Seismic swarms, fault plane solutions, and stress tensors for

Seismic swarms, fault plane solutions, and
stress tensors for São Miguel Island central
region (Azores)
Rita Silva, Jens Havskov, Chris Bean &
Nicolau Wallenstein
Journal of Seismology
ISSN 1383-4649
J Seismol
DOI 10.1007/s10950-012-9275-x
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DOI 10.1007/s10950-012-9275-x
ORIGINAL ARTICLE
Seismic swarms, fault plane solutions, and stress tensors
for São Miguel Island central region (Azores)
Rita Silva & Jens Havskov & Chris Bean &
Nicolau Wallenstein
Received: 14 June 2011 / Accepted: 5 January 2012
# Springer Science+Business Media B.V. 2012
Abstract The central region of São Miguel Island is
one of the most seismically active areas of the Azores
archipelago. A revised analysis of the seismicity distribution at this region has, for the first time, shown
that the seismicity is clustered in two distinct areas: the
area around Fogo Volcano (Fogo) and the area around
Congro maar (Congro), with each area having a highly
localized swarm activity. From a total of about 15,000
events in the period from 2002 to 2010, 78 best
located events were selected to make fault plane solutions using P-wave polarities and amplitude ratios.
This set of fault plane solutions, and another six subsets derived from it, were inverted for the best fitting
stress tensor. The stress tensor using all the 78 fault
plane solutions is characterized by a subhorizontal σ1
striking WNW-ESE and a σ3 striking NNE-SSW,
consistent with the regional stress field for this region.
A similar result, using only the fault plane solutions
R. Silva (*) : N. Wallenstein
Centro de Vulcanologia e Avaliação de Riscos Geológicos,
Universidade dos Açores,
Ponta Delgada, Portugal
e-mail: [email protected]
J. Havskov
Department of Earth Science, University of Bergen,
Bergen, Norway
C. Bean
School of Geological Sciences, University College Dublin,
Dublin, Ireland
located in the Fogo area, was obtained. On the other
hand, for the Congro area, a local stress field seems to
be superimposed on the regional field: subhorizontal
σ3, striking NNE-SSW, and a near-vertical σ1. The
same stress regime persists in the first 5 km depth,
probably related to the upwelling of thermal fluids.
The rising fluids generate horizontal extension at shallow depths, which favour the opening of cracks and
the circulation and ascension of hydrothermal fluids.
The stress regime deeper than 5 km is more uncertain;
however, it is indicative of a compressional regime.
Thus, it can be conclude that the smaller Fogo area
appears to be dominated by the normal regional stress
field while the high active Congro area seems to have a
different, highly heterogeneous stress field dominated
by local conditions.
Keywords Azores . São Miguel Island . Fault plane
solutions . Seismic swarms . Stress inversion
1 Introduction
The Azores archipelago is located at the Triple Junction, defined by the intersection of the Mid-Atlantic
Ridge (MAR) with the North American (NA), Eurasian (EU), and African (AF) plates boundary (Fig. 1).
The Azorean Islands are all of volcanic origin and
several historical eruptions occurred inland and offshore. São Miguel is the largest island of the archipelago, and the last highly explosive eruption
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Fig. 1 Map of Azores archipelago showing the main morphotectonic features and seismicity distribution. The gray-shaded
area represents the different segments of the Terceira Rift, and
the dashed line represents the sheared western segment of the
African-Eurasian plate boundary. Tectonic structures: EAFZ
East Azores Fracture Zone, NAFZ North Azores Fracture Zone,
GF Gloria Fault, FFZ Faial Fracture Zone, AFZ Açor Fracture
Zone, PAFZ Princesa Alice Fracture Zone, PFZ Pico Fracture
Zone (morphotectonics features adapted from Hipólito 2009).
The Azores Plateau is limited by the 2,000 m bathymetric curve
(bathymetric data from Lourenço et al. 1997). The seismic data
are from 1997 to 2010 and contains all epicentres available in
the CIVISA database. The red solid line defines São Miguel
Island
(subplinian) inland occurred in 1563 at Fogo caldera
(Fig. 2). In the last 100 years, there have been recurrent swarm activity in the Fogo-Congro area (Nunes
and Oliveira 1999) and, in 2005, there was an exceptionally high seismic swarm activity, with more than
6,000 events recorded in 6 months (Figs. 3 and 4).
Recent studies indicate that the Fogo-Congro seismicity might be related to crustal deformation (Trota
2008), and there has been a real concern that renewed
volcanic activity might occur. So far no studies have
been made of the stress field in the area. In this work
some of the best recorded events were used to analyze
seismic swarm patterns, determine fault plane solutions, invert for the stress field, and investigate a
possible relationship between the seismicity, stress
field, and deformation. We use data from both the
permanent seismic network operated by the Center
for Seismo-volcanic Information and Surveillance of
the Azores (CIVISA) and a temporary network of
portable stations deployed between April and July,
2003.
1.1 Tectonics
The triangular shaped Azores Plateau is, in the West,
defined by the MAR, in the North, defined by the
North Azores Fracture Zone (NAFZ), and by a complex alignment with WNW-ESE direction, running
from the MAR to the western limit of Gloria Fault
(GF), designated as Terceira Rift (TR),and in the
South, defined by the East Azores Fracture Zone (EAFZ)
(e.g., Luís et al. 1994; Borges 2003) (Fig. 1). The
MAR and TR are detectable in the archipelago
seismicity distribution, as well as the Faial Fracture
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Fig. 2 Tectonic sketch of São Miguel Island (adapted from Gaspar et al. 1995; Wallenstein 1999; Ferreira 2000; Carmo et al. 2009).
