Geophysical Journal International
Geophys. J. Int. (2011) 187, 128–146
doi: 10.1111/j.1365-246X.2011.05137.x
Earthquake swarms in South America
S. G. Holtkamp,1,2 M. E. Pritchard2 and R. B. Lohman2
1 Geology
Department, Miami University, Oxford OH, USA. E-mail: [email protected]
of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, USA
2 Department
Accepted 2011 June 29. Received 2011 June 29; in original form 2009 December 29
GJI Gravity, geodesy and tides
SUMMARY
We searched for earthquake swarms in South America between 1973 and 2009 using the global
Preliminary Determination of Epicenters (PDE) catalogue. Seismicity rates vary greatly over
the South American continent, so we employ a manual search approach that aims to be
insensitive to spatial and temporal scales or to the number of earthquakes in a potential swarm.
We identify 29 possible swarms involving 5–180 earthquakes each (with total swarm moment
magnitudes between 4.7 and 6.9) within a range of tectonic and volcanic locations. Some
of the earthquake swarms on the subduction megathrust occur as foreshocks and delineate
the limits of main shock rupture propagation for large earthquakes, including the 2010 M w
8.8 Maule, Chile and 2007 M w 8.1 Pisco, Peru earthquakes. Also, subduction megathrust
swarms commonly occur at the location of subduction of aseismic ridges, including areas
of long-standing seismic gaps in Peru and Ecuador. The magnitude–frequency relationship
of swarms we observe appears to agree with previously determined magnitude–frequency
scaling for swarms in Japan. We examine geodetic data covering five of the swarms to search
for an aseismic component. Only two of these swarms (at Copiapó, Chile, in 2006 and
near Ticsani Volcano, Peru, in 2005) have suitable satellite-based Interferometric Synthetic
Aperture Radar (InSAR) observations. We invert the InSAR geodetic signal and find that the
ground deformation associated with these swarms does not require a significant component
of aseismic fault slip or magmatic intrusion. Three swarms in the vicinity of the volcanic arc
in southern Peru appear to be triggered by the M w = 8.5 2001 Peru earthquake, but predicted
static Coulomb stress changes due to the main shock were very small at the swarm locations,
suggesting that dynamic triggering processes may have had a role in their occurrence. Although
we identified few swarms in volcanic regions, we suggest that particularly large volcanic
swarms (those that could be detected using the PDE catalogue) occur in areas of infrequent
eruption and may be related to large regional fault zones.
Key words: Transient deformation; Radar interferometry; Subduction zone processes; South
America.
1 I N T RO D U C T I O N
Clustering of earthquakes in space and time indicates that interaction between earthquakes is an important component of the seismic
cycle. One manifestation of earthquake clustering is the occurrence
of ‘seismic swarms’, which can be defined as an increase in seismicity rate that lacks a clear triggering main shock earthquake
(Mogi 1963; Sykes 1970; Hill 1977). Seismic swarms occur in a
variety of different environments and might have a diversity of origins. Swarms are of interest because they might provide clues about
poorly constrained subsurface processes like aseismic fault slip (e.g.
Lohman & McGuire 2007; Ozawa et al. 2007; Wolfe et al. 2007)
and fluid or volatile movement (e.g. Brauer et al. 2003).
Earthquake swarms in volcanic regions have been extensively
studied because they are often associated with eruptions or magmatic intrusions (Benoit & McNutt 1996). Large earthquakes may
128
be inhibited in volcanic areas due to substantial heterogeneity in
material or thermal properties, localized high fluid pressure which
acts to reduce the failure shear stress by reducing the effective normal stress or high stress concentrations that are spatially limited by
magmatic movements (Benoit & McNutt 1996).
Earthquake swarms not clearly associated with volcanism have
been documented at strike slip and convergent boundaries around
the world. Swarms along transform faults often occur at releasing bends (e.g. Shibutani et al. 2002; Lohman & McGuire 2007),
and in these cases the geodetic and seismic observations cannot
be explained by magmatic processes alone and require that we
invoke aseismic slip or some other driving force. However, the
common association of releasing bends with volcanic and geothermal activity suggest that these underlying causes cannot be completely decoupled from magmatic involvement. Earthquake swarms
at subduction margins have been documented in New Zealand
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Geophysical Journal International Earthquake swarms in South America
(Evison & Rhoades 1993), Japan (Fujinawa et al. 1983; Matsuzawa
et al. 2004), Kamchatka (Zobin 1996), Mexico (Zobin 1996) and
South America (Lemoine et al. 2001). The origin of these swarms
and their potential interaction with larger main shock events is debated (e.g. Evison & Rhoades 1993; Llenos et al. 2009).
The goal of this paper is to create a uniform catalogue of swarms
in South America to better understand their basic characteristics, any
interaction between earthquake swarms and other geological processes (megathrust earthquakes, volcanic eruptions, crustal faults,
etc.), and whether the swarms are associated with any observable
aseismic deformation. South America has not been the focus of any
broad earthquake swarm studies like in southern California or Japan
(e.g. Vidale et al. 2006; Vidale & Shearer 2006), although several
swarms have been documented in detail (Lemoine et al. 2001; Gardi
et al. 2006; Barrientos et al. 2007).
This work involves a search for swarm-like activity all throughout the South American continent using the National Earthquake
Information Center’s (NEIC) Preliminary Determination of Epicenters (PDE) catalogue. We report on 29 potential earthquake
swarms and describe the environments in which they occurred, as
well as possible triggering mechanisms. We use satellite-based Interferometric Synthetic Aperture Radar (InSAR) observations to
determine if two of the swarms were associated with any aseismic deformation. We also model static Coulomb stress change for
two swarms apparently triggered by an M w = 8.5 earthquake in
southern Peru to test whether static stress changes due to the main
shock could have had a role in triggering the swarms. We conclude
that aseismic ridge subduction and large megathrust earthquakes
affect where swarms occur (Section 4.1), swarms may follow a
magnitude–frequency scaling relation (Section 4.2) and swarms
that appear to be triggered are dynamically, not statically triggered
(Section 4.4).
2 D ATA A N D M E T H O D S
2.1 Approach for identifying swarms
We examine the complete PDE catalogue for the western half of the
South American continent, which spans from latitude 13◦ N to 57◦ S
and longitude 63◦ to 83◦ W (http://neic.usgs.gov/neis/epic/epic_
global.html). The completeness threshold for the PDE catalogue,
which limits the resolution of our survey, is spatially heterogeneous as it depends on station distribution. The PDE completeness
threshold is also temporally variable—the threshold has generally
decreased over time as more stations are installed in the global network. In recent years, we determine the completeness threshold to be
slightly less than M w = 4.5 (Holtkamp 2010). For the sake of comparison, we find that although a local catalogue from the Instituto
Geofı́sico del Peru includes some events not in the PDE catalogue,
overall the PDE catalogue contains many more earthquakes (Jean
Paul Ampuero, personal communication, 2004).
