to this study
Due to its peculiar geodynamic setting, the Azores archipelago is
the location of important seismo-volcanic activity. The islands were
affected by several major earthquakes and about 30 volcanic eruptions
over the past five hundred years (Weston, 1964; Silveira et al., 2003).
Fig. 1. a) location of São Miguel, Azores, b) map of São Miguel Island, with location of the permanent CO2 soil flux stations; fumaroles location and geographical references are also
marked, c) seismic stations in the central part of São Miguel, around Fogo volcano, including those from the SIVISA permanent seismic network and those deployed during the field
experiment in April–July 2003.
Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo
volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015
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F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx
Presently the Centro de Vulcanologia e Avaliação de Riscos Geológicos (CVARG), a multidisciplinary research unit within the
University of Azores and a member of the World Organization of
Volcano Observatories (WOVO), monitors the seismic and volcanoseismic activity, fluids geochemistry, ground deformation and volcano-magmatic processes in the Azores.
On S. Miguel Island, the permanent seismological network consists
of 19 stations (16 analogue, with 3-D and 1-D short period sensors,
and 3 digital, 2 with 3D short period and one with broadband sensors),
located throughout the island (Fig. 1) and telemetred to the CVARG
recording centre in Ponta Delgada. Nine 3D-1 Hz short period
analogue permanent stations are deployed around Fogo Volcano.
Additionally, a temporary network was deployed from April to mid
July 2003, in the framework of the EU project e-Ruption (Saccorotti
et al., 2004; Fig. 1). The sparse network consisted of 14 stations equipped
with both short period and broad-band seismometers, and three dense
arrays of short period instruments. The aim of the field experiment
was to obtain a continuous record of the seismicity to characterise the
present level of activity of Fogo and its neighbouring Furnas volcano
(on the eastern part of São Miguel) and to obtain broadband recordings
of distant sources for investigating the presence of shallow velocity
anomalies beneath Fogo volcano, possibly associated with the existence of a shallow magma chamber (Saccorotti et al., 2004; Silva, 2004;
3
Silva et al., 2005; Zandomeneghi, 2007; Zandomeneghi et al., 2008;
Bonagura et al., Characteristic of recent seismicity at Central São
Miguel Island, Azores, Manuscript in preparation, hereinafter referred
to as Bonagura et al., in preparation).
Regular fumarole sampling, soil CO2 concentration/flux and soil
radon measurements are performed by the CVARG Gas Geochemistry
Unit. Four soil CO2 flux continuous monitoring stations are running
at São Miguel Island, based on the accumulation chamber method
(Chiodini et al., 1998). One of those, set up in 2002, is on the northern
flank of Fogo volcano, within the Pico Vermelho geothermal area (Fig. 1).
The station also includes additional sensors to record (on an hourly
basis) information on meteorological and environmental parameters
such as barometric pressure, air temperature, air humidity, wind speed
and direction, rainfall, soil water content and soil temperature (Ferreira
et al., 2005). Monthly rainfall data are also provided by a series of udometric stations managed by the DROTRH (Direcção Regional do Ordenamento do Território e Recursos Hídricos; Fig. 12).
The influence of external factors on soil CO2 flux at the permanent
stations is gauged by applying multivariate regression analysis (Draper
and Smith, 1981). The results obtained show that the meteorological
variables play different roles in the control of the gas flux depending on
the selected monitoring site (Viveiros, 2003; Viveiros et al., 2003;
Viveiros et al., 2008). Meteorological variables may explain between
Fig. 2. Time evolution of the daily number of events (top) and corresponding duration magnitudes (bottom).
Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo
volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015
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F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx
18.1% and 50.5% of the soil CO2 flux variations at the permanent stations
installed in the island of São Miguel. At the Fogo station (GFOG1), only
18.1% of the gas oscillations are due to the external monitored variables
including soil temperature, soil water content and rainfall (Viveiros et al.,
2008). The remaining oscillation is likely explained by the background
hydrothermal variations and the influence of other unmonitored
variables. The presence of a geothermal power plant in the vicinities
of Fogo permanent flux station is probably responsible for some of the
gas flux oscillations, however those activities are not being monitored
and consequently are not included in the statistical analysis.
Recognizing and filtering environmental influences is an important
prerequisite to a reliable application of soil CO2 flux monitoring prior
to establishing correlations with seismic and/or volcanic activity.
3. The seismic data
This study involves the analyses of the seismic data recorded by
the six seismic stations located on and around the Fogo massif (Lagoa
de Fogo—LFA, Coroa Mata—PMAT, Monte Escuro—MESC, Vila Franca do
Campo—VIF, Chã da Macela—CML and Pico Vermelho—PVER; Fig. 1) in
São Miguel by the seismic network SIVISA (Azorean Seismic Surveillance System) during 2003 and 2004. More than five thousand seismic
events were recorded by the network in this period (Fig. 2). Within
this dataset, 2067 good signal/noise ratio events from the six stations
are used in this study. P and S waves were manually picked for these
events.