Observable faults are shown as solid lines and inferred faults as dashed lines. The solid line box outlines the study area
Zone (Fig. 1), where a high seismicity concentration is
observed. There is no significant seismicity associated
with the EAFZ.
São Miguel Island is located in the alignment of the
TR, and its most important tectonic structures (faults
and lineaments) follow the general pattern exhibited
Fig. 3 Left, the 1989 swarm (Nunes and Oliveira 1999); right, the best located events (see text) from the time period 2002–2010. The
Fogo and Congro areas are indicated with dotted lines
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Fig. 4 Monthly number of earthquakes with Md≥1.5, as well as magnitude and depth as a function of time (top right). The times of
some of the major swarms are indicated. Top left, monthly number of the 1,336 selected earthquakes (shown in Fig. 3)
by the Azores Plateau, with main directions NW-SE and
WNW-ESE (Fig. 2), as shown by Gaspar et al. (1995),
Wallenstein (1999), Ferreira (2000), and Carmo et al.
(2009). The emplacement of the polygenetic volcanic
edifices and the distribution of secondary volcanic phenomena, like fumaroles (Notcutt and Davies 1999) and
thermal springs (Cruz et al. 1999) are related to the main
structures referred above.
The central region of São Miguel Island, where
recent increase in seismic activity has been observed,
consists of the Fogo and Furnas volcanoes and the
Achada das Furnas Plateau (Fig. 2). The Fogo Volcano, also known in the literature as Água do Pau
Volcanic Massif, is placed in the central region of
São Miguel Island. Like other polygenetic volcanoes
in the archipelago, its emplacement seems to be due to
the intersection of the dominant NW-SE and E-W
fracture systems, thought to be associated with the
existence of deep transform faults (e.g., Queiroz
1997; Wallenstein 1999). At the northern flank of the
volcano, it is possible to infer the existence of the
Ribeira Grande Graben by the alignment of the scoria
cones and domes, reflecting the NW-SE and NNW-SSE
directions. It is also possible to observe NE-SW alignments and a circular system of faults, which might be
responsible for the emplacement of trachytic domes on
the upper part of the volcanic edifice (Wallenstein
1999). The Achada das Furnas region corresponds to
a basaltic plateau, which links the Fogo Volcano to the
Furnas Volcano. This plateau consists of cinder/spatter
cones and lava flows, and includes a small young trachytic centre known as Congro maar (Cruz et al. 1999).
Furnas Volcano is the youngest of the three polygenetic
volcanoes. The main fracture system crosses all volcanic
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systems with a dominant WNW-ESE direction, probably associated with a right-lateral strike-slip fault
(Gaspar et al. 1995). On the southern and western flanks
of the volcano, a distinctive fault system with N-S to
N20°–50° E direction is present, as well as NW-SE
directions (only on the S flank) coincident with the TR
regional direction. The NNE-SSW system seems to
control some structural depressions, calderas outlines,
valleys alignments (Gaspar et al. 1995) and plays a
major role in the transfer of magma to the surface (Guest
et al. 1999).
1.2 Seismicity
During the last century several seismic swarms were
recorded in the study area. The first swarm accurately
located occurred in 1989 (e.g., Tryggvason et al. 1989;
Nunes and Oliveira 1999).
Over 17,000 earthquakes have been recorded at this
central region since 1997, the majority with duration
magnitude, Md≤2, and located in the depth range
between 0 and 10 km. Since the seismic network
configuration has changed over this time period, only
the statistics for events larger than the overall detections threshold (Md01.5) will be shown. More than
4,000 events with Md≥1.5 were recorded in this time
period (Fig. 4). Duration magnitudes were calculated
using the empirical relation of Lee et al. (1972).
Since 2002, the central region of São Miguel Island
has experienced a higher seismic activity than in the
previous decades (Fig. 4). This seismicity occurs
mainly as seismic swarms. Figure 4 indicates some
swarms recorded in this region. It is seen (Fig. 3) that
the 1989 swarm was located in the same area as the
latest swarms in Congro area. In the previous centuries,
at least since 1891, several other swarms have been
reported (Nunes and Oliveira 1999), showing that seismic swarm activity in this area is a recurrent phenomenon, being the one occurred in 2005 probably the largest
instrumentally recorded.
Earthquake swarms are characterised as a sequence
of small magnitude events with a strong time and
space clustering, and without a dominant earthquake
(Mogi 1963). This type of seismicity occurs normally
in volcanic and/or geothermal regions, and presents
high b values of magnitude-frequency distribution
since the swarms consist of a larger number of events
with low magnitude values, compared with the ones
with high magnitude (e.g., Fischer 2005). The
occurrence of earthquake swarms in volcanic active
regions has been attributed to subsurface magma movement at shallow depths (e.g., Spicák and Horálek 2001;
Fischer and Horálek 2005; Hensch et al. 2008) and/or
associated with hydrothermal fluids circulation through
new or pre-existent fractures (e.g., Husen et al. 2004;
Farrel et al. 2009). Therefore, the swarms are commonly
limited to regions with exceptionally heterogeneous
distributions of material properties and stress concentrations (Hill 1977).
The seismicity located at the central region of São
Miguel Island has earlier been seen without a clear
separation into distinct seismicity areas, probably because earlier raw results presented important global
location errors. In order to look only to the best data, a
selection of the events occurred between 2002 and 2010
was made. First, the original events were relocated using
only stations with less than 13 km of epicentral distance
and the standard crustal velocity model for the region
(Senos and Nunes 1978 in Senos et al. 1992) (Table 1).
