Earth and Planetary Science Letters 305 (2011) 215–225
Contents lists available at ScienceDirect
Earth and Planetary Science Letters
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e p s l
Earthquake swarms in circum-Pacific subduction zones
S.G. Holtkamp ⁎, M.R. Brudzinski
Miami University Geology Department, 114 Shideler Hall, Oxford OH, 45056, USA
a r t i c l e
i n f o
Article history:
Received 4 October 2010
Received in revised form 2 March 2011
Accepted 4 March 2011
Available online 30 March 2011
Editor: P. Shearer
Keywords:
Earthquake swarms
Subduction zones
a b s t r a c t
We systematically and manually search through clusters of earthquakes along circum-Pacific subduction
zones to identify potential earthquake swarms. In total, we find 266 potential earthquake swarms: 180 we
classify as megathrust and 68 we classify as volcanic due to their proximity to the megathrust or to volcanoes.
We focus on the megathrust swarms and demonstrate that: (1) the number of events in a swarm is not a
function of the largest earthquake in the swarm, (2) swarms exhibit an approximately constant rate of
seismicity that lasts until after the mean timing of events in the swarm, (3) the timing of the largest
earthquake in the sequence is no different than the timing of any other earthquake in the sequence, (4) our
catalogs of earthquakes comprising swarms (~ 9000 events) have high b-values (1.5 to 2), and (5) when
earthquake swarms are considered as single events using total duration and cumulative moment, they appear
to be consistent with the slow earthquake magnitude-duration scaling law presented by Ide et al. (2007). The
first three observations, along with the observation that swarms can span very large areas compared to their
cumulative seismic moment, argue against static stress triggering as a driving mechanism for earthquake
swarms. Along strike propagation velocities are observed for several swarms, showing epicentral propagation
of ~ 10 km/day, similar to other documented slow slip events. Together, this evidence implies that aseismic
slip along the megathrust is likely an important mechanism for the generation of megathrust earthquake
swarms in circum-Pacific subduction zones. We then conduct a comparison of swarms and large megathrust
earthquakes, finding evidence that the two are broadly anti-correlated: megathrust segments with large
earthquake swarm gaps are more likely to experience large (Mw N 8) megathrust events. We characterize the
ubiquity of megathrust swarms at different margins, and suggest that fault properties along Marianas-type
margins may allow for earthquake swarms to occur regularly, but other margins may rely on other variables,
such as the subduction of a ridge or seamount, to facilitate the generation of megathrust earthquake swarms.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
Relationships between earthquakes are observed by the clustering of earthquakes in space and time. This clustering commonly
occurs as mainshock–aftershock (MS–AS) sequences, which are
generally interpreted to contain the initial rupture of a fault (the
mainshock) and a decaying cascade of smaller ruptures on or very
near to the initial rupture plane (aftershocks) (Lay and Wallace,
1995). In fact, aftershock sequences are often used to define the
rupture plane of the associated mainshock (e.g., Sykes, 1971; Utsu
and Seki, 1954).
Clustering of earthquakes in space and time can also occur as
earthquake swarms, which are empirically defined as an increase in
seismicity rate above the background rate without a clear triggering
mainshock earthquake (Hill, 1977; Mogi, 1963; Sykes, 1970).
Earthquake swarms are often associated with volcanic regions and
are studied because of their relationship to eruptions or intrusions of
⁎ Corresponding author. Tel.: + 1 513 235 8915.
E-mail addresses: [email protected] (S.G. Holtkamp), [email protected]
(M.R. Brudzinski).
0012-821X/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.epsl.2011.03.004
magmatic material (Benoit and McNutt, 1996). Earthquake swarms
have been documented in areas not associated with active volcanism,
such as transform faults (Lohman and McGuire, 2007; Shibutani et al.,
2002) and hydrothermal systems (Fischer and Horalek, 2003; Heinike
et al., 2009). Triggering mechanisms for these non-volcanic swarms
range from associated aseismic slip on associated faults (Lohman and
McGuire, 2007) to movement of volatiles in hydrothermal systems
(Heinike et al., 2009).