In addition, during the three months field survey in 2003 (Saccorotti
et al., 2004; Silva, 2004), more than a thousand earthquakes were detected by the temporary network. Most of this seismicity is associated
with an intense swarm that occurred on April 26th, 2003, in which more
than 300 micro-earthquakes (Md b 3.5) occurred over a period of a few
hours. The high number of additional temporary stations deployed
around Fogo volcano provides a very well constrained sub-set of data,
allowing for precise hypocentral solutions for about 500 events in this
3 months period, and a study of the temporal and spatial evolution of
the seismicity. As seen later, such detail can not be achieved for the
entire 2003–2004 dataset due to the low energy of the seismicity and
the low number of stations at which the signals were recorded.
4. Locations of seismicity
4.1. Method and velocity model
The hypocentral location of the seismicity has been obtained using
the probabilistic approach of Lomax et al. (2000). In this method,
minimum-misfit hypocenters are determined based upon a complete
grid search acting over a pre-computed library of travel times calculated via a finite-difference approximation to the Eikonal equation.
These were computed using a slightly modified version (explained
below) of the minimum 1-D velocity model obtained by tomographic
inversion of the subset of the data recorded between 1st April and 15th
July 2003 by Zandomeneghi (2007). The true 3-D velocity deviations
from the minimum 1-D model are evenly distributed with zero mean.
Table 1
Velocity model used for the location procedure. Modified version of 1-D minimum
model from Zandomeneghi (2007)
Layer
Layer
Layer
Layer
Layer
Layer
Depth
(km)
Vp
(km/s)
Vp
gradient
Vs
(km/s)
Vs
gradient
Density
Density
gradient
−1.0
1.0
2.0
3.0
5.0
12.0
2.58
3.83
5.26
5.46
6.78
7.80
0.00
0.00
0.00
0.00
0.00
0.00
1.53
2.28
3.13
3.25
4.03
4.64
0.00
0.00
0.00
0.00
0.00
0.00
2.7
2.7
2.7
2.7
2.7
2.7
0.0
0.0
0.0
0.0
0.0
0.0
Fig. 3. Hypocentral solutions for the 2003–2004 seismicity recorded at stations LFA,
PMAT, MESC, VIF, CML and PVER. The locations were obtained using the probabilistic
approach of Lomax et al. (2000) with a modified version of the minimum 1-D velocity
model obtained by tomographic inversion of the subset of the data recorded between
1st April and 15th July 2003 (Zandomeneghi, 2007). Solutions obtained with a minimum
of four P phases only were retained.
The average value of the 3-D model across each velocity layer is effectively identical to the minimum 1-D model for the same layer.
From the minimum 1-D velocity model obtained by Zandomeneghi
(2007) and Zandomeneghi et al. (2008), the top layer has been extended to
−1 km a.s.l., so that the station elevation is taken into account. Layers
between 5 and 9 km depth were incorporated in one layer only as their
velocities only differ by a few m/s. The model is listed in Table 1.
4.2. Relocation of the 2003–2004 data recorded by the permanent network
Only solutions obtained with a minimum of four P phases were
retained. This yielded 1030 solutions out of the initial 2067 events
from the entire 2 year dataset. The resulting hypocenters (Fig. 3) have
a sparse distribution, with most of the seismicity clustered on the
eastern flank and E of Fogo caldera. The hypocentral depths range
from 0 to 10 km, with most of the seismicity concentrated between 2
and 7 km. This cut off in depth might be related to the brittle–ductile
transition associated to the high geothermal gradient observed in the
area (Dawson et al., 1985).
Seven major swarms have been identified in the 2 year period
analysed (Table 2). The earthquake magnitudes rarely exceed 2.5 Md.
These data suggest that the different swarms originate substantially in the same seismogenic region. For individual swarm activity,
no mainshock–aftershock sequence nor migration of the hypocenters
over time was observed. Some migration towards shallower locations
can be observed in some periods for different swarms (Wallenstein
et al., 2007b).
Table 2
Seven major swarms identified in the period 2003–2004
Swarm no.
Date
No. of days from
01-01-03
Number of events
1
2
3
4
5
6
26 April 2003
7 September 2003
7–8–9 November 2003
21–22 January 2004
10 June 2004
5–6–(7–8)–9
September 2004
30–31 October 2004
116
250
311–312–313
386–387
527
614–615–
(616–617–)618
669–670
1327
298
121 + 293 + 58
136 + 512
180
142 + 98 +
(42 + 19) + 125
141 + 221
7
Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo
volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015
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Fig. 4. Hypocentral solutions for the events of the 26th April 2003 swarm, obtained
retaining a minimum of four P phases picked at a) the six permanent stations LFA, PMAT,
MESC, VIF, CML and PVER, and b) with additional stations deployed in the Fogo area
during the field experiment between April and mid July 2003.
4.3. Relocation and analyses of the April 2003 data recorded by the
permanent and additional temporary networks
Due to the additional sensors deployed during the three months
field experiment in 2003, the events belonging to the April 26th, 2003
5
swarm were recorded at a higher number of stations around the Fogo
massive, allowing a more precise location of a higher number of events.
In Fig. 4, the swarm hypocentral solutions obtained using only the six
permanent network stations on Fogo and using also the additional
stations deployed during the 2003 field experiment are plotted. While
the survey data provide a well constrained focused image of the
swarm, very few sparse hypocentral solutions were found with the
data from only the six permanent stations. From this example, it is clear
that a detailed analysis of the swarms other than that of April 26th
2003 is really not feasible.