The Hypocenter program (Lienert and Havskov 1995)
was used for the locations. Then a second selection was
made requiring a minimum number of eight stations, the
root mean square of travel time residuals (RMS) less
than 0.1 sec, and estimated errors (within 90 % confidence) with less than 1.5 km in all directions, being
selected 1336 events (Fig. 3). Some revision of the
phase arrivals was performed, and so the phase picking
data are not identical to what was available in the
CIVISA database.
The data are clearly clustered into two areas according to their locations: Fogo and Congro areas (Fig. 3).
The Fogo area is located around Fogo Volcano, and
Congro area is located nearby Congro Maar. This
separation was not so clear prior to the selection of
the high-quality data, and has not been reported earlier, probably due to the absence of restrict selection
criteria. In fact, Zandomeneghi et al. (2008)
Table 1 P-wave velocity model—
TAC model (Senos and Nunes
1978 in Senos et al. 1992). See
section on velocity models later
Depth
(km)
P-wave
velocity (km/s)
0
2.4
0.5
4.1
2.2
5.4
5.4
6.8
11.9
7.8
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elaborated a tomographic study of the area, and the
3D locations obtained did not show the two clustered
areas. In order to better identify swarms in different
localities, the data will be analyzed separately for Fogo
and Congro areas.
1.3 Seismic swarm periods
From 2002 to 2010, four main swarm episodes (April
2003, October 2004, May/September 2005, and September 2006) were identified (Fig. 4), where hundreds to
thousands of small earthquakes occurred within days or
over a few months. The statistic for the data selected for
Fogo and Congro regions is also shown in Fig. 4, despite
that these two subsets do not correctly reflect the overall
seismicity since not all events are included. However, the
comparison gives an indication if the seismicity occurs
simultaneously within one month in the two areas. As it
can be seen, most of the seismicity in the two areas does
not quite coincide in time. In Fig. 5, none of the 15
identified swarms, with duration of 1–2 days, occur
simultaneously in the two areas, which indicate that the
source of the activity in the two areas is not directly
related. The Congro area is the area with the largest
number of swarms, and with the highest activity. Generally, it is seen that the seismicity has gradually declined
since the high activity in 2005. Nevertheless, the central
region is still the most active zone in São Miguel Island,
and one of the most active of the Azores archipelago.
In order to identify the best epicentral locations of
the swarms, periods with higher activity were identified within the selected dataset of high-quality events
(Fig. 3). This limited dataset might reduced the number of events in the swarms when compared with the
whole dataset of the 17,000 events, and might even
have prevented the detection of some swarms. A total
of 15 swarms, with a total 381 events (Table 2; Fig. 5),
were identified within the 1,336 high accurately located events. The epicentres for the 15 swarms are shown
in Fig. 5, as well as some individual fault plane solutions occurring during the swarm periods (see text
below). In addition, the even higher accurately located
epicentres of the events used for individual fault plane
solutions are shown (see later).
The events in the individual swarms are generally
located within areas less than 2×2 km, and considering the location accuracy estimated to 1.5 km, the
enclosed area might be even smaller. The similarity
among some seismograms of different events within
one swarm seems to point to the existence of multiplets,
which reflect a restricted area of influence. The swarms
seem to move around and cover different parts of the
seismically active area at different times. The two largest
Congro swarms are the exception since they cover a
much larger area. No systematic migration with time
was found within these two large swarms. The events
look to jump around randomly in the whole area during
the occurrence of the swarm, indicating that the stress
changes that are in the origin of these large swarms cover
large areas when compared with the small swarms.
The hypocentral depths of the routine picked events
are more uncertain than the depths of the events used
for fault plane solutions, since the calculated errors for
these last are less than 1 km, in contrast with the range
of 0.5 to 2 km of the former ones. Table 2 shows the
depth range for the different swarms. All events have
depths ranging from 1 to 7 km. Within one swarm, the
events can be at very similar depths (like swarms #1 to
3), or spread out in depth (like swarm #5). Considering
swarm #5, the depths of the 11 events used for fault
plane solutions are very well determined (less than
1 km error), so it is clear that there is a considerable
spread in depth for some seismic swarms. There is a
significant difference in depths between Fogo and
Congro seismic events. In the Fogo area, the majority
of the events are above 5 km depth while in the
Congro area there are several events that are located
deeper than 5 km. High seismic activity and swarms
generally occur either in Fogo or Congro areas. However, they do not seem to occur simultaneously in the
two areas. The use of only the best located events has
enabled the identification of more restricted swarm
source areas, which have not been seen so clearly
previously, since earlier it was thought that the swarm
events were occurring randomly over the whole seismically active Fogo-Congro area (Silva 2004).
2 Data for fault plane solutions
The data used for fault plane solutions were obtained
from the 16 permanent stations operated by CIVISA at
São Miguel Island and from 11 temporarily deployed
stations on the behalf of the European project, e-Ruption,
from April to July, 2003 (e.g., Bonagura et al. 2004;
Saccorotti et al. 2004). The earthquake locations were
obtained using SEISAN software (Havskov and
Ottemöller 1999) and a five-layer, 1D velocity
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Fig. 5 The 15 swarms identified in this study. In each panel, the
78 best located events (grey squares) used for fault plane solutions are shown together with the swarm events (black
squares). Individual fault plane solutions available for the
swarm (bottom right corner) are shown. The seismic activity
is concentrated either near the Fogo lake (outline shown to the
left) or to the east, in the Congro maar area
model (Table 1). In order to get reliable fault plane
solutions, the selected events had to be recorded
on, at least, 10 stations, and with Md ≥ 1.5. The
RMS values should also be lower than 0.2 s. In
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Table 2 Swarm depth ranges
Swarm #
Year
Month
Day
Area
1
2003
2
2003
3
4
Number of events/swarm
Depth range (km)
04
26
Congro
7
5
09
07
Congro
14
5
2003
11
08
Congro
17
6
2004
01
22
Fogo
20
1–5
5
2004
10
30–31
Fogo
15
1–7
6
2005
05
12–14
Congro
76
1–6
7
2005
07
08
Congro
21
2–5
8
2005
07
20–21
Congro
25
1–5
9
2005
09
20–23
Congro
92
1–7
10
2006
05
27
Congro
10
4–5
1–5
11
2006
06
03–04
Congro
30
12
2006
09
17
Congro
21
5
13
2006
10
12
Fogo
11
4–5
14
2007
06
16
Fogo
12
4–5
15
2008
04
03
Congro
10
2–5
For swarms references, see Fig. 5
the total, 100 events were selected, 78 of which
were used in determining the fault plane solutions
(Fig. 5). The remaining seismic events were exclude due to the absent of clear polarities and/or
amplitudes to get reliable solutions.