Earthquake swarms at subduction margins not associated with
volcanism have been documented in New Zealand (Evison and
Rhoades, 1993), Japan (Fujinawa et al., 1983; Matsuzawa et al., 2004),
Kamchatka (Slavina et al., 2007; Zobin, 1996), Mexico (Zobin, 1996),
and South America (Holtkamp et al., 2011; Lemoine et al., 2001).
Studies of earthquake swarms at these convergent margins have been
motivated by their potential relation to large megathrust events,
although the mechanisms behind swarm nucleation and potential
interaction with large megathrust events remains debated (Evison
and Rhoades, 1993; Llenos et al., 2009).
Most swarms documented in literature were located with local or
regional scale seismic networks, often including offshore networks,
and utilize local earthquake catalogs with lower magnitude
216
S.G. Holtkamp, M.R. Brudzinski / Earth and Planetary Science Letters 305 (2011) 215–225
thresholds (e.g., Evison and Rhoades, 1993; Flueh et al., 1998; Vidale
and Shearer, 2006). While the heterogeneity of seismic networks
prevents a global study of this type, the goal of this paper is to initiate
a catalog of earthquake swarms along Circum-Pacific subduction
zones using the global scale Preliminary Determination of Epicenters
(PDE) data set. The core of this work is an expansion of the manual
earthquake swarm search conducted by Holtkamp et al. (2011) over
the South American continent.
2. Methods
We download and examine the complete PDE catalog from 1973 to
2010 over the following regions: South America, Mexico/Central
America, Alaska, Kurile-Kamchatka, Japan, Taiwan/Manila/Philippines,
Sumatra, Vanuatu, and Tonga/New Zealand. Since earthquake swarms
have been defined empirically in the past (e.g., Hill, 1977), we begin
with our definition of an earthquake swarm that agrees with
previously defined swarm properties (detailed below). We define an
earthquake swarm to be a noticeable increase in seismicity rate above
a visually established background seismicity rate without a clear
triggering mainshock. Swarms typically have many earthquakes near
the magnitude of the largest earthquake in the cluster so they do not
follow Baths Law, which states that the largest aftershock is typically
one moment magnitude smaller than the triggering mainshock. We
find that many earthquake swarms have abrupt onset and termination
of seismicity when compared to background seismicity (e.g., without a
decay in seismicity rate as in a decaying aftershock sequence). We use
this to help determine if a cluster is a swarm, but it is not a requirement, as it is likely that relatively abrupt termination is a necessary
outcome of the visual swarm determination. Fig. 1 outlines these
observations with a representative swarm example. In contrast, Fig. 2
shows a typical mainshock–aftershock (MSAS) sequence, in which the
mainshock is first in the sequence and is typically one moment magnitude larger than the second largest earthquake (Baths Law), and the
sequence typically fades into the background seismicity rate without
an abrupt termination.
We use these criteria to search through all major circum-Pacific
subduction zones for clusters of earthquakes that appear swarm-like.
For each region, we systematically examine each apparent cluster of
seismicity (apparent as a vertical line of dots in the bottom panel of
Figs. 1 and 2). Clusters that appear to have a triggering mainshock or
are dominated by a single event are discarded, while the remaining
clusters are marked as having swarm-like characteristics. Background seismicity rates in the PDE catalog are highly variable in two
ways: (1) reported seismicity rates from 1973 to 2010 vary by about
2 orders of magnitude, likely due to increased instrumentation, and
(2) background seismicity varies within each region studies,
sometimes drastically (e.g., central Chile, from 30 to 35°S, accounts
for half of the seismicity in the South and Central American PDE
catalog).