By applying the master event technique of Deichmann and GarciaFernandez (1992) to the April 26th 2003 dataset with a crosscorrelation threshold of 0.75, for a window of 5 s around P and S
arrivals, Bonagura et al., (in preparation) identified three clusters
of well cross-correlated events within the swarm. Each cluster has
inter-hypocentre distances of the order of tens of metres, with a total
extent of some hundreds of meters (Bonagura et al., in preparation).
They strike from ENE–WSW to NE–SW and describe sub vertical or
very steeply N dipping planes (Bonagura et al., in preparation). They
range in depth between 4–4.5 km, 5–5.5 km and 7.5–8 km approximately. Observing their temporal occurrence, the cluster at ∼5 km
depth activates after both the shallower and the deeper clusters
(Bonagura et al., in preparation). These observations, taken together
with the uneven temporal distribution of magnitudes, suggest that
most of the 26th April seismicity was possibly related to the random
activation of distinct patches of the same fault plane subjected to a
heterogeneous stress field (Bonagura et al., in preparation).
Fault plane solutions have been determined for the better constrained absolute location hypocenters, obtained using at least twelve
P and S phase readings, with a RMS error b1 and both vertical and
horizontal error b2 km (Bonagura et al., in preparation). Fault plane
solutions associated with a compressional regime were found for the
April 26th swarm events, while the seismicity preceding and following the swarm gave normal focal solutions, suggesting extension
along E–W striking faults (Bonagura et al., in preparation).
Bonagura et al. (in preparation) and Saccorotti et al. (2004) conclude that the seismicity preceding and following the April 26th
swarm on E–W trending faults was mostly associated with the plate
divergence along the Terceira Rift, which is consistent with, e.g., the
GPS data analysed by Jónsson et al. (1999) which suggest an accommodation of 75% of plate divergence inside the island of S. Miguel. On
the other hand, the reverse movement related to the April 26th swarm
is not consistent with the general stress field acting on the island.
Taken together with the arguments reported above, therefore, we may
imagine that fault weakening due to increasing pore pressure has
played an important role in triggering these particular earthquakes.
Fig. 5. Example of three doublets identified in the catalogue under investigation and the detection of velocity changes through the application of Coda Wave Interferometry (CWI).
Seismograms (top) and plots of lag times of maximum of cross-correlation between the two traces, in non overlapping windows (bottom). a) No velocity variation is detected between
the two events, b) lag times show a linear decrease with time: the second event in the doublet is slower, c) lag times show a linear increase with time: the second event is faster.
Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo
volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015
6
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F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx
Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo
volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015
Fig. 6. Results from CWI for the six permanent network stations on and around Fogo, Z-component. Each line represents the result of CWI applied to a doublet. The line starts at the time of the first event and ends at the time of the second event.
Black solid line = the second event is slower, black dashed line = the second event is faster, gray line = no velocity variation was observed. This allows us to define time periods of velocity variation, that have been highlighted in dark grey (slower
velocity) and light grey (faster velocities). The asterisks highlight some departures from the general behaviors: those can be explained by the rainfall patterns (see text in Section 7).
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Fig. 6 (continued).
F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx
Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo
volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015
7
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F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx
These observations are compatible with the temporal and spatial
behaviour of the April 26th seismicity. Moreover, the Vp/Vs ratio is
found to be 1.4–1.7 (Dawson et al.,1985; Saccorotti et al., 2004; Bonagura
et al., in preparation), lower than usually observed in volcanic areas.
According to Ito et al. (1979), such anomalous Vp/Vs ratios could be
attributed to the presence of a boiling hydrothermal system beneath
the investigated area. The water–steam phase transition should affect
compressibility more than shear modulus, with the net effect of
lowering the ratio among P- and S-wave velocities.
These observations seem to suggest that pressurization may play a
role in triggering the seismicity. These conclusions have been deduced
from a detailed study of the April, 26th swarm and its background
seismicity. It would be interesting to attempt to confirm this behaviour
for all swarms that occurred throughout 2003 and 2004. However,
such a detailed study of the other swarms is not feasible due to the low
number of stations at which they are recorded.
As pressurization would lead to velocity changes in the medium,
we search for such changes using the repeating seismicity. The method
is outlined in the next section.
tion is proportional to the slope. The velocity variation and its associated error are computed by least squares method.
More than 400 doublets have been identified by correlating in a
3 second window all the events within the catalogue (outlined in
Section 2) in the time interval between January 2003 to December
2004 at the stations LFA, PMAT, MESC, VIF, CML and PVER. The data
were band pass filtered between 2 and 15 Hz to reduce environmental
noise. We apply the CWI method to monitor velocity changes through
time, using pairs of events (doublets) with cross-correlation coefficients of 0.9 or greater. Only in two cases (station CML and PMAT) the
threshold had to be lowered to 0.85, due to low signal to noise ratio.