2.1 Velocity models and locations
The first seismic study of crustal structure in Azores
occurred on 1974, with the main scope to define the
lithospheric structure near the Mid-Atlantic Ridge
(Senos et al. 1992). Later, in 1977 and 1978, other
seismic studies focusing on the crustal structure of
Terceira and São Miguel islands were carried out in
order to define geothermal areas of interest (Senos et
al. 1992). Two 1D seismic velocity models were deduced: one for Azores in general (MAC model) and
one for São Miguel Island in particular (TAC model;
Table 1) (Senos and Nunes 1978 in Senos et al. 1992).
These two models have been used since then in the
routine location at CIVISA.
There have been several attempts to improve the
velocity model for the central region of São
Miguel without much success (Escuer 2006). Later,
Zandomeneghi (2007) and Zandomeneghi et al.
(2008), using data from the e-Ruption project, calculated a 3D velocity model for the same region. With the
obtained 3D model, the average shift in location was
less than 0.07 km in any direction relative to using the
TAC model, and therefore the TAC model will still be
used as the prime model for this study.
To get accurate fault plane solutions, the hypocentral location is critical, and in particularly its depth is
very important due to the dependence on angle of
incidence.
Since the 1D model used for location assumes a
layered structure, errors have to be assumed in the
location in addition to the ones associated to the
uncertainties in the phase readings. In order to minimize the errors caused by the velocity model structure,
events were located using only stations within 10 km
epicentral distance. In general, the effect of this limitation on the location was an increase in depth of 1 to
2 km, with a virtually unchanged epicentre, as compared with using all stations. All phase readings and
depths were checked (RMS vs. depth was also
checked). Formal errors were generally less than
1 km, but the real errors can probably be larger. It
was observed that it was often difficult to get very low
residuals (RMS generally between 0.1 and 0.2 s using
10 arrivals) for the S-arrivals, despite the arrivals
being very clear (example in Fig. 6). This fact can be
attributed to the existence of a heterogeneous structure, which will clearly increase the uncertainty of the
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Fig. 6 Seismograms from station MESC. The event origin time
is 8 August 2003 at 22:00, with Md01.8, and hypocentral depth
of 5.3 km
results. It was also investigated if the station corrections
could improve the location results. However, it was not
possible to determine consistent stations residuals, probably because the stations were located too close to the
heterogeneous structure.
2.2 Determination of fault plane solutions
Although the hypocenters locations were calculated
with stations at less than 10 km epicentral distances,
data used for the fault plane solutions included stations
up to 15 km distance (only polarity). The fault plane
solutions could be found, or at least the P- and T-axes
estimated, for about 20 % of the data with only polarity data. However, for the majority of the data, SH/P
amplitude ratios were also needed in order to get a
solution, and in practice at least five polarities and
three amplitude ratios were necessary.
The actual calculations were made using two different programs: FOCMEC and HASH. FOCMEC
(Snoke et al. 1984) can use polarities and amplitude
ratios of S- and P-phases for all components. However, only the P-phase on the Z component, and SH on
the T component were used due to the uncertainty of
correcting SV for the free surface effect (Snoke et al.
1984). The input amplitudes were corrected for the
free surface effect, and could also be corrected for Q.
Due to the short hypocentral distances, no correction
for Q was made. The program HASH (Hardebeck and
Shearer 2002, 2003) also uses polarities and amplitude
ratios of S- and P-phases. The P-amplitude is given by:
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Ap ¼ A2r þ A2z and the S-amplitude is given by:
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
As ¼ A2sv þ A2sh , where A is amplitude, r is radial, z
is vertical, sv is SV, and sh is SH. The free surface
correction was not built in, but replaced by a fixed
factor per station, which had to be determined independently. In order to simplify the input, the free
surface corrected amplitude ratios from FOCMEC
were used as input for HASH. The program was
modified to use only SH, and by using the free surface
corrected P-phase on the Z-component, the assumption was made that Ap 0Az(P). Amplitude ratios were
only used for the stations within 10 km epicentral
distance, where clear direct arrivals (example in Fig.
6) could be observed while polarities were used for
stations up to 15 km’s distance.
To test the data, FPFIT (Reasenberg and Oppenheimer
1985) and PINV (Suetsugu 1998) programs were also
used. However, these programs do not use amplitude
ratios, and they were used only for comparison.
FOCMEC operates on the principle that the user
gives a maximum number of polarities and amplitude ratios errors and the amplitude ratio error
should be less than a given ratio error. This is
useful for getting an idea of all the possible solutions. However, if there are few or badly distributed data, changing one parameter (like allowing
one more polarity error) can give distinct results.