Because the PDE catalogue reports a compilation of different
catalogues (global and local), one must take care when comparing
magnitudes. In the past ∼20 yr, earthquakes larger than M w 4.5
have reported moment magnitudes, while prior to that only earthquakes larger than M w 6 include moment magnitudes estimates.
When a body wave magnitude was given but no moment magnitude (which in the past 20 yr generally only occurs for earthquakes
between M w 4.0 and M w 4.5), body wave magnitude was
converted to moment magnitude by adding 0.31 to the body wave
magnitude (Stein & Wysession 2003). Earthquakes with only ML,
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Geophysical Journal International 129
or local magnitudes, were ignored in all but a few cases that are
explained in the text.
To examine each cluster of seismicity in the catalogue, we extract all earthquakes in a moving window over a grid of South
America with no restriction on depth. Within each grid search area,
we plot magnitude versus time for all earthquakes in that area to
identify seismicity rate changes or bursts of seismicity. For each
case where an apparent increase in seismicity was not accompanied by a large earthquake, we focused on the cluster in question to
determine the nature of the seismicity. To accomplish this, we simultaneously examined the time–magnitude distribution of earthquakes
on a 15-yr timescale to get a sense of the background seismicity,
the time–magnitude distribution in a short time period bracketing
the potential swarm and a map view image of seismicity in a window completely encompassing the potential swarm (e.g. Fig. 6). We
define an earthquake swarm based on three broad characteristics,
although all three are not required for us to flag a seismicity burst
as a potential earthquake swarm: (1) when a notable seismicity rate
increase is not accompanied by a clear triggering main shock, (2)
seismicity in the burst does not follow Bath’s law (the second largest
earthquake is typically about one moment magnitude less than the
largest) and (3) seismicity in the burst begins and ends relatively
abruptly in comparison to the background seismicity rate (i.e. it does
not resemble an aftershock sequence following Omori’s law with an
exponential decay in seismicity rate). A particular concern for this
type of search in South America is that the initial set of aftershocks
after a large earthquake (particularly along the subduction zone)
could extend for hundreds of kilometres (e.g. Lay & Wallace 1995),
so in this analysis, it is important that the seismicity be completely
contained (top panels in all swarm figures, we nominally choose a
box size of 3◦ ).
We chose a manual search methodology to restrict the number
of false negatives, as the highly variable nature of seismicity rates
and completeness combined with our criteria to not be insensitive
to spatial scales made an automated search difficult (Holtkamp &
Brudzinski 2011). It is worth noting that this methodology may
introduce some systematic bias. For example, swarms are difficult
to determine visually in areas with high background seismicity
rate (e.g. central Chile, between 30◦ and 35◦ S contains half of
the reported earthquakes in South America). Also, it is difficult to
identify swarms that follow or are triggered by large earthquakes and
aftershock sequences. For example, when we discuss the 2001 Peru
earthquake and its subsequent seismicity in Section 4.4, we take
care in defining this distinction between aftershocks and swarms.
2.2 InSAR methodology
We searched the European Space Agency’s (ESA) ERS-1, ERS-2
and Envisat radar satellites catalogues for SAR acquisitions spanning each swarm. We found acquisitions bracketing five swarms
[See Table 1. Note that Fukushima (2007) use InSAR to investigate
the 2007 Aysen swarm with the Japanese Advanced Land Observation Satellite, or ALOS.]. For this study, we focused on swarms
that had larger maximum magnitudes (∼6) because these have the
highest probability of causing sufficient ground deformation to be
detected geodetically.
We processed interferograms with the ROI_PAC software package maintained by JPL/Caltech (Rosen et al. 2004). Interferograms
were initially processed using orbits provided by the ESA and available online, but long wavelength ramps were present after this initial
processing step in each interferogram. These ramps are often due
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S. G. Holtkamp, M. E. Pritchard and R. B. Lohman
Table 1. Interferograms made. Bperp is the perpendicular baseline between
the two satellite passes. The Track column has the format ‘Satellite’-T‘
Track Number’, where satellite is either ERS or IM‘number’ (ENVISAT
Image mode number). For example, IM2-T447 is Envisat image mode 2,
track 447.
Location
Master date
Slave date
Track
Bperp
Copiapó
Copiapó
Copiapó
Ticsani
Ticsani
Ticsani
Ecuador
Ecuador
Ecuador
S. Peru (Coropuna)
S. Peru (Coropuna)
Peru (Pisco)
Peru (Pisco)
Peru (Pisco)
06/18/2007
01/14/2008
03/05/2007
01/22/2005
06/17/2006
06/14/2006
02/11/2006
02/11/2006
07/12/2003
01/09/2002
01/13/2003
07/28/2006
10/19/2007
08/17/2007
08/22/2005
08/22/2005
09/06/2004
01/07/2003
12/04/2004
01/05/2005
07/12/2003
06/07/2003
06/07/2003
04/09/1996
10/06/1997
02/18/2005
10/10/2003
2/18/2005
ERS-T96
ERS-T96
ERS-T96
IM2-T411
IM4-T361
IM2-T318
IM2-T068
IM2-T068
IM2-T068
ERS-T225
ERS-T497
IM2-T447
IM2-T354
ERS-T447
300
260
230
20
30
20
30
100
80
40
40
100
60
190
Okada’s rectangular dislocation solution in an elastic half-space
(Okada 1985) to model the observed deformation. We invert the
geodetic data with the Neighbourhood Algorithm, a non-linear,
global inversion scheme useful in exploring several different model
parameters efficiently (Sambridge 1998), including the fault plane
location and orientation. Our primary goal in performing inversions
of these geodetic observations is to determine if any aseismic slip
component is required to predict the observed ground deformation. Any disagreement between the seismically and geodetically
constrained moments could indicate aseismic slip had occurred.
3 R E S U LT S
to uncertainties in satellite orbital locations, so we fit them using
a quadratic ramp and removed them before proceeding. Interferograms were then down-sampled, unwrapped and geocoded using
ROI_PAC.
We down-sampled the interferograms using the resampling tool
of Lohman & Simons (2005), which relies on an initial fault plane
estimate to identify important regions in the interferogram. We use
We identified 29 swarm-like clusters of earthquakes. A summary
of all swarms is presented in Table 2 and Fig. 1. Characteristics
of swarms are diverse in geological setting, relation to other earthquakes, duration, spatial extent, number of earthquakes and magnitudes of earthquakes. In all, we associate close to 1000 earthquakes
with swarms out of a total of 50 000 earthquakes in the PDE catalogue, with moment magnitudes up to M w = 6.7. In the following
sections, we discuss in detail a few of the eight previously discovered and 21 new swarms—complete information on all of the
swarms is available in Holtkamp (2010).