The high correlation between events is not sufficient to guarantee the
same source characteristics, which requires detailed observation of
the first arrivals. All pairs of events were visually checked before being
analysed with the CWI technique. The waveforms are aligned at the Pwave arrival and we visually check the similarity of the P-onset, as an
additional constraint on the similarity of the source-time function. The
choice of the window length T for the time-shifted correlation coefficient is determined by a trade off between the number of independent
data and a fast drop in coherency for windows that are too small. Our
5. The methodology: coda wave interferometry
Families of repeating, almost identical earthquakes or multiplets
are observed among the seismicity recorded on Fogo volcano on the
fixed network as well as the 2003 survey data. Repeating earthquakes exhibiting very similar waveforms are representative of the
subsequent activation of a source whose mechanism and location do
not change over time (Geller and Mueller, 1980; Poupinet et al.,
1984; Roberts et al., 1992). In a doublet, while ballistic arrivals
match, the coda can lose coherency as multiply scattered waves are
sensitive to small changes in the medium. In a strongly scattering
environment such as a volcano, the seismic coda, composed of
multiply scattered waves, ‘samples’ the medium more effectively
than the direct (ballistic) arrivals. For identical co-located sources,
acting at different times, any observed difference in waveforms is
related to a change in the elastic properties of the medium. The coda
wave interferometry technique (CWI; Snieder et al., 2002) uses
multiply scattered waves to detect temporal changes in a medium
by using the medium as an interferometer, i.e. comparing waves
sampling the medium at different times. The perturbation of the
medium can be retrieved from the time shifted cross-correlation of
the coda waves before and after the perturbation. Exploiting the
information contained in the coda, CWI can discriminate between
source movement, scatterer movement and velocity variations in
the medium (Snieder et al., 2002; Snieder and Hagerty, 2004; Gret
et al., 2005; Snieder and Vrijlandt, 2005; Pandolfi et al., 2006,
Carmona et al., 2007; Martini et al., 2007).
CWI theory is based on path summation; the total wave field is
regarded as a superposition of all the possible scattering paths, including all mode conversions (Snieder, 1999, 2002). Starting with two
waveforms, u(t) and ū(t), the time-shifted correlation coefficient is
computed in a time window of length 2T centred on t, following the
formula:
R
t+T
uðt VÞu ðt V+ ts Þdt V
t−T
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Rðts Þ = v
ut R+ T
tR
+T
u
t
u2 ðt VÞdt V
u 2 ðt VÞdt V
t−T
ð1Þ
t−T
For each time window, the percentage velocity variation dv/v can
be computed from the lag-time ts as ts = −dv / vt. Under the hypothesis
of a uniform velocity variation, the plot of the lag-time corresponding
to the maximum of the cross-correlation per time window, versus
time, shows a linear relationship, where the mean velocity perturba-
Fig. 7. Top: Monthly rainfall (in mm) measured at station GFOG1, between January 1st
2003 and December 31st 2004. Middle: Black line/symbol: rain per day (in mm) for the
same time period and, in white, the 6th order polynomial fit. The choice of order is
simply to highlight the long term trend. The seasonal variations are evident, with more
rainfall between October and April and minimum rainfall in June–July and August.
Bottom: Time periods of velocity variation, observed through coda wave interferometry
analysis of all the doublets (delineated by the vertical black dotted lines in the limit of
the doublet time resolution): in dark grey defined period of slower velocity and in light
grey faster velocities. The vertical dashed grey lines highlighted the months (as in
Fig. 6). A correspondence between periods of low rainfall and higher velocities, and high
rainfall and lower velocity is clear.
Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo
volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015
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results are stable using window lengths between 5 and 10 cycles,
without an overlap.
We show doublet pairs from the 2003–2004 dataset in Fig. 5;
the application of CWI technique to these pairs, in case of a) null,
b) negative and c) positive velocity variations.
6. Results
We found a high number of doublets and, amongst those, several
multiplets. Due to the low energy of the majority of the events, not all
the stations in the region recorded all the pairs. We use each pair to
both determine the percentage change in velocity between the events
in the pair and to determine the sign of the change (faster or slower).
As not all stations are equipped with three component sensors,
9
we applied the CWI technique to the vertical component only, at all
stations.
Results are visually represented in Fig. 6a to f. In the figures, each
line represents the result from CWI applied to a doublet. The line starts
at the time corresponding to the first event of the doublet (time 1) and
it ends at the time corresponding to the second event (time 2). This
means that the results each line represents are not relative to the entire
line length, but they are relative to the status of the medium at time 2
compared to time 1. A black solid line means that the second event
(at time 2) is slower than the first (see example in Fig. 5b), a black
dashed line that the second event is faster (Fig. 5c), a grey line that no
variation was observed between the two events (Fig. 5a).
A consistency in the sign of the velocity variation at all the stations
was observed. The magnitudes of the variations are also consistent,
Fig. 8. a) Example of doublets measured at more than one station, and the relative percent velocity change calculated with coda wave interferometry for the same doublets at the
different stations, b) Rainfall measured at udometric stations Monte Escuro and Fogo II in May, June, July 2003 (first doublet in panel a), c) Rainfall measured at udometric stations
Monte Escuro, Fogo II and Ribeira da Praia in May, June, July 2003 (second doublet in panel a), d) Rainfall measured at udometric stations Monte Escuro and Fogo II between May and
December 2003 (third doublet in panel a), e) Rainfall measured at udometric stations Monte Escuro and Fogo II November 2003 to February 2004 (fourth and fifth doublet in panel a).
Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo
volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015
ARTICLE IN PRESS
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F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx
when the signal to noise ratio is good. At all stations, the results show
a decrease of velocity between November 2003 and March 2004, and
again starting in October 2004. An increase of velocity between May
2003 and September 2003 and again between May and September
2004 was also observed. These variations are indicated as dark and
light grey background shading in Fig. 6, respectively. The subtle velocity variations clearly highlight a seasonal cycle of the velocity patterns, which is unlikely to be related to volcanic activity.
No multiplets were found with both events before April 2003 and
both events after November 2004; therefore the velocity patterns
before April 2003 and after November 2004 cannot be defined through
analysis of this catalogue.
7. Correlation with rainfall
The daily and the monthly rainfall (Fig. 7; hourly rain data available
from the main meteorological station on Fogo) for the same two year
period show clearly that São Miguel Island experiences maximum
rainfall from October to April, and minimum rainfall between May and
July. This indicates a possible relationship between dry months and
higher velocities, and wet months and lower velocities.
The magnitude of velocity variations can also be correlated with
the episodes of rainfall: when the same doublet is recorded at more
than one station, a broad correlation could be found between the
magnitude of the velocity variation obtained by that doublet at each
station with the locally measured amount of rainfall (udometric
stations rain monthly data from the DROTRH, Direcção Regional do
Ordenamento do Território e Recursos Hídricos). Some examples are
listed and compared with the rain data in Fig. 8. Coda wave interferometry analysis on the first doublet (25 May 2003–24 July 2003)
gives a velocity variation of +0.2% at station MESC and +0.96% at
station LFA. Analysing the rain data at the two closest udometric
stations (Fogo II for LFA and Monte Escuro for MESC), we find that in
the area close to Monte Escuro it rained more than in the area in
proximity of LFA in the months of May, June and July 2003 (Fig. 8b),
therefore explaining a smaller positive variation. Analogously, the
doublet 22 May 2003–24 July 2003 gives again the smallest positive
variation at MESC (0.13%) when compared with the values obtained at
LFA (0.4%) and VIF (0.6%): the udometric station at Ribeira da Praia
(the closest to VIF) recorded locally the lowest amount of rain of the
three, while the highest amount of rain for the period fell in the Monte
Escuro area (Fig. 8c). Coda wave interferometry analysis on the
doublet 30 July 2003–03 November 2003 shows a velocity variation of
−0.56% at LFA and −0.52% at MESC, mirroring a similar pattern of the
rainfall at the udometric stations Fogo II and Monte Escuro (Fig. 8d).
The event on 9th December 2003 compared with a similar event on
14th February 2004 shows a negative velocity variation of −0.26% at
LFA while no variation velocity was observed at MESC: in February
2004 the Fogo II udometric station recorded a very high peak of
rainfall (Fig. 8d). Interestingly, the same December 2003 event gave a
negative velocity variation of −0.50% and −0.37% at LFA and MESC
respectively, when analysed towards an event on 16th February 2004
(only two days after the previous example): the different values obtained at the two stations only two days apart can be explained by a
particularly heavy rain episode on February 15th recorded also at the
Fogo main meteorological station.
In a similar matter, abundant episode of rainfall can explain the
few exceptions in the velocity variations from the general seasonal
cycles (indicated by asterisks in Fig. 6). For example, the positive velocity variation identified between April 2003 and January 2004 at
station PMAT can possibly be explained by above the average rainfall
in March–April 2003 (which can also explain a positive variation
recorded between May 2003 and January 2004 at station PVER).
Analogously, again at the station PMAT, the positive velocity variation
found between doublets with events in November 2003 and
November 2004 can be explained as November 2004 was drier than
Fig. 9. a) Cumulative sum of the seismicity (solid line, left) and of the rainfall (dashed
lines, right), b) normalised correlation coefficients calculated between the seismicity
and rainfall time series: at all lags (M ⁎ M − 1, where M is the length of the time series) in
the inset, for seismicity following rainfall (from lag M + 1 to end) in the main image: the
two highest peaks occur at time lag 733 (3 days) and 777 (47 days), c) the correlation is
performed scaling the raw values by [1 / (M − abs(lags))] (“unbiased”), at all lags in the
inset, for M + 1 to end in the main image. The values are normalised by the maximum
value amongst all lags for the insets, for positive lags only in the main images b and c.
the same month in the previous year. A drier 2004 winter explains
similar anomalies at station CML. At station VIF, events from 14
January 2004 and 22 January 2004 gave a different sign velocity
variation when compared to events in October 2004, which can be
explained by a peak in the rainfall on 18–19 January 2004.