In regions with high background seismicity rate (e.g. Alaska,
central Chile), visual characterization of swarms becomes more
difficult. In these cases, larger earthquake magnitudes (~1 Mw larger)
or larger increases in seismicity rate (e.g., several tens of earthquakes
in a period of days to weeks) are necessary to distinguish the cluster,
but not both. For example, Holtkamp et al. (2011) find a swarm at the
Papudo seamount, South America, without an increase in earthquake
magnitudes because there were several tens of earthquakes in a few
days. In areas with low background seismicity rate (e.g. southern Chile
and Bonin-Marianas Trench) seismicity rate increases can be detected
even if only a few earthquakes are large enough to be recorded by
regional networks. In Puerto Aysen, southern Chile, for example, we
identified two earthquake swarms (1991 and 2007) despite finding
less than 15 regionally recorded earthquakes in the PDE catalog. In the
case of the 2007 swarm, a local seismic network recorded over 6000
earthquakes without a mainshock (Mora et al., 2008), supporting
the use of our approach in cases of limited earthquake numbers in the
PDE catalog. For a more detailed examination of the visual detection
methodology, see Supplementary Figs. S1 and S2.
In considering ways to pursue an automated swarm detection
approach instead, we found that previous studies successfully implementing an automated detection have often relied on a uniform
background seismicity level and magnitude threshold, which are
conditions that cannot be met in our global study. For example, the
method of Vidale and Shearer (2006) constructed an unbiased automated burst detection algorithm that exploited a uniform background
seismicity rate, but with limited spatial and temporal scale. Yet even
within that dataset, visual classification of swarms was still required.
Since we aim to produce a swarm catalog which is not limited in space
and time and is produced from a global catalog with widely varying
background seismicity rate and magnitude threshold (both vary by
several orders of magnitude), it does not allow us to assume a
constant background seismicity rate or magnitude threshold. As a
result, we rely on a visual swarm detection algorithm. While our
visual search is likely incomplete, we are encouraged that the swarm
characteristics we present in the next section closely resemble those
of Vidale and Shearer (2006).
Since magnitude plays a role in defining earthquake swarms, we
seek to establish a consistent magnitude measurement in our catalog
search. First, with regards to catalog completeness, we find that in
recent years completeness is ~ Mw = 4 along major convergent
margins. However, in the earlier decades of the catalog, completeness was ~ Mw = 5. Secondly, magnitudes given in the PDE catalog
are either locally constrained (ML) or regionally/globally constrained (waveform-constrained moment magnitudes for
Mw N ~ 4.5 in the past 20 yrs and body wave magnitudes for
∼ 4 b Mw b∼4.5). In this analysis, locally constrained magnitudes are
ignored as there is no clear conversion to moment magnitudes.
When only body wave magnitudes are given, a conversion to
moment magnitudes is performed by adding 0.31 to the body wave
magnitude (based on an empirical law given by Stein and Wysession,
(2003)). Prior to 20 yrs ago, only Mw N ~ 6 had waveform-constrained moment magnitudes reported and so earthquakes smaller
than this are converted from body wave magnitudes. Considering
that magnitude differences in MS–AS sequences are ~ 1 (Bath's Law),
these minor adjustments we make to try to establish a consistent
magnitude measurement are not likely to influence swarm
detection.
3. Characteristics of earthquake swarms
In total, we find 266 potential earthquake swarms (Fig. 3). We
next attempt to classify them according to the tectonic regime where
they occurred. There exists a bimodal distribution of swarms in
subduction zones: those near the seismogenic megathrust and those
near the volcanic arc (perhaps best seen in Supplementary Figs. S3
and S4). 180 swarms lie within the 0 and 50 km depth to slab
interface contours, and we classify these as megathrust earthquake
swarms. The PDE catalog does not have the epicentral or depth
resolution to determine whether these earthquakes represent actual
megathrust faulting, but these swarms show thrusting focal
mechanisms for every case where magnitudes were large enough
to have Centroid Moment Tensor (CMT) solutions (about one quarter
of swarms, 47 of the 182). In any case, the proximity of these swarms
to the plate interface indicates that the megathrust is playing a
prominent role in their formation.