3.1 Previously discovered swarms
A key test for the effectiveness of our effort to compile a thorough list of swarms is whether our method identifies previously
Table 2. South American earthquake swarms. a Denotes previously discovered swarms. b Denotes swarms examined
with InSAR. Duration is in years. Lat and long are the approximate coordinates of the centre of the swarm. The
‘environment’ variable is determined from the tectonic environment of the swarm (between the trench and the 50-km
depth contour is ‘megathrust’, anywhere near the arc is ‘volcanic’). The ‘volcanic’ label only implies the swarm is in
the vicinity of volcanoes, not that it is necessarily a volcanic earthquake swarm. For figures referenced as ‘T’ the reader
is referenced to Holtkamp (2010).
Date
Lat.
Long.
Duration (yr)
Num. EQs
Total M w
Fig.
Environment
Location
1973.5
1976.9
1977.3
1979.3
1980
1985.1
1985.3
1986.15
1991.6
1994
1997.5
1999.3
1999.45
1999.84
2000.6
2001.45
2001.45
2001.45
2001.8
2003.4
2005.05
2005.2
2005.61
2005.55
2006.3
2006.7
2007
2008.34
2008.40
−26.83
−11.93
−1.36
−27.15
−12.93
−33.08
0
−17.43
−44.93
−33.2
−30.52
−33.33
−33.33
−38
−5.36
−15.4
−15.41
−17
−33.2
−32.34
−1.36
−14.77
−34.3
−16.64
−27.02
−33.2
−45.24
−42.7
−42
−70.92
−73.5
−80.79
−71.05
−74.5
−71.85
−80.5
−65.5
−72.5
−72.2
−71.86
−72.29
−72.29
−72.5
−76.62
−72.2
−70.36
−70.25
−72.2
−72.19
−80.79
−76.54
−72.5
−70.79
−71.02
−72.2
−72.65
−72.5
−72.3
0.12
0.25
0.05
0.03
0.8
0.01
0.2
0.25
0.07
0.1
0.04
0.02
0.1
0.15
1.7
.15
0.5
0.15
0.01
0.02
0.09
0.8
0.01
0.25
0.1
0.1
0.25
0.01
0.02
72
14
9
12
12
15
5
7
13
10
32
25
50
11
15
31
16
20
10
25
39
15
9
38
180
14
15
10
7
6.7
5.9
5.6
6.2
5.8
5.8
4.9
5.8
6.1
4.4
6.9
4.9
4.8
5.8
5.6
6.1
5.6
5.8
4.9
5.2
6.6
5.5
4.7
6.0
6.9
4.7
6.4
5.6
5.7
5
T1.30
T1.25
S2
T1.31
S6
T1.27
T1.32
T1.23
Megathrust
?
Megathrust
Megathrust
?
Megathrust
Megathrust
Sub-Andean
Volcanic
Megathrust
Megathrust
Megathrust
Megathrust
Megathrust
?
Volcanic
Volcanic?
Volcanic
Megathrust
Megathrust
Megathrust
Megathrust
Megathrust
Volcanic
Megathrust
Megathrust
Volcanic
Volcanic
Volcanic
Copiapóa
C. Peru
Ecuador
Copiapó
C. Peru
Valparaiso
Ecuador
Bolivia
Hudson
Topocalma
Punitaquia
Topocalma
Topocalma
Arauco
N. Peru
Coropunab
Titicaca
Tutupaca
Topocalmaa
Papudo
Ecuadorb
Piscob
34S
Ticsania,b
Copiapóa,b
Topocalma
Aysena
Chaiténb
Hornopiréna
T1.12
11
T1.35
10
10
10
S7
S8
8
9
T1.21
2
6
T1.22
S9
S9
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131
Punitaqui, the subduction of the Juan Fernandez Ridge, and southern
Chilean volcanoes Chaitén and Hornopirén in the online Supporting
Information.
3.1.1 Southern Peru and Ticsani Volcano, 2005
Figure 1. Combined topography/bathymetry of South American region
studied in this paper. Red circles and associated dates provide times and
locations of all swarm events discussed in the Results section. Size of red
circles is approximately the size of the zone of shocks associated with the
swarms. Thick black lines provide plate boundary information from Bird
(2003) and thin dashed lines show depth to slab contours every 50 km (from
Syracuse & Abers 2006).
discovered swarms documented in the literature or meeting abstracts. Our search was ‘blind’ in the sense that we performed the
swarm search described above before searching the literature for
swarms. Our swarm search method successfully identified all eight
of the swarms known in the literature that are above or near the
catalogue completeness threshold (∼4–5). We discuss swarms at
Ticsani Volcano and Copiapó, Chile here. We discuss swarms at
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Geophysical Journal International Fig. 2 shows a swarm that occurred near Ticsani Volcano and
Laguna Viscacha in the middle of 2005 (Edmundo Norabuena,
personal communication, 2005). The swarm begins between Ticsani and Viscacha and includes a burst of seismicity 2 months later
beneath Ticsani. The relationship between the burst of seismicity
beneath Ticsani Volcano and the swarm was examined by Gonzáles
et al. (2006) and we explore this further. This swarm contains two
different types of clustering activity in adjacent regions: the southeastern cluster spans the whole 3 months and is more uniformly
distributed in time (Fig. 2, purple to green events) whereas the
northwest cluster appears as a sudden, short burst (Fig. 2, yellow
events).
Ticsani Volcano lies on the Andean Plateau in southern Peru
where the altitude and climate of the plateau are conducive to maintaining radar coherence over long time spans. The volcanic regions
of the Andes are also relatively well sampled by imagery from the
existing SAR satellite platforms, so several different InSAR pairs
exist that document the deformation associated with this swarm.
Fig. 3 shows seven interferograms spanning times that do not contain the swarm (panels a–g) and three interferograms from times
that do contain the swarm (panels h–j). The interferograms appear
to contain three regions of deformation, most clearly visible in
Fig. 3 (panel h). The southeastern region of deformation beneath
Laguna Viscacha first appeared in interferograms that spanned 2003
January to 2004 March, well before the seismic swarm took place.
No swarms or other types of anomalous seismicity exist in the global
catalogues during this time period at this location. The other two
areas of observed deformation (in the centre and northwest corner
of Fig. 3, panel h) are most likely associated with the swarm activity, although the temporal relationship between the deformation
signal and the earthquakes is impossible to constrain from these
interferograms because of the long time between SAR acquisitions.