HASH returns solutions with less than a given number
of polarity errors and averages the amplitude ratio error
less than a given limit. If no solutions are found,
error limits can be increased and normally several
solutions are returned. Using this, an estimate of
the best solution is made and likely errors calculated. The advantage in using HASH is that it finds one
best solution while with FOCMEC the user must select
one among many. Also HASH will not change completely the solution by one wrong amplitude ratio,
once the amplitude ratio errors average is used as
selection criteria, instead of a single amplitude
ratio. On the other hand, FOCMEC does not give
any estimate of the errors in the solution, while
HASH calculates an estimated error. However, that
requires an input in which each event has been located
with, e.g., ten different likely input models, and all data
are used as input, in order to get an estimate of the model
uncertainties. This was not done in the present study, but
an estimate of the quality of the solutions was performed, with the attribution of quality codes ranging
from A to C. The codes were attributed according to
the following criteria:
A. The fault plane solution could be obtained with
polarities only, and both FOCMEC and HASH
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gave the same result when adding amplitude
ratios;
B. There was not a unique fault plane solution using
polarities only, and only an indication of dominant P- and T-axes was possible. Both FOCMEC
and HASH results coincided when adding amplitude ratios;
C. No possible solution was found with polarities
only. Fault plane solutions for HASH and FOCMEC were different (<40°) when adding amplitude ratios.
The evolution of the seismicity from 2003 to 2008
(Fig. 5) shows two distinct areas acting independently
are visible: Fogo and Congro areas. With four significant swarms in the 6-year period, the Fogo area was
less active than the Congro area, where 11 swarms
occurred in the same period.
For 10 of the 15 swarms, it was possible to find some
individual fault plane solutions. In some cases, just one
individual solution (swarms #4, 6, 12, and 13) was found,
but in other cases there were two or more individual
solutions (swarms of #5 and 7) identified for each swarm.
In general, there is a mixture of fault plane solution types
both in the Fogo and Congro areas. The most representative fault style in both areas is strike-slip to oblique
strike-slip, with an important thrust component.
3 Crustal deformation
Ground motions using GPS measurements in the area of
the Fogo Volcano has been carried out since 1994
(Jónsson et al. 1999). Their study indicated a slight
deflation of the volcano and they suggested that it could
be related to a pressure decrease in a shallow magma
chamber or due to the extraction of water and steam
from the ground by the nearby geothermal power plant
(Jónsson et al. 1999). Recent crustal deformation studies
(Trota 2008), using GPS datasets from the end of 1999
to 2007, suggest that the central region of São Miguel
has experienced a complex pattern of deformation,
which when correlated with the seismic data recorded
during the same period, lead Trota (2008) to divide the
recorded deformation into three phases. The first phase,
from 1999 to the beginning of 2005, corresponds to a
period with both horizontal and vertical components of
deformation, with a general uplift with the maximum
value at the northern slope of Fogo Volcano (Fig. 7a).
The author suggests that this is due to pressure increase
caused by some intrusion at superficial crustal levels.
The second phase was complex in terms of deformation
since deflation and inflation occurred at the same time
(Fig. 7b). Regarding the horizontal displacement, this
phase presents the same radial pattern as the first, while
the vertical motion shows uplift and subsidence, respectively at the South and North of the centre of deformation (marked as a pink star on the figure). According to
the author, this is probably related to partially draining
magma from a magmatic chamber along new and/or reactivated fissures. The last deformation phase started in
October 2005 and included the 19th September 2006
swarm. It was marked by a low deformation rate, which
the author suggests to be related with the decrease of
pressure due to magma migration from the reservoir.
During this last phase the radial outward pattern was still
present at the Fogo and Congro areas, but the North
sector showed a general uplift, whereas for the South
sector exhibited a strong subsidence (Fig. 7c).
We will try to compare this information on deformation with the seismicity and the possible changes in
the stress field.
Comparing the crustal deformation with seismicity,
it can be observed that there is, in general, an overall
correlation between the area of deformation and the
area of seismicity, so they are clearly related to each
other. The modelled centre of deformation is located in
the Congro area and coincides with the higher seismicity zone of the area. This might explain the higher
rate of seismicity in the Congro area when compared
with the one of Fogo area. Since crustal deformation is
affecting mainly the Congro, the seismicity in the two
areas might be decoupled as it is also indicated by the
occurrence of non-simultaneous swarms in the two
areas. The seismicity in the Fogo area might be representative of the average long term seismicity while the
Congro seismicity is exceptional.
4 Stress tensor analysis
The stress tensor analysis consists in finding the directions of the three principal stress axes (σ1, σ2 and σ3,
from most compressional to most extensional) that are
responsible for a set of earthquakes. The stress tensor
can be calculated from different fault plane solutions
under the assumption that they represent a uniform
stress field. Considering a homogeneous, elastic and
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J Seismol
Fig. 7 Top, horizontal velocity field for all datasets (from
1999.9 to 2007.7 (decimal years)); bottom, vertical components
of the velocity field for the three deformation phases and the
location of the identified swarms (same as in Fig. 5) occurring in
each phase: a) from 1999 to the beginning of 2005, b) from the
beginning of 2005 to September 2005, and c) from September
2005 to 2007. The pink stars indicate the centre of deformation
after modelling. Contour lines of 10 mm. Scale bar units, mm/
year (Trota 2008)
isotropic medium without friction and without preexisting faults, the stress tensor should have a shear
stress (τ) of (σ1+σ3)/2, where σ1 and σ3 correspond
to the largest and smallest eigenvalues of the stress
tensor, respectively (Legrand et al. 2002).