A seasonal effect can also be broadly seen in the seismicity occurrence. September to April is the wettest period in São Miguel, and all
the swarms under investigation occur in those months, with the only
exception of swarm no. 5 in June 2004 (Table 2). In June 2004 rain fell
more abundantly than the summer average, an amount also
considerably higher than that of June 2003. The majority of the
swarms recorded over the two year period seems to occur in close
temporal association with higher than average rainfall (the converse is
Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo
volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015
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11
Fig. 10. Correlation coefficients calculated between the seismicity and a randomize version of the rainfall series. This was done in order to test the statistical significance of maxima of
correlation coefficient obtained from the two real time series, observed in Fig. 9b. The normalized correlation values for 4 populations of 100 tests each are plotted in panels a, b, c, d
(circle symbol), for the peak at 3 days (left column) and 47 days lag (right column), compared with the real time series (cross symbol).
Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo
volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015
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F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx
not always true). The observed delay between the seismicity and the
episode of rainfall ranges between 0 to 5 days (Fig. 9a).
In order to better assess this empirical observation, we correlated
the rainfall and seismicity time series at all lags (2 ⁎ M − 1, where M is
the length of the time series). The hourly rainfall data recorded at the
main meteorological station were summed up to obtain a daily record,
which was correlated with the daily number of seismic events.
When looking at the results for all lags (Fig. 9b and c, insets), an
annual periodicity can be seen in the correlation. This tells us that,
statistically, there is an underlying annual cycle in both data sets. The
correlation also exists, and is quasi-periodic, at negative lag times (e.g.
seismicity occurring after the rain), therefore meaning that the two
datasets have the same underlying periodicity. From these long term
trends we can conclude that the annual periodicity of the rainfall and
the seismicity appear to be the same, which indicates that, on average,
the rain is helping to drive the seismicity.
In analysing the results from lag M + 1 to end (e.g. seismicity
occurring after the rain), we found that the two highest values of
the correlation coefficient (1 and 0.99, or 0.69 and 0.68 when values
are normalised to the same value for all lags) are obtained with a
3 and 47 days lag delay between the rain and the seismicity
(Fig. 9b). When the correlation is performed scaling the raw values
by [1 / (M − abs(lags))] (“unbiased”) to compensate for the fact that
the coefficient decreases moving away from zero lag as less data are
correlated, the highest values results at large lags, but the two peaks
at lag 3 and 47 still stands out in the shorter lag region (Fig. 9c). The
fact that the correlations are better at larger lag times is an artefact
of more noise as less data are overlapped, and, in any case, it refers
to lags (N500) too large to have a physical significance for a possible
influence of the rain on the seismicity.
Due to high amount of rainfall and high level of seismicity in the
island, we have to exclude the possibility that the observed correlations cannot be explained by random chance. In order to test the
statistical significance of this result, we performed the correlation
several hundreds times, randomizing at each run the time of the
rainfall series. We then compared the correlation coefficient at lag 3
and 47 for each run with the results obtained with the real rainfall
time series. The normalized correlation values for 4 populations of 100
tests each are plotted in Fig. 10 (circle symbol), for the peak at 3 days
(left column) and 47 days lag (right column), compared with the real
time series (cross symbol). For the 3 days lag case, we can observe that
none of the randomize test gave a value equal or higher than the
real one, and only few runs (approximately 2%) gave values equal or
greater than 0.6. For the 47 days delay case (the second highest peak in
the correlation), several randomize test gave values higher or similar
to the real one. Hence, while we feel confident of the existence of a
correlation between the rainfall and seismicity time series with a delay
of 3 days, the same cannot be confirmed for the 47 days peak, which
therefore has no significance for our purposes.
Moreover, the spatial distribution of the epicentres within each
swarm was analysed in relation to the rainfall distribution recorded at
the udometric stations (monthly rainfall data; Fig. 11). These data
show two high well-above-the average values at the beginning and
end of 2004 at Fogo II and Lombo stations. The epicentre distribution
of swarm 4 and 7 (Fig.12c and e), recorded in the corresponding periods,
seem to be concentrated in an area around or not far from station
Fogo II and Lombo, respectively. With the exception of those two obvious peaks, all the remaining rainfall data, at all stations (Fig. 11), have
very comparable values, making this spatial correlation impossible
for the other swarms. It should be added that the udometric stations
record monthly rainfall data while the swarms always occur in 2 day
periods, therefore the two cases of observed spatial correspondence
between the seismicity and the pronounced peaks of rainfall should be
considered cautiously.
Deformation data (GPS) at Fogo showed in recent times several
periods of inflation and deflation in the area. In 1993–1997 displacements toward the caldera were registered, indicating slight deflation
of the volcano, which were interpreted as due to pressure decrease in
a shallow magma chamber beneath Fogo, or extraction of hot water
and steam by a geothermal plant to the north of the volcano edifice
(Jónsson et al., 1999). Unfortunately, no recent data are currently
available to the authors to establish a correlation with the observed
velocity variations and/or rainfall episodes.
8. Discussion
One of the most striking features of São Miguel seismicity is the
occurrence of swarm episodes comprised of hundreds of low
Fig. 11. Monthly rainfall data measured at the udometric stations for 2003 and 2004. Station locations are shown in Fig. 12. Data courtesy of DROTRH (Direcção Regional do
Ordenamento do Território e Recursos Hídricos).
Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo
volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015
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magnitude events occurring over a few hours. Understanding the
origin of this type of seismicity is our long term primary goal,
especially if it is understood to be connected to renewed volcanic
activity.