The region of deformation closest to Ticsani Volcano includes
two prominent lobes of deformation, which are often indicative of a
double-couple source mechanism (Fig. 3, panels h–j). We focus on
this deformation signal when resampling the three interferograms
and inverting for the deformation source with the Neighbourhood
Algorithm (see Supporting Information; Sambridge 1998). This deformation signal is sampled by three different Envisat tracks: track
318 beam mode 2, track 411 beam mode 2 and track 361 beam mode
4, representing three different incidence angles and both ascending
and descending orbital directions. Table 3 illustrates the best-fitting
parameters chosen by the Neighbourhood Algorithm with comparisons of data and models in Fig. 4. The inversion arrived at a total
moment equivalent to an M w = 5.7 event, not significantly different than the total seismic moment from the swarm as constrained
by earthquakes in the earthquake catalogues (PDE catalogue total
M w = 5.75). This suggests that no significant aseismic moment
release in addition to the swarm activity is required by the data.
Lavallee et al. (2009) show that Ticsani and other volcanoes form
the Ubinas–Huaynaputina–Ticsani Volcanic Group (UHTVG) and
suggest that volcanism is controlled by a regional fault system.
Near Ticsani, this fault system has a strike of ∼305◦ . The strike
between the deformation signals in Fig. 3 is ∼295◦ to ∼300◦ , suggesting that the source of these deformation signals may be connected through this regional fault system.
132
S. G. Holtkamp, M. E. Pritchard and R. B. Lohman
Figure 2. The 2005 Ticsani earthquake swarm. Top panel shows a map view of seismicity with circle size representing M w and colour representing time as
shown in the middle panel. Small inset map in the top right locates the area of interest, with red ellipses corresponding to black ellipses in the top panel and
representing large megathrust ruptures. The dashed and labeled lines on the map show depth to slab contours from Syracuse & Abers (2006). Red triangles are
volcanoes. The middle panel shows PDE reported seismicity in a small time window just bracketing the seismicity and within the area shown in the top panel.
The bottom panel contains 15 yr of seismicity to show the background seismicity rate. Thin vertical lines in the bottom panel show beginning and ending times
of the middle panel. When present, stars in the top and bottom panels show epicentres of earthquakes with M w > 6.5 within the 15 yr time span shown in the
bottom panel. InSAR associated with the swarm is discussed in the text, and coloured vertical lines in the bottom panel show the acquisition dates, with similar
colours representing independent interferograms made. The same scheme will hold for all subsequent figures of swarm seismicity.
3.1.2 Central Chile, 1973, 1979 and 2006 Copiapó Swarms (27◦ S)
Comte et al. (2002) report a swarm during July and August of 1973
using ISC reported earthquakes. We also document this swarm,
and find that it contains at least 72 earthquakes, with a maximum
magnitude of M w = 6.3. The swarm began with earthquakes near
27◦ S and propagated to the north. Comte et al. (2002) report this
swarm to be in the middle of the 1922 M w = 8.2 rupture zone and
marks the southern terminus of the 1983 M w = 7.4 earthquake. They
suggest that if this swarm were on the subduction zone interface
and not in the continental crust above it, this would indicate that
the region north of the swarm was not ready to rupture in 1973.
The 1973 Copiapó swarm and its relation to the 1983 earthquake is
shown in Fig. 5.
Our swarm search identified an additional swarm in 1979 not
discussed by Comte et al. (2002) probably because it contains a
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Figure 3. Interferograms of the 2005 Ticsani earthquake swarm. The first seven interferograms show no deformation at or near Ticsani, but some do show
deformation near Laguna Viscacha by at least March 2004. Focal mechanisms and timing of the earthquakes match the source observed at Ticsani, so the
discrepancy in the location of the events is likely due to epicentral errors in the CMT catalogue. Red triangles with black outline represent volcanoes.
much smaller number and magnitude of earthquakes than the 1973
swarm. The 1979 swarm is shown in Fig. S2 (Supporting Information) and contains only 12 earthquakes, with a maximum magnitude
of M w = 5.6.
More recently, in April to May of 2006, a swarm consisting of
approximately 180 earthquakes observed within the PDE catalogue
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of the 1973 Copiapó swarm. This swarm was identified and examined by Comte et al. (2006). They present evidence that seismicity
within this swarm is correlated with the location of a subducting
seamount. In addition, they find that events occur in areas of low
Vp and high Vp /Vs ratio, as constrained by an on and off shore
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S. G. Holtkamp, M. E. Pritchard and R. B. Lohman
Table 3. Neighbourhood Algorithm inversion results for the Ticsani and Copiapó swarms.
Location
Lon
Lat
Depth
Strike
Dip
Rake
L
W
Mw
M0
Slip
Ticsani
Copiapó
−70.6367
−71.2099
−16.7267
−27.1254
4.03
24.8
339
15
62
21
−90
80
4.98
46
4.98
31
5.61
6.87
3.42e24
2.51e26
0.39
0.55
Figure 4. Inverse model of the 2005 Ticsani earthquake swarm. Red is an increase in LOS displacement or subsidence, blue is uplift. The subsidence-only
pattern to the southeast is effectively removed during resampling of the deformation field so is not modelled or removed.
temporary seismic network (Comte et al. 2002, 2006). Seismic
anomalies of low Vp and high Vp /Vs ratio are consistent with the presence of excess fluids in the area because fluids are seismically slow
but affect shear velocities more than compressional velocities (e.g.
Lay & Wallace 1995). The 2006 Copiapó swarm is shown in Fig. 6.
This swarm may exhibit north to south propagation of hypocentres (Holtkamp 2010) but there is not enough spatial resolution in
the NEIC catalogue to rule out discrete jumps in seismicity as the
source of this apparent propagation. A best-fitting line to the main
part of the seismicity, from ∼2006.335 to ∼2006.345, prefers a rate
of 7.4 km d−1 ±1.5 km d−1 of along-strike epicentral propagation—
the potential significance of this value will be discussed in
Section 4.2.
The aridity of the region (e.g. Montgomery et al. 2001) allowed
us to make three interferograms with SAR imagery from Envisat
track 96 beam mode 2 that span the swarm—two share a common
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Figure 5. 1973 Copiapó Earthquake swarm.
scene and one is completely independent. The dates of these acquisitions are shown in the bottom panel of Fig. 6 and in Table 1.
We simultaneously inverted all three interferograms to reduce the
impact of atmospheric noise and we explored several inversion approaches (described in the Supporting Information) to arrive at a
solution consistent with a priori information about the location of
the subduction zone interface inferred from a local seismic network
(Comte et al. 2002). The location of the preferred fault plane with respect to subduction zone seismicity is shown in Fig. S4 (Supporting
Information).