According to this ideal scenario, the P- and the Taxes can approximately correspond to the principal
stress orientations. However, the medium is often prefractured and friction exists on the faults, and therefore
the movement along pre-existing faults might imply that
σ1 and σ3 are not oriented at 45° to the fault surface
(Ramsey and Huber 1987) and therefore, they will not
match the P- and T-axes. In fact, the only restriction that
can be made is that σ1 lies within the dilatational quadrant of the focal mechanism. Therefore, a given stress
field can induce movement of pre-existing faults with
different orientations (e.g., Legrand et al. 2002), which
suggests that a better approach is to find the stress tensor
that best fit the overall fault plane solutions. To obtain
the principal stress axes, the method described by
Michael (1984) was used. In this method, to quantify
the difference between the obtained stress tensor and the
available data, the β parameter, or misfit angle, is computed. β is the angle between the “observable” slip
direction on each plane and the maximum shear stress
(τ) predicted by the stress tensor (Michael 1984), which
ideally should be parallel to the slip direction. If β>40º,
it means that the stress field is spatially heterogeneous,
since it cannot be accurately represented by only one
stress tensor (Michael et al. 1990).
Michael’s stress inversion code uses a bootstrap
resampling technique (Michael 1987a, b) in order to
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define the confidence regions. This technique consists
in resampling the original dataset randomly and several times (which means that the same mechanisms
can be repeated two or more times, while others can be
absent) to simulate having new earthquakes in the
same area and at the same period. According to Michael (1987a, b), to get a confidence level of 95%,
2000 repetitions are required, which was the number
used in this work. Each bootstrap-resample is inverted
for its best stress tensor, and the tensors distribution
gives the confidence limits on the best tensor for the
original dataset. This inversion process, as tested by
Michael et al. (1990), was shown to be robust for
datasets with 15 or more events, and at least 15 events
should ideally be used. When inverting fault-plane
solutions, two nodal planes are obtained. However,
as discussed by Michael (1987a, b), it is advisable to
select one of the nodal planes as the fault plane for the
purposes of inversions, and for estimating confidence
limits by bootstrap resampling, but since this information is not known a priori, the Michael (1987a) algorithm uses both nodal planes, trying at the same time
to choose the correct fault plane and to find the best
stress tensor.
The stress tensor inversion gives the orientation
(strike/dip) of the three principal stress axes σ1, σ2,
and σ3, and the ϕ parameter (ϕ0(σ2–σ3)/(σ1–σ3)),
which defines the relative size of the intermediate
principal stress with respect to the maximum and
minimum principal stress.
5 Stress inversion results
5.1 Regional stress field
The regional stress field near São Miguel Island has
been inferred from fault plane solutions of large earthquakes to be approximately normal (ca N40° E) to the
Terceira Rift (Fig. 1) deformation zone (Borges et al.
2007). The nearest earthquake to São Miguel Island
used in that study was the event of 21st January 1989,
Mw05.7, located North of the island. It was a pure
normal fault with a T-axis strike of 39°. For the São
Miguel area, there are no other published fault plane
solutions for large or for small earthquakes. Therefore,
the average stress field, using the data from the present
work, will be estimated.
Table 3 Different regional stress directions and fault plane
orientations inferred from different methods
Strike
Dip
Rake
σ3
σ1
1989 earthquakea
131
41
−87
39/5
193/86
Composite solution
104
69
−59
171/18
53/55
Source
ABC solutions
19/34
113/5
A solutions
15/30
−95/31
B solutions
21/19
118/22
C solutions
24/46
167/38
T
P
15/−
105/−
Rose diagram
For the two fault plane solutions, it is assumed the T- and P-axes
correspond to σ3 and σ1, respectively. For stress inversion, σ3,
σ1, consist of strike/dip. The composite solution is obtained
with all the 78 events. Rose diagram corresponds to the dominant direction from rose diagrams of P- and T-axes strikes. All
values in degrees
ABC, A, B, and C solutions are stress inversions results using: all
quality types, only A solutions, only B solutions, or only C
solutions, respectively
a
According to Borges et al. (2007)
In this study, 736 polarities for 78 events were used.
Of these, 79 % were the same for all events indicating
that, although the fault plane solutions look rather
random (Fig. 5), they are the result of a non-random
stress field. A better way of getting an idea of the
average stress field is doing a stress inversion, assuming that the stress field is uniform. The average stress
field was calculated with the software SLICK (Michael
1984), using the 78 fault plane solutions (Table 3;
Fig. 8). Since we consider three quality types of fault
plane solutions (A, B, and C), the stress analysis was
carried out for each of the three groups. With this basis,
the results (Table 3) indicate similar solutions for the
three quality types, being performed an average inversion using all the data.
A rose diagram was also made with the P- and Taxes directions from the 78 best located events
Fig. 8 Stress tensor solutions obtained from all the inversions b
performed. Stress tensor solutions: a) for the 78 best located
events, b) for the events located within Fogo area, c) for the
events located within Congro area, d) for the events located
below 5 km, e) for the events located above 5 km, f) for the
October 2004 swarm, and g) for the 2005 swarm. The square
represents the σ1 axis, the triangle the σ2, and the circle the σ3
axis. The numerous dots define the 95% confidence region for
each stress axis. The rose diagrams of P- and T-axis azimuths are
also seen in the figure
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(Fig. 8a). It can be observed that the well-defined
dominant T-axis is orientated ~15° N while the P-axis
direction is more random. However, because of the
circular distribution of the P- and T-axes, and the fact
that the dip is ignored, when averaging directions, a
careful treatment and interpretation of the rose diagrams is necessary. The minimum compressive stress
axis (σ3) strike is also similar to the one inferred from
the rose diagram.