We classify 68 swarms as volcanic, which we define as occurring
within ~ 50 km of an active volcano in the Smithsonians Global Volcanism Program (GVP) database. These swarms are typically shallow
(in the crust) and many are associated with volcanic eruptions or
documented volcanic activity. We list 18 swarms as other because
they don't fit the megathrust or volcanic swarm definitions. These
S.G. Holtkamp, M.R. Brudzinski / Earth and Planetary Science Letters 305 (2011) 215–225
217
Fig. 1. Example of an earthquake swarm. (Top Panel) Map view of the seismicity displayed in the middle panel and associated with the 1980 Vanuatu swarm. Red triangles are
Holocene volcanoes from the Global Volcanism Program. Dashed lines give depth to slab contours in 50 km increments. Colors of circles are relative time as defined by the color bar at
the top of the middle panel. Small map shows the regional context. (Middle Panel) Earthquake magnitude vs. time for ~ 3 weeks around the swarm. (Bottom Panel) Earthquake
magnitude versus time over the region defined in the top panel for 15 yrs surrounding the swarm with vertical bars representing the time shown in the top two panels.
include swarms that occurred in the outer rise (e.g. Izu-Bonin trench)
and backarc spreading centers (e.g. the Andaman Sea backarc spreading center). A list of all earthquake swarms shown in Fig. 3 are
included in Supplementary Tables S1 (megathrust), S2 (volcanic), and
S3 (other).
Next, we quantitatively classify and characterize our megathrust
swarm catalog by following the methodology of Vidale and Shearer
(2006). Fig. 4 shows the number of events in each swarm against the
largest earthquake in that swarm. For this figure, MSAS sequences
are simply a random sampling of mainshocks from each subduction
zone studied. This figure has two important characteristics: (1) there
is no tendency among swarms to have a larger number of events
with a bigger largest event, that is, the number of events is not
controlled by the largest event (which is expected for and seen in
MSAS sequences), and (2) swarms and MSAS sequences plot in two
distinct, separated regions in this plot, effectively separating MSAS
sequences from swarms and providing a quantitative measurement
of swarminess.
To further examine the swarminess of our megathrust swarm
catalog, we investigate the relative timing of events within the swarm.
We do this with the time normalization method of Vidale and Shearer
(2006), where the timing of each event in a swarm is normalized such
that the mean event timing is 1. The relative timing of events within
the swarms can then be averaged for all 180 swarms. Fig. 5 shows this
218
S.G. Holtkamp, M.R. Brudzinski / Earth and Planetary Science Letters 305 (2011) 215–225
Fig. 2. Example of a mainshock–aftershock (MSAS) sequence. Layout is similar to Fig. 1. (Top Panel) Map view of the seismicity displayed in the middle panel and associated with a
1996 Aleutian MSAS sequence. (Middle Panel) Earthquake magnitude vs. time for ~ 1 week around the sequence showing the typical Omoris Law trend where the rate of aftershocks
is proportional to the inverse of time since the mainshock. (Bottom Panel) Stars mark mainshocks of productive MSAS sequences.
result for the timing of all events and the timing of the largest event in
the sequence. We compare our normalized swarm to that of Vidale
and Shearer (2006) and find remarkably consistent results between
the two studies despite large differences in region and scale between
these studies 5. In both cases (global: circum-Pacific and local: Southern California), we find three consistent points. First, the initial peak
contains ~ 15% of the earthquakes. We agree with the interpretation in
Vidale and Shearer (2006) that this peak is likely composed of the
aftershocks of the initiating earthquake, which is sometimes one of
the larger in the sequence, or that the largest earthquake and its
aftershocks disproportionately occurs early in the sequence. Second,
there is an approximately constant rate of earthquakes at about 5% per
time period after the initial peak that lasts until after the mean time.
Third, the seismicity rate diminishes rapidly after ~1.4 normalized
time, perhaps in accordance with an Omori-type decay law. The
second point is particularly convincing evidence that our catalog is in
fact swarms.