As a first step toward this objective, we analysed 430 doublets
identified in the 2003–2004 catalogue at six stations on and around
the volcano through a cross-correlation based technique. The results
highlight a seasonal cycle of the velocity patterns, with lower velocity
in winter time and higher velocity during summer months. We observed that seismicity seems to occur preferably in periods of more
abundant rainfall. While there is a clear relationship between seismicity and rainfall, an incontrovertible evidence of rain episodes triggering the seismic swarms at Fogo cannot be established mainly due
to lack of detailed temporal rainfall information (the hourly data are
only available at two permanent stations).
Fluids are known to be important in earthquake generation as pore
pressure variations alter the effective strength of faults, thus tiny varia-
13
tions of pressure associated with precipitation could conceivably initiate earthquakes at a few kilometres depth when the crust is close to
a critical stress state.
Evidence for meteorological triggering of seismicity has already
been found in other part of the world, e.g. in Switzerland
(Deichmann et al., 2006), USA (Saar and Manga, 2003; Christiansen
et al., 2005), Germany (Hainzl et al., 2006; Kraft et al., 2006a,b) and
in the Balkan area (Muço, 1999). Although seasonal variability
related to ground water recharge and precipitation has been
observed in all those cases, a statistically significant causal relationship between rainfall and earthquake swarms occurrence in an
isolated region has been shown only for Mt. Hochstaufen in SE
Germany: Hainzl et al. (2006) found that the recorded seismicity at
Mt. Hochstaufen is highly correlated with the calculated spatiotemporal pore pressure changes due to diffusing rain water and in good
agreement with the response of faults described by the rate-state
friction law (Kraft et al., 2006a). For those data, the precipitation and
Fig. 12. Epicentre distribution for swarms a) 1, b) 3, c) 4, d) 6 and e) 7 (2 and 5 did not have enough solutions), together with location of seismic stations (stars) and udometric stations
(squares).
Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo
volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015
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F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx
groundwater increase show maximum cross-correlation with the
seismicity when delayed by 9–11 days.
In active volcanoes, precipitation clearly influences, amongst other
parameters, fumaroles and ground temperature (for example, at Merapi,
Richter et al., 2004, Friedel et al., 2004) as well as thermal springs
temperature and gas concentrations (i.e. Arenal volcano, Lopez et al.,
2006) or self potential (Friedel et al., 2004). There is, to our knowledge,
no unambiguous evidence of the effect of precipitation on seismicity.
An increase in superficial seismic events due to quenching and destabilisation of parts of the lava dome has been observed at Unzen volcano (Yamasato et al., 1998), more significantly when fresh lava was
involved. At Mt. Merapi volcano there is some evidence that rainfall
influences seismicity rates, indicating interaction of meteoric water
with the volcanic systems (Richter et al., 2004), but in this case the
effect seems to be different: even if fresh lava was observed in the
dome region, there are examples of a decrease in the total seismicity
rate after heavy rainfall (Richter et al., 2004).
All those examples are clearly not comparable to the Fogo case. At
Fogo, there is no lava dome.
While our case seems to be more similar to the Hochstaufen one, it
should be noted that at Mt. Hochstaufen the seismicity ranges from
very shallow depth to a maximum of 2.5 km and the effect of precipitation at surface on the fault system at this depth takes approximately 10 days. In contrast at Fogo the seismic events range in average
from 2 to 7 km depth, and up to 10 km, with a delay between the
rainfall and the swarms occurrence from 0 to a maximum of 5 days:
the process seems to be different.
Furthermore, the velocity variations highlighted with the CWI
technique can be observed as an immediate effect following the
episodes of rainfall. Due to the lack of resolution in time of the CWI
technique (its resolution is strictly dependent on the occurrence of
doublets and their timing) we cannot quantify the delay in term of
hours; however, in many cases the delay is smaller than 1 day.
One possible interpretation of the velocity variations is that they
are the effect of water saturation of the soil layer following rainfall. But
it should be kept in mind that those velocity variations are observed
with the CWI method for doublets some of which are located at several kilometres depth and multiply scattered coda waves are likely
generated throughout a large volume encompassing the source and
the receiver, although there may be some near surface bias.
Therefore, it is unlikely that the CWI-derived velocity changes measure “directly” the rain at surface.
The temporal and spatial analysis of the swarms highlights the lack
of any main-shock after-shock sequence and no organized migration
of the hypocenters. The almost immediate response to the rain and
lack of hypocenter migration would exclude a process of fault pressurisation due directly to water percolation at Fogo. It would rather
suggest that a general effect of some other form of pressurization
might instead play a contribution in triggering the seismicity.
Similar observations have been made for induced seismicity at the
Castanhão water reservoir in NE Brasil where a very good correlation
and virtually no time delay between the water level rise in the reservoir
(due to above average heavy rainfall) and the increase in seismic
activity has been observed in 2004 (Ferreira et al., 2008). The spatio
temporal analysis of the data revealed that the seismicity occurred in
clusters at different depths and that these were activated at different
periods. The mechanism controlling this seismicity has been explained
as an instantaneous effect of loading and the delayed effect of pore
pressure (Ferreira et al., 2008).