Comparisons of data and ground deformation predicted from our
preferred inversion are shown in Figs 7 and S3. Our model suggests
that the observed ground deformation does not require a significant
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almost the same total moment release (M w = 6.87) as the swarm
in the PDE catalogue (M w = 6.89). However, the inversion does
not rule out the potential for some aseismic slip since we find there
is a large trade-off between inverted fault depth (which is not well
constrained with InSAR because only one lobe of the deformation
pattern is observed) and the magnitude of slip on that fault. The
best-fitting result (without using any a priori information described
above) is about 15 km deeper than the seismicity on the subduction
interface reported by the Comte et al. (2002) and contains approximately three times the total seismic moment release from the swarm
in the CMT catalogue. This result is inconsistent with the location
of the megathrust, which is well constrained by a local on- and
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Figure 6. 2006 Copiapó earthquake swarm. InSAR associated with the swarm is discussed in the text (Table 1), coloured vertical lines in the bottom panel
show the acquisition dates, with similar colours representing independent interferograms made.
off-shore seismic network (Comte et al. 2002). If the swarm occurred on a crustal fault shallower than the subduction interface,
our inversions suggest that the geodetic moment would be smaller
than the seismic moment.
3.2 Newly discovered earthquake swarms
In addition to the eight swarms documented in the literature and
discussed above, we found 21 additional swarms. Newly discovered
swarms described hereafter were probably recognized by local populations because most contained earthquakes that were large enough
to be felt.
3.2.1 Ecuador swarms at Carnegie Ridge intersection
Historical seismicity for Ecuador shows there is a sizable seismic
gap between about 0◦ and 10◦ S (e.g. Swenson & Beck 1996). This
section of the margin accommodates a large convex bend in the
trenchant, the subduction of the Carnegie Ridge, which is related
to the Galapagos hotspot to the west, from about 0◦ to 2.5◦ S, both
of which have been postulated to produce enough heterogeneity at
the plate interface to prevent the propagation of large earthquake
ruptures into or throughout the region.
We found two swarms at the intersection of the aseismic Carnegie
Ridge, near the coastal city of Manta and the southern part of
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Figure 7. Comparison between full resolution data and best-fitting inverse model after using a priori information (megathrust seismicity, Fig. S4—Supporting
Information) for the 2006 Copiapó swarm. All profiles are taken from the same swath profile as shown in the data and residual columns, with the first profile in
each case representing all the data and model points and the second profile showing just the resampled data and model points. Model and residual interferograms
are based on linear interpolation of the resampled interferograms onto the full resolution interferogram. For additional comparisons between data and modelled
interferograms, see Fig. S3. For a distributed slip model, see Fig. S5.
Manabi, Ecuador. The first occurred in 1977 and lasted about a
month. The maximum magnitude associated with the 1977 swarm
is small, only M w = 5.6, and few events were recorded. The second
swarm occurred in 2005 and is shown in Fig. 8. The 2005 swarm was
much more energetic with moment magnitudes up to 6.2 and the
total sum of the moment was equivalent to an M w = 6.6 earthquake.
This swarm showed bilateral propagation of epicentres at rates of
∼4.5–10 km d−1 (Holtkamp 2010), however the PDE catalogue
does not have enough resolution to tell if this propagation occurred
smoothly or as discrete jumps.
The coast of Ecuador receives a substantially larger amount of
rainfall than northern Chile and southern Peru, so InSAR coherence
degrades faster, particularly for the available C-band data. Only
one track in Ecuador has acquisitions spanning the swarm, and we
acquired and processed three scenes to test the coherence near the
time of the swarm (Table 1 and Fig. 8). Radar coherence associated
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is maintained over short time intervals (on the order of a month), it
is almost entirely lost over longer intervals (Holtkamp 2010). The
only scenes spanning the swarm are over 1 yr apart, so it does not
appear InSAR can provide geodetic observations of this swarm.
3.2.2 North and central Peru at ∼5◦ S
In 2001–2002, a pair of small clusters of earthquakes (2001 March
and 2002 June) occurred at the eastern edge of the Eastern Cordillera
in northern Peru. There are less than 10 earthquakes in both clusters, so definitively labelling this as a swarm or otherwise is not
certain. Each of the earthquakes was either near the plate interface (106–117 km depth) or was assigned the default depth
in the region of 33 km. If all of the events did occur near the
plate interface at ∼100 km depth, this swarm would be the only
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Figure 8. 2005 earthquake swarm in southern Ecuador. InSAR associated with the swarm is discussed in the text, and coloured vertical lines in the bottom
panel show the acquisition dates, with similar colours representing independent interferograms made.
one that we found that was not either on or near the shallow
megathrust or in the upper plate crust. Also, two small swarms occurred on the plateau above the Peruvian flat slab segment at −12◦
and −13◦ S.
3.2.3 Prior to the 2007 Peru earthquake
An M w = 8.1 earthquake occurred off Pisco, Peru on 2007 August
15. This earthquake was preceded a year earlier by an M w = 6.7
foreshock with an epicentre very close to the 2007 epicentre. Both
the foreshock and the main shock were near the northern edge of
the rupture zone of the M w = 8.1 earthquake (Motagh et al. 2008;
Pritchard & Fielding 2008; Biggs et al. 2009; Sladen et al. 2010). A
pair of swarms in early 2005 and early 2006 occurred south of the
epicentre of the 2007 event (Fig. 9) near the southern terminus of the
aftershock sequence. Perfettini et al. (2010) show that this area, to
the south of the rupture, experienced the maximum amount of postseismic afterslip (constrained by GPS). The location of the swarm
at the southern terminus of the main shock rupture and a region
of significant aseismic afterslip highlights a potential relationship
between these processes, and will be discussed in Section 4.1.
To test whether or not the 2005–2006 swarm was accompanied
by aseismic slip, we examine interferograms formed from acquisitions made before and after the earthquake. For interferograms
spanning both the swarm and the earthquake, we remove a bestfitting joint InSAR-seismic model for the earthquake (Pritchard &
Fielding 2008). Residuals in both cases are on the order of several
centimetres and can be explained by atmospheric effects. It appears
that any slip associated with the swarm is below the observation
threshold for InSAR (Holtkamp 2010).
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Figure 9. 2005 Pisco, Peru earthquake swarm preceding the 2007 Pisco earthquake. The epicentre of the M w = 8.1 Pisco earthquake is shown as a star and
the aftershocks of this earthquake are hollow circles. Pink lines are 2 m slip contours from Sladen et al. (2010). Gridded fault slip data (colours) are GPS
constrained afterslip from Perfettini et al. (2010). InSAR associated with the swarm is discussed in the text, and coloured vertical lines in the bottom panel
show the acquisition dates, with similar colours representing independent interferograms made.