Fig. 5b is identical to 5a, but for only the largest earthquake in
each sequence. The similarity between Fig. 5a and b is further
evidence of the swarminess because the largest earthquake is no
different than any other earthquake. Also, point (3) suggests that the
swarm-like behavior may on average stop after 1.4 normalized time
in the sequence. If the seismicity rate then decays with an Omoritype law, the end of the sequences would be best explained as
S.G. Holtkamp, M.R. Brudzinski / Earth and Planetary Science Letters 305 (2011) 215–225
219
Fig. 3. Map of earthquake swarm seismicity. Blue circles represent earthquakes associated with megathrust earthquake swarms, green circles represent volcanic earthquake swarms,
and black circles are other earthquake swarms as defined in the main text. Earthquake swarms were found in every subduction zone examined, but appear to be more common in
Mariana-type margins. Red triangles are Holocene volcanoes from the Global Volcanism Program, plate boundary model is from Bird (2003), and colored background is seafloor
bathymetry.
1000
Legend
Swarm-like
MS
in Between Swarm and MS-AS
rm
y”
100
S
-A
MS
-A
S
10
MS
“S
wa
rm
y”
“S
wa
Number of EQs > Mw=5
-A
S
Mainshock-Aftershock (MS-AS)
1
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
Largest EQ (Mw)
Fig. 4. Number of earthquakes in a sequence relative to the magnitude of largest event for swarms and mainshock–aftershock sequences (MSAS). Only earthquakes greater than
Mw = 5, which we consider to be a global catalog threshold, are included. The number of earthquakes in swarms, unlike MSAS, is not a function of the largest earthquake in the
sequence. We compare our results with those for a local catalog in Southern California (Vidale and Shearer, 2006). In both cases, “swarmy” sequences and MSAS sequences can be
effectively separated by a line.
220
S.G. Holtkamp, M.R. Brudzinski / Earth and Planetary Science Letters 305 (2011) 215–225
a
20
t
-1
Megathrust Swarm EQs:
Volcanic Swarm EQs:
Other Swarm EQs:
2006 PDE Catalog:
4
9a
15
Log(Count > Mag)
Percent of Earthquakes
Slope (b-value)
5
Normalized 180 Swarm Average
Timing of all events
10
-1.50 +- 0.03
-2.01 +- 0.07
-2.09 +- 0.06
-1.04 +- 0.02
3
2
1
5
t
-1/2
0
4
0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
6
8
Magnitude (Mw)
Normalized time
b
20
Timing of largest earthquake in each sequence
t
-1
Timing of largest event
in each sequence
9b
Percent of Earthquakes
Fig. 6. Magnitude-frequency relations for earthquakes within swarms. Each earthquake
that was part of a swarm is included in this panel, separated by their spatial categorization. B-value is calculated by least squares fit over the region which shows a linear
relationship.
15
10
5
t
0
0.0
0.5
1.0
-1/2
1.5
2.0
2.5
3.0
Normalized time
Fig. 5. Temporal distribution of earthquakes within all swarms by normalizing time to
the total duration of each swarm and then stacking all 180 swarms together. The
method of time normalization is taken from Vidale and Shearer (2006), to which we
refer the interested reader for a thorough description of the method. (a) Timing of all
earthquakes in each swarm. We plot two Omori-style rate functions, showing that they
do not fit for most of the normalized swarm. Common results for our analysis and that
for a local catalog in Southern California (Vidale and Shearer, 2006) are: (1) an initial
burst with ~ 15% of earthquakes, (2) roughly steady rate that lasts until normalized time
1.4, which is after the mean time of 1, and (3) after time 1.4, the seismicity rate drops
off, perhaps following the Omori-style curve (implying the end of seismicity is
comprised of aftershocks of the swarm events). (b) Relative timing of the largest event
in the sequence. The similarity between (a) and (b) suggests that the largest earthquake is not special, as it does not have a higher probability to occur at any particular
time as any other earthquake in the sequence.
aftershocks of the earthquakes in the sequence. This may lead to
slightly overestimated durations, which will be important in
Section 4.1.