While this investigation goes beyond the scope of this paper, it will
be object of future work; the mechanisms we intend to explore for our
dataset at Fogo are (i) pressure changes at depth in response to surface
rain that could contribute to critically stress the system and trigger
seismicity, and (ii) interaction with the geothermal system. The geothermal fluids in the Fogo massif have been identified as derived from
meteoric water, which infiltrates through the Fogo caldera and the
volcano flanks and flows from south to north on the northern flank
(Carvalho et al., 2006).
Analysis of seismic array data through multi-channel processing
techniques from a deployment in April 2003 (Bonagura et al., in preparation), shows the presence of correlated noise days before
the occurrence of the April 26th, 2003 swarm. These authors
interpreted such occurrence in terms of tremor-like signals originating from unsteady fluid flow in the geothermal system (see their
paper for a detailed discussion of these results).
Such unsteadiness could be due to rapid pressure changes at
depth caused by increased load due to the rain at surface and/or
increase of the ground water level, and the consequent static stress
increase in the volume to depth. Another possibility is that after
rainfall, the impermeable moisture layer near the surface could have
a capping effect and thus temporarily prevent CO2 flux locally, whilst
increasing soil gas levels in the subsurface. This capping effect has
been observed in other areas for both CO2 (Solomon and Cerling
1987; Hinkle 1991, 1994) and Radon (Asher-Bolinder et al., 1991).
Both a pressure increase and/or an increase of soil gas levels at depth
might explain the seismic velocity variations observed with coda
wave interferometry.
Both the extra load due to the rain and/or soil capping effects could
cause an increase of pressure at depth, contributing, together with
tectonic and/or magmatic causes, to seismicity triggering when the
system reaches a critical stress state. If the system is critically stressed,
the small changes associated with extra load due to the rain and/or
soil capping effects could possibly lead to triggered seismicity. Both
processes would likely have only a local effect (e.g. the degassing is not
totally prevented during rainy periods if we look at the system at large
scale) and their efficiency for increasing pressure at depth needs to be
tested and quantified.
In the case where the seismicity has a tectonic or deep magmatic
origin, the rain/stress transmission/pore pressure increase effect would
facilitate the seismic energy release at shallower levels by decreasing
the capability of the system to sustain the stress transfer from the
triggering sources. This could explain why deep and shallower events
are triggered simultaneously. This would also justify the “style” of the
seismicity in the region, with low Vp/Vs ratio and many low energy
events as a measure of the low capacity of the system to hold stress to
be released in bigger events.
Samplings at the main fumarolic fields show in some cases minor
increases in some gas ratios (e.g. H2/CH4) that could possibly be explained by an increased pressure in the feeding aquifers of the fumaroles; these changes are observed at times of increased seismicity
(F. Viveiros, pers. comm.). If this can be observed in more detail for all
the seismicity episodes, it could help explain the mechanism that
triggers the seismicity at Fogo volcano, and determine if the seismicity
variations are driven by magmatic processes, rainfall or an interaction
(and/or alternating) of the two.
9. Conclusions
A recurrent feature of the seismicity observed in volcanic regions is
the occurrence of families of similar seismic events, whose origin can
be attributed to the same source mechanism, acting in the same small
rock volume. Doublets/multiplets were identified in this study within
a catalogue of small magnitude (usually b3) volcano tectonic events
recorded in 2003–2004 by a selection of seismic stations around
Fogo volcano, São Miguel, Azores.
Subtle velocity variations, found through analysis of all the
doublets using Coda Wave Interferometry, highlight a seasonal cycle
of the velocity patterns and an excellent correlation with the rainfall,
with lower velocity in the wetter winter time and higher velocity
during the drier summer months. A seasonal effect can also be broadly
seen in the seismicity occurrence, suggesting that rainfall might play a
contribution in local seismicity.
Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo
volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015
ARTICLE IN PRESS
F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx
Moreover, temporal and spatial analysis of several swarms highlights the lack of any mainshock–aftershock sequence and organized
migration of the hypocenters, suggesting the involvement of a heterogeneous stress field, which in turn might be explained in terms of
vigorous fluid migration/pressurization at and around the hypocentral
areas.
These results point to a possible role played by rainfall in triggering
seismicity at Fogo volcano, the mechanisms of which are currently
under investigation. The immediate response of the seismic velocity
changes to the rain, an average 3 days delay between the seismicity
occurrence to the rain and lack of hypocenters migration would
exclude a process of fault lubrication. The mechanisms is more likely
related to local pressure changes at depth in response to surface rain
that could contribute to triggered seismicity when the system is critically stressed and/or the interaction of rain water with the geothermal system.
Acknowledgements
This work is sponsored by European Commission, 6th Framework
Project VOLUME (Contract 08471). DROTRH (Direcção Regional do
Ordenamento do Território e Recursos Hídricos) is kindly acknowledged for providing rain data from the udometric stations. The authors
wish to thank C. Riedel and R. Silva for early discussion and very
valuable information on the dataset, and Jesus Ibañez and an
anonymous reviewer for their suggestions on the manuscript.
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Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo
volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015