3.2.4 Triggered seismicity after the 2001 Peru earthquake
The M w = 8.5 2001 southern Peru earthquake seems to have
triggered seismicity up to several hundred kilometres away from
the rupture zone (Devlin 2008) in four distinct areas. Several
large earthquakes around the world have been documented to trigger seismicity at distances beyond what would normally be considered the aftershock zone (e.g. Hill et al. 1993; Husen et al.
2004). We identified three clusters of seismicity as swarms. The
first swarm contained two distinct bursts of seismicity located beneath Coropuna Volcano and to the southeast of Coropuna Vol
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(2007) show deformation associated with Coropuna Volcano and
the swarm shown in Fig. 10, however the nature of the deformation field is complex and its relation to the swarm is unknown.
The second swarm occurred to the west of Lake Titicaca and the
third occurred near Tutupaca Volcano. Tutupaca Volcano has postglacial lava flows (de Silva & Francis 1991) and reported solfataric activity (Smithsonian Institution 1991), suggesting that active
fluid circulation may play a role in triggering at this volcano. We
will discuss stress transfer as a potential triggering mechanism in
Section 4.4.
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S. G. Holtkamp, M. E. Pritchard and R. B. Lohman
Figure 10. 2001 Peru earthquake and triggered seismicity at Coropuna, Tutupaca and Titicaca. InSAR associated with the swarm is discussed in the text.
3.2.5 Central Chile at ∼38◦ S
We find one swarm in late 1999 near the Arauco Peninsula (Fig. 11).
This swarm is immediately south of and adjacent to the aftershock
zone of an M w = 6.6 earthquake that struck the region in 2004 and
is shown as a star in Fig. 11. The earthquake was not the subject
of any previous studies, probably because it was not a damaging
earthquake and is relatively small compared to other earthquakes
in South America. The recent M w = 8.8 2010 February Chilean
earthquake occurred to the north of this swarm, and we will discuss
possible interaction between these events in Section 4.1.
4 DISCUSSION
A key motivation for this work is to assess whether there is a link
between aseismic fault slip, fluid or magmatic movements and seis-
mic swarms. Direct geodetic evidence (Lohman & McGuire 2007;
Ozawa et al. 2007; Wolfe et al. 2007) suggests that aseismic slip
sometimes coincides with earthquake swarms, but where no geodetic data exists this suggestion has been based on an expansion or
propagation of hypocentres at rates faster than fluid diffusion is
likely to occur (e.g. Vidale & Shearer 2006). Alternatively, fluid
or volatile movement appears to dominate some swarm regions.
The Vogtland/NW Bohemia swarm region is extensively studied
(e.g. Fischer & Horalek 2003) and interpreted to represent devolatilization of an active magma body (Brauer et al. 2003). This devolatilization is thought to be the forcing mechanism behind events
with significant non-double-couple moment tensors (Horalek et al.
2002).
Although we did not find unambiguous evidence for aseismic
deformation associated with swarms in South America, we discuss
several properties of these swarms that are consistent with aseismic
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Figure 11. 1999 Arauco Peninsula earthquake swarm.
deformation observed with swarms elsewhere. Thus, we suspect
that aseismic deformation may have been present during at least
some of the swarms, but was not observed either due to lack of
available data or because the deformation was below our detection
threshold.
4.1 Ridge subduction and megathrust segmentation
We find swarms on or near the megathrust in some interesting and
unique regions of the South American margin. There are three main
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Ridge in Ecuador, the Nazca Ridge in Peru and the Juan Fernandez
Ridge in Chile. All three of these ridges have been associated with
earthquake swarms in the past 40 yr (most easily seen in Fig. 1). The
Carnegie and Nazca ridges have been characterized by prominent
seismic gaps (e.g. Swenson & Beck 1996, 1999), and the 2007
Pisco earthquake was shown to only partially fill the Nazca gap (e.g.
Pritchard & Fielding 2008; Perfettini et al. 2010). There are two endmember models proposed for why seismic gaps occur. Either (1) the
fault is fully locked and accumulating stress to be released in a great
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S. G. Holtkamp, M. E. Pritchard and R. B. Lohman
earthquake or (2) the fault is unable to accumulate stress and will
never rupture in a great earthquake. If the swarms in Ecuador are
associated with significant aseismic moment release as observed in
other areas (e.g. Lohman & McGuire 2007; Ogata 2007) this could
possibly explain part of the seismic gap in the southern Ecuador
region, as frequent aseismic stress release could prevent the fault
from loading.
Two recent earthquakes in South America show potential interaction with earthquake swarm regions, the 2007 Pisco earthquake and
the 2010 Chile earthquake. In Peru, Pritchard & Fielding (2008)
demonstrate that a seismic gap still remains after the 2007 Peru
earthquake, particularly at the crest of the incoming Nazca Ridge
to the south of the 2007 rupture zone. This region above the incoming Nazca Ridge has experienced geodetically constrained afterslip
on the order of 0.5 m after the Pisco earthquake (Perfettini et al.
2010, Fig. 9) . This is also the area of the 2005–2006 earthquake
swarm. Pritchard & Fielding (2008) solve for approximately 10 m
of maximum slip during the earthquake. Since the last earthquake
in the region occurred in 1746, this earthquake released approximately half of the ∼20 m slip deficit accumulated (assuming full
coupling) since then. This deficit may be made up in future earthquakes or the deficit may be (or have been) accommodated aseismically. These observations could document a transition in fault
properties associated with the subduction of the Nazca Ridge from
velocity weakening to velocity strengthening (e.g. Perfettini et al.
2010).
The M w = 8.8 Chile earthquake on 2010 February 27, ruptured 4◦ of the megathrust between ∼34◦ and ∼38◦ S. This earthquake was bounded to the north and south by the Arauco swarm
(Fig. 11) and the Topocalma swarms (Supporting Information Section S3.1.2 and Fig. S7). Fig. 12 shows the relationship between
slip distribution, aftershocks and prior earthquake swarms. The epicentre was at ∼36◦ S, so the rupture propagated bilaterally. The
rupture was terminated at both ends in regions that had experienced prior swarm activity, again suggesting a potential relationship between earthquake swarm processes and megathrust rupturing processes. The Topocalma swarms are associated with the
subduction of the Juan Fernandez Ridge, which is a border sediment input to the trench and styles of volcanism vonHuene et al.
(1997), suggesting that to the north, the 2010 rupture was controlled by downgoing plate properties. The southern terminus of
the 2010 rupture was the Arauco Peninsula, which Melnick et al.
(2009) suggest has been a consistent barrier to rupture propagation on the million year timescale. Downgoing plate bathymetry
is smooth across the Arauco Peninsula, but the Arauco Peninsula
is interpreted to be the northern boundary of a forearc microplate
bounded by the Lanalhue fault, which strikes through the peninsula.