The results obtained inverting all of the 78 fault
plane solutions (Fig. 8, all) show a nearly subhorizontal WNW-ESE maximum compressive stress
axis (σ1) and a minimum compressive stress axis (σ3)
striking NNE-SSW, with a dip <45° (see Table 4). The
rose diagrams indicate that the P- and the T-axes are
orientated in agreement with the stress tensor. The inversion of the entire dataset showed that all fault plane
solutions could not be explained by a single uniform
stress tensor, since the misfit angle (β) was too large to
assume a near constant stress field (Fig. 9). However,
the analysis shows a consistent T/σ3 strike of 15°
to 19°, indicating that the dominant regional stress field
is tensional with a direction as expected from the
regional tectonics, although it is not completely
normal to the Terceira Rift zone. This could indicate
some local modification in the regional stress field.
Looking at the variation of the misfit angle (β) as a
function of event number (Fig. 9), it is observed that
there is a smaller oscillation of the values between
event number 18 and 41, which could indicate a more
uniform stress condition during this time period. The
October 2004 swarm, located at the Fogo area (swarm
#4 in Fig. 5), occurred at the beginning of this sequence and it is also the swarm with the largest number (11) of individual fault plane solutions. The stress
tensor inversion performed for this group (Fig. 8f),
shows a β value of ~24° (Table 4), emphasizing the
hypothesis of a more homogeneous stress field, when
compared with the results from the others inversions.
The result from the stress inversion for the October
2004 swarm shows σ1 and σ3 subhorizontal, striking
WNW-ESE and NNE-SSW, respectively, and σ2 nearvertical, indicating a strike-slip mechanism. Again, the
analysis shows σ3 strike of ~20°, consistent with the
regional dominant tension.
The 2005 swarm is considered the most representative of the seismic swarms that occurred in the Congro area, and it was used to perform the stress inversion
which showed a normal faulting style (Fig. 8c). However, and as it was verified for the entire Congro dataset,
the β value is high (Table 4), showing one more time the
existence of a heterogeneous local stress field in this
area.
A stress inversion analysis was done for different
depth ranges and using the focal mechanisms of the 78
best located events. The focal mechanisms of events
shallower than 5 km were inverted and the results
showed a normal faulting style, with σ1 near-vertical
and the other two subhorizontal axes, with σ3 striking
NNE-SSW (Fig. 8d). The uncertainties in σ1 and σ2
are quite large while σ3 seems to be well determined
and in agreement with the regional stress field. The
Table 4 Stress inversion results for the datasets analyzed in the present study, with strike/dip of the principal stress axes
Dataset
# FPS
σ1
σ2
−149.3/55.6
All
78
112.7/5.3
Fogo
20
−72.5/11.1
Congro
51
−171/46.6
−71.9/8.6
Depth<5
61
−156/62.4
111.3/1.3
Depth>5
17
−80.7/0.5
−170.7/6.1
October 2004
11
−71.9/13.3
164.9/66.5
2005 swarm
20
−133.4/54.8
123.6/8.9
All values in degrees
SS strike-slip, NF normal fault, TF thrust fault
a
The magnitude ratio of principal stress axes (see text)
b
The misfit angle
c
According to Zoback (1992)
91.3/78.4
σ3
19.2/33.8
ϕa
βb
Stress regimec
0.94
59
SS
0.39
50
SS
25.9/42.1
0.56
58
Unknown
20.6/27.5
0.85
54
NF
14.3/83.8
0.31
63
TF
22.7/19
0.22
24
SS
27.6/33.6
0.53
47
NF
−163.1/3.1
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Fig. 9 Misfit angle (β)
variation along the entire
dataset
stress solution obtained with events deeper than 5 km
shows reverse mechanisms (Fig. 8e). However, this
solution is more uncertain than the solution obtained
for shallower events, and should be used carefully.
Despite the uncertainties, there is a clear difference
in the stress field for deep and shallow events.
but with a lower β value (Table 4). The stress tensors
are different for the two groups and could indicate that
there is a real difference in the average stress fields
between Congro and Fogo. The inconsistencies for
Congro could be caused by a larger extension of the
area, indicating that the stress field is not homogeneous over the whole region.
5.2 Comparing Fogo and Congro areas
Since the seismicity splits naturally into two different
areas (Fogo and Congro), stress inversion, and rose
diagrams were also made for these two groups (see
Fig. 8b, c).
Considering both groups individually, it can be
observed that the stress tensors are better constrained
than in the other inversions. The dominant T-axis
direction, in the rose diagrams, still follows the commonly observed for the region in both groups, although it is much better defined for Fogo than
Congro. Even the P-axis direction is well defined for
Fogo while quite random for Congro, reflecting the
mixture of different focal mechanisms (Fig. 10). The
stress tensor found for the entire Fogo area dataset is
similar to the stress tensor obtained with all the 78
events, as well as for the October 2004 swarm, which
seems to represent a more homogeneous stress field,
since it is the dataset with the lowest β value. For the
Congro area, σ1 is subvertical, σ2 subhorizontal, striking WNW-ESE and σ3 subhorizontal, striking NNESSW. The 2005 swarm, which occurred in the Congro
area, presents a similar stress field as the whole area
6 Discussion/conclusions
This study has shown that significant swarm activity
has occurred over a long period at São Miguel Island
central region, being particularly representative the
one that took place in 2005. It was observed, for the
first time, that the seismicity is clearly divided into two
areas, Fogo and Congro, and with the exception of the
2005 swarm, most of them were constrained in small
localized areas. A new set of fault plane solutions was
obtained and it was demonstrated that reliable fault
plane solutions can be obtained using signal amplitudes, even in a heterogeneous structure. From all the
events used to build fault plane solutions, 35% occurred during one of the identified swarms (Fig. 5),
being 74% of the solutions obtained for the seismicity
of Congro area while the remaining 26% were related
with the Fogo area. The stress solution found with all
the 78 fault plane solutions shows a minimum compressive stress axis (σ3) striking NNE-SSW, consistent with the regional stress field, with a nearly
subhorizontal WNW-ESE maximum compressive
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Fig. 10 Focal mechanisms of the best located events from each area
stress axis (σ1). Studies based on geological surveys at
São Miguel (NE region) by Carmo (2004) and at
Graciosa Island by Hipólito (2009), found a similar
regional stress field acting at these distinct zones, with
σ1 ranging from NW-SE to N-S, subhorizontal and σ3
from E-W to NE-SW, also subhorizontal. Taken together, all studies point to a minimum compressive
stress axis (σ3) subhorizontal and oriented approximately NE-SW, parallel to the direction of motion of
the Eurasian plate relative to the African plate.