Fig. 6 shows magnitude-frequency relations for the individual
earthquakes within the swarms. Globally, b-values (the log-linear
slope of the magnitude-frequency relation) are around 1. The entire
2006 ISC catalog has a b-value of around 1.04 (Fig. 6). Our catalogs of
earthquakes within swarms (megathrust, volcanic, and other, ~ 9000
events total) have high b-values (1.5 to 2), indicating that swarms
are deficient in larger magnitude events compared to other earthquakes. This is not unusual, as high b-values are often documented
within individual earthquake swarms, for example in volcanic
regions (Lay and Wallace, 1995). We do not calculate b-values for
the overall magnitude of swarms because there are too few to
produce a large enough magnitude range over which a magnitude
frequency relationship is linear.
While megathrust swarms exist in every subduction zone, they
appear to be more common in Vanuatu, Tonga, Kamchatka (Kurile),
and Alaska (Aleutian) and less common in Japan, Central America,
most of South America, and Sumatra (Table S1). However, the regions
we report to more commonly have swarms all have low background
seismicity levels, so there is likely some bias in this observation. In
South America, we find a strong correlation between the location of
megathrust swarms and the subduction of oceanic ridges or seamounts (Fig. 3) (Holtkamp et al., 2011). This correlation has also been
previously recognized in Japan (Fujinawa et al., 1983), but does not
appear to be the case in other areas (i.e., Vanuatu, Tonga, or Izu-BoninMariana; Supplementary Figs. S3–S5). Therefore, we suggest that
certain subduction zones, perhaps Marianas-type, are more prone
to experiencing earthquake swarms over broader portions of the
interface while other subduction zones require an external factor,
such as the subduction of an oceanic ridge, for earthquake swarms to
occur.
In some subduction zones, Vanuatu for example, earthquake
swarms can cover large areas. Fig. 7 shows a remarkable example of
this. This sequence in 1980 began with a Mw = 7.1 MSAS sequence
near the southernmost edge of the margin. Over the next two years, a
series of 5 earthquake swarms occurred to the northwest occupying
an along-strike distance of ~200 km of the megathrust. Despite filling
this 200 km wide region, the largest earthquake was Mw = 6.2. The
total seismic moment release for these swarms was ~ Mw = 7.0.
Several of the megathrust swarms (e.g. Figs. 1 and 8, and the 2006
Copiapo, Chile swarm (Holtkamp et al. (2011))) show apparent
along-strike migration of epicenters over time. Fig. 8 shows this along
strike propagation for the 2008 Tonga swarm and give epicentral
propagation velocities of 8.5 ± 1.9 km/day. However, the PDE catalog
only has enough spatial resolution to show propagation or expansion of epicenters for the largest and broadest swarms, so potential
migration of swarms at smaller scales cannot be determined from this
study.
4. Discussion
We document ~ 5500 individual earthquakes within the 180
megathrust swarms which have a total magnitude of Mw = 8.0. Of
S.G. Holtkamp, M.R. Brudzinski / Earth and Planetary Science Letters 305 (2011) 215–225
221
Fig. 7. An intriguing succession of MSAS and swarm sequences over a 2 yr period in Vanuatu. Layout is similar to Fig. 1. MSAS sequences marked by stars are labeled 1–3 and swarms
are labeled a–f. The largest earthquake in the section of arc south of − 21°S and west of 171°E is Mw = 6.2, remarkable considering that nearly the entire ~ 20,000 km2 area produced
seismicity.
~ 286,000 earthquakes in these regions, ~9000 (3%) are associated
with earthquake swarms, but the swarm earthquakes account for only
0.1% of the moment release. Despite the small percentage of earthquakes and moment release, we believe swarms on the megathrust
can give valuable insight into the physical properties of the megathrust. In particular