Melnick et al. (2009) suggest that seismicity in the region, including the 1999 earthquake swarm, is all in the upper crust and not
on the megathrust. Thus, Melnick et al. (2009) suggest that at the
Arauco Peninsula the overriding plate is the dominant segmenting
force.
The swarms before the earthquakes in Chile and Peru could be
related to the coseismic rupture in two ways. First, the swarms
could indicate areas where aseismic slip has occurred such that
rupture is not likely to propagate through the swarm area. Alternatively, swarms could signify an area of the plate interface that has mechanical properties conducive to swarm generation and provides a barrier to rupture propagation (i.e. an area
of stable sliding, or heterogeneous frictional properties or fluid
distribution).
4.2 Earthquake swarm scaling relations
We explored the magnitude–frequency content in our catalogue of
swarms to test whether there is any indication of how frequent
swarms in South America may be and if South American swarms
are similar in their rate of occurrence to swarms in Japan or southern
California. Vidale et al. (2006) and Vidale & Shearer (2006) use
local catalogues to constrain types of earthquake bursts in Japan
and southern California, but the sizes of the events and duration
of the catalogues are different than for our swarm search. To compare South American swarms with the local catalogue of Japanese
swarms, we examine the frequency of swarms per unit of margin
length and time. We did not include southern California in this
analysis because the different tectonic environments do not allow
the frequency to be normalized by margin length. Also, we do not
normalize by convergence rate. Although faster convergence should
require more stress to be released, it is not clear that this should necessarily reflect earthquake swarm generation. Convergence velocities for South America (70–80 mm yr−1 ) and Japan (7583 mm yr−1 )
are similar within error (Syracuse & Abers 2006), so we would not
expect this normalization to change the results.
Fig. 13 shows that when normalized this way and grouped into
0.4 M w bins, both swarm catalogues are similar with respect to how
frequent swarms of a given magnitude should be. Extrapolation
suggests that in South America, an M w = 4 swarm should occur
about every year and we estimate there should be seven M w = 2.5
(near the observable completeness limit when dense local seismic
networks exist) swarms per year. Some swarms of this magnitude
have been reported in South America, for example at Cordón Caulle
(Smithsonian Institution 1994), at Nevado del Ruiz, (Banks et al.
1990) and in northern Chile (e.g. Salazar 2008). An M w = 8 swarm
should occur every 50 yr and an M w = 8.5 swarm every 90 yr, but
it is important to keep in mind that events this large may not be
physically possible.
When possible, we calculated apparent along-strike (parallel to
the trench) epicentral propagation velocities of the swarms with a
least-squares linear fit. Table 4 shows a brief comparison of epicentral propagation rates found in this study and in other areas
of the world. All studies show along-strike propagation of epicentres on the order of 5–10 km d−1 . As this propagation velocity
seems to be common for aseismic transient events (e.g. Lohman &
McGuire 2007; Shelly et al. 2007; Wech & Creager 2008; Boyarko
& Brudzinski 2010), it might indicate that some of the swarms we
observe are partially driven by slow slip.
We also used our catalogue to explore relationships between
different properties of swarms in this catalogue. No obvious patterns
emerged relating moment release, number of earthquakes, duration
and swarm area.
4.3 Aseismic slip and stress drops
Both swarms that we studied with InSAR (Ticsani and Copiapó)
show no conclusive large aseismic slip component. However, both
swarms studied here contained inset bursts of seismicity that accounted for a large amount of the total seismic moment released in
the swarm, but over a short period of time compared to the swarm
duration. Various authors (Toda et al. 2002; Ogata 2007; Llenos
et al. 2009) have argued that such bursts of seismicity should be removed from the swarm analysis because they may indicate a separate
process, such as a triggered main shock–aftershock sequence that is
not directly related to the aseismic slip. The largest earthquakes in
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Figure 12. Comparison of the 2010 February 27 M w = 8.8 Chile earthquake and prior earthquake swarm activity. Left-hand panel: Colour filled circles show
updated fault slip solutions published by the USGS at http://earthquake.usgs.gov/earthquakes/eqinthenews/2010/us2010tfan/finite_fault.php. Open circles show
aftershocks and filled black circles show prior earthquake swarm activity. Right-hand panel: Red line shows the USGS slip solution binned according to latitude.
Light red lines show alternate slip solutions, also available at the url above, by Anthony Sladen (Caltech) and Chen Ji (UCSB) and can be used as a measure
of uncertainty in the slip inversions. Blue line shows number of aftershocks by latitude. Black bars show number of earthquakes associated with earthquake
swarms in the area.
the 2006 Copiapó swarm show a clear aftershock sequence which
appears to follow Omori’s Law, but the rest of the swarm does not.
The inset burst at Ticsani Volcano (yellow earthquakes in Fig. 2) may
contain an aftershock sequence, but only 10 earthquakes were large
enough to be recorded which is too few to determine consistency
with Omori’s Law. The lack of an observable geodetic signal does
not rule out a broad underlying mechanism driving these swarms,
and deviation from Omori’s Law may suggest such a mechanism.
Interestingly, Holtkamp & Brudzinski (2011) show that earthquake
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magnitude-duration slow slip scaling law presented by Ide et al.
(2007).
In an effort to use another method to assess whether the Copiapó
and Ticsani swarms are different from other swarms with aseismic
slip, we have calculated the static stress drop, which for the simple
single uniformly slipping fault plane used here is fault displacement
(times rigidity, a constant) divided by fault area. Low stress drops are
common for slow earthquakes and may be ubiquitous for aseismic
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S. G. Holtkamp, M. E. Pritchard and R. B. Lohman
Figure 13. We plot the magnitude and frequency of earthquake swarms in
Japan Vidale & Shearer (from 2006) and South America (all swarms from
this study), normalized by the length of the subduction zone. Although the
duration and completeness threshold of the earthquake catalogues in Japan
and South America are different (see text), the swarms in the two areas follow
approximately the same exponential law. The two South American data
points circled are not included in the linear regression because the downturn
in most likely caused by these magnitudes being below the completeness
threshold.
Table 4. Comparison of along-strike epicentral propagation velocities.
Location
Propagation Velocity
Reference
Copiapo 1973
Copiapo 2006
Ecuador 2005
Punitaqui 1997
Salton Trough
West Moreland
N. Cascadia
C. Cascadia
Shikoku Japan
3.5 km d−1
7.5 km d−1
5-10 km d−1
2.5 km d−1
3-20 km d−1
3-10 km d−1
5-15 km d−1
5 km d−1
12 km d−1
(Holtkamp 2010)
This Study
This Study
(Holtkamp 2010)
(Lohman & McGuire 2007)
(Lohman & McGuire 2007)
(Wech & Creager 2008)
(Boyarko & Brudzinski 2010)
(Shelly et al. 2007)
or slow slip events (Ide et al. 2007). Our geodetic stress drop for
the Ticsani earthquake is 35 bars, similar to a ‘normal’ earthquake.