There is a significant difference between the Fogo
and Congro areas seismicity, where the Fogo area
seems to have a more consistent stress field than the
whole study area, and coincident with the regional
tectonic directions (Fig. 11). The Congro area shows
more seismic activity that covers a larger area. The
former has deeper events and a different stress field,
with a permutation between σ2 and σ1, when compared either with the Fogo area or with the regional
directions, suggesting the existence of a very local
heterogeneous stress regime. In some cases, the complex geology and the highly fractured medium, along
with the ascendant hydrothermal fluids, lead to conditions that favour normal faulting (e.g., Giampiccolo
et al. 1999). Therefore, the extensional regime at Congro can be seen as a response to a compressive regime
Fig. 11 Left, sketch map summarizing the main stress directions
found for the Fogo and Congro swarms. The black arrows
represent the directions of the regional minimum compressive
(σ3) and maximum compressive stress axes (σ1). The green
arrows are the principal stress axes found for the October 2004
Fogo swarm (shadowed area to the W) and the red arrows for
the 2005 Congro swarm (shadowed area to the E). These two
directions are also consistent with the direction found for the
whole Fogo and Congro areas, respectively. The red dot represents the near-vertical σ1 axis. Right, sketch of the main directions found for the two ranges of depths with a schematic
interpretation. The σ3 subhorizontal and σ1 subvertical opens
mainly subvertical fractures, which allows the upward flow
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at larger depths. This could promote the ascension of
thermal fluids to crustal lower levels thereby weakening pre-existent fault planes which, due to gravitational stress, become potential slip surfaces.
The seismicity is clearly related to the observed
deformation level changes. However there is no direct
relation between the maximum level changes and the
rate and location of seismicity. This might be due to
the depth of the events, since surface level changes
might not necessarily be related to corresponding level
changes at depth. Various authors have concluded
(Zandomeneghi 2007; Zandomeneghi et al. 2008) that
the main driving force for the level changes is not the
presence of magma but hydrothermal activity. Evidence of the hydrothermal activity is seen in the Furnas Volcano area (Fig. 2), and it is also well documented
North of Fogo, where the hydrothermal field is located
(Johnston 1979 in Silva et al. 1985; Duffield and
Muffler 1984; Zandomeneghi 2007; Zandomeneghi
et al. 2008). A local earthquake tomography study
(Zandomeneghi 2007; Zandomeneghi et al. 2008) of
the central region of São Miguel indicated a positive
Vp anomaly near the Fogo Volcano with a high Vp/Vs
ratio, which seems to indicate that this zone is marked
by low Vs, due to fluids contained in fractures. Thus, this
area might correspond to a transport zone and/or a zone
of crystallization of fluids through a solidified intrusion
(Zandomeneghi 2007; Zandomeneghi et al. 2008).
Seismic tomography studies for the area also related the velocity anomalies to hydrothermal activity
(Zandomeneghi 2007; Zandomeneghi et al. 2008).
This seems reasonable considering that there has not
been any magmatic eruption in the area since 1563, but
frequent and intense seismic activity. Also the typical
low-frequency earthquakes related to magma movements
has not been observed, at least in significant amount. The
magmatic bodies that are in the origin of the secondary
volcanic activity of the Fogo Volcano (such as fumaroles
and thermal springs) appear to induce upflow of the
thermal fluids, acting as a heat source and promoting
the convection of the hydrothermal system (Duffield
and Muffler 1984; Gandino et al. 1985; Silva et al. 1985).
For shallow depths, the predominance of subhorizontal σ3 (D < 5 km) is compatible with what is
expected beneath volcanic areas (e.g., Legrand et al.
2002; Sánchez et al. 2004; Konstantinou et al. 2009).
It is thought that rising fluids can generate horizontal
extensional, which would favour the opening of cracks
and the ascent and circulation of hydrothermal fluids.
This might explain the observed fumarolic activity
around Fogo Volcano. The results obtained in the
inversion for events deeper than 5 km are not reliable;
however the stress regime is clearly different below
5 km as compared to above 5 km, indicating different
deformation regimes.
The main importance of this study is that it has
been shown that the seismic swarm’s area on São
Miguel central region is not a uniform area of activity,
but clearly can be divided into two main areas, Congro
and Fogo, each with localized swarm activity. The
smaller Fogo area appears dominated by the normal
regional stress field, while the high activity at Congro
area seems to have a different, highly heterogeneous
stress field. This could indicate that the Congro area is
affected by abnormal local stresses caused by deformations and/or large pressure changes in the first
10 km below the surface.
Acknowledgments Rita Silva was supported by a Ph.D. grant
from Direcção Regional da Ciência e Tecnologia, Região
Autónoma das Açores (Azores Government) during this work.
CIVISA partially supported the stay of Jens Havskov at the
Azores.
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