On the other hand, our geodetic inversions for the 2006 Copiapó
swarm result in a stress drop of 0.68 bars, which is over an order of
magnitude lower than the average of ∼10 for interplate contacts (e.g.
Lay & Wallace 1995), and may indicate anomalous behaviour. We
note that stress drop calculations for the distributed slip model for
Copiapó (Fig. S5, Supporting Information) were similar. However,
since much of the slip occurred off-shore, the InSAR observations
do not place a strong constraint on the spatial extent of rupture.
Allmann & Shearer (2009) found that the stress drops of the two
largest events in the 2006 Copiapó swarm were much higher at 19
and 14 bars, which is expected as the individual earthquakes still
follow seismic scaling laws.
4.4 Triggering of earthquake swarms and volcanic
earthquake swarms
We examined two of the three swarms associated with the 2001
M w = 8.5 southern Peru earthquake for the possibility of static
triggering via Coulomb stress changes. The swarms are not on the
megathrust and are 100 to 200 km from the ruptured segment so
they are not aftershocks. Figs S10 and S11 (Supporting Information) show triggered seismicity near Coropuna Volcano and Lake
Titicaca, respectively. Small Coulomb stresses (defined as a combination of shear and confining stresses) on the order of tenths of a
bar, can have enough impact to trigger seismicity (King et al. 1994).
Earthquakes have also been shown to be triggered dynamically, particularly at volcanoes and hot springs several hundreds of kilometres
away (Hill et al. 1993). More recently, seismicity has been shown
to be dynamically triggered by long-period surface waves several
thousands of kilometres away (Velasco et al. 2008).
Pritchard et al. (2007) calculate displacements associated with
the 2001 earthquake by combining InSAR and teleseismic data in
a joint inversion. We used their inversion results to compute static
changes in the Coulomb stress field due to the 2001 Peru earthquake
using the approach presented in Meade (2007) for stress and strain
due to triangular tensile and shear faults in an elastic half-space.
For the seismicity near Coropuna Volcano, we use the CMT
solution strike (329◦ ) and dip (88◦ ) to construct the target fault for
our stress change calculation. Only one CMT solution for seismicity
southwest of Lake Titicaca exists and it is not consistent with the
trend of earthquakes associated with the swarm and so was not
used. To be consistent with the trend in seismicity, we used a strike
of 50◦ . Fig. S12 (Supporting Information) shows Coulomb stresses
resolved onto a fault plane consistent with seismicity near Coropuna
Volcano. Both the Coulomb and confining stresses resolved onto
fault planes consistent with swarm seismicity show very little static
effect, on the order of 10−5 bars. Coulomb stress modelling for the
Titicaca swarm yields similar results (Holtkamp 2010). It therefore
does not appear as though these swarms were triggered by static
stress changes due to the M w = 8.5 earthquake.
The swarms in question did not occur coseismically but occurred
days to weeks after the event. The direct effects of dynamic triggering are linked to actual propagation of the seismic waves, and will
only last on the order of minutes to hours, so it seems necessary
that some static triggering is involved. Alternatively, a dynamic
triggering mechanism that takes some time to manifest could be
responsible (e.g. Linde et al. 1994; Gomberg et al. 1998).
Since we discovered few volcanic swarms, their significance is
not as well determined as the swarms associated with the megathrust. With the exception of the 2005 Ticsani Peru swarm, all volcanic swarms were associated with large megathrust earthquakes
(the 2001 M w = 8.5 earthquake) or volcanic eruptions. Volcanic
swarms detectable by our methodology in the north and central
parts of the South American margin do not seem to be associated
with eruptions, and volcanic eruptions in these areas do not produce swarms we could detect. In contrast, eruptions at southern
Chilean volcanoes are associated with sizable earthquake swarms,
such as during the Hudson and Chaitén eruptions. The Cerro
Hudson and Chaitén volcanoes both have infrequent eruptions, as
their last eruptions were ∼3600 (Naranjo & Stern 1998) and ∼8000
yr ago (Naranjo & Stern 2004). Volcanic activity and volcanic earthquake swarms in the southern volcanoes can be triggered by large
earthquakes as well, as the M w = 9.5 Chilean earthquake triggered
the eruption of Cordón Caulle, either via movement of the LiquiñeOfqui Fault Zone (LOFZ; Lara et al. 2004) or via bubble ascent
(e.g. Linde & Sacks 1998). The large total moment release of the
swarms associated with the eruptions in southern Chile may be
related to the nature of the volcanoes, which may occur in cooler
crust than their more frequently erupting northern counterparts, or
to the fact that a large fault system is near these volcanoes (e.g.
the LOFZ in southern Chile). Ticsani, the home of the only swarm
in the northern half of South America not associated with a large
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triggering earthquake, also exists within a large regional fault system (Lavallee et al. 2009).
5 C O N C LU S I O N
We performed a search for earthquake swarms in South America
and determined their basic characteristics. We identified 29 possible earthquake swarms within the PDE catalogue of varying spatial
scales and tectonic locations. We find that two earthquake swarms
(2007 Pisco, Peru and 2010 Maule, Chile) may have some interaction with large megathrust events in South America, based on
the observation that the termination of large megathrust ruptures
sometimes are controlled by where swarms have recently occurred.
These swarms on the megathrust may not have ruptured during the
large earthquakes either because they are areas where aseismic slip
is manifest or the fault properties change. We examine two swarms
with InSAR geodetic data (2005 Ticsani, Peru and 2006 Copiapo,
Chile) and conclude that no large amount of aseismic deformation
is necessary to explain the observed surface deformation, although
some aseismic slip may have occurred. Seismic swarms that appear
to have been triggered by the M w = 8.5 2001 Peru earthquake are
examined and we find that static changes in the Coulomb stress field
are too small to be likely triggers of the events, indicating that some
dynamic triggering process may have been responsible. We examined the frequency–magnitude content of our swarm catalogue and
found it to be in agreement with the regional swarm catalogue from
Japan despite large differences in catalogue duration suggesting that
we are observing the same process on a different scale. Although
few volcanic swarms were found, we propose a possible relationship
between swarm magnitudes and the frequency of eruption, and/or
the existence of large regional faults.
AC K N OW L E D G M E N T S
InSAR data was provided by the European Space Agency via Category 1 project 3194. S.G.H. was supported by a NASA Graduate
Research Fellowship and M.E.P. was partly supported by NSF grant
EAR 0510719. We thank Larry Brown, Mike Brudzinski and several
anonymous reviewers for helpful comments
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S U P P O RT I N G I N F O R M AT I O N
Additional Supporting Information may be found in the online version of this article:
Supplement. This supplement contains a discussion on swarms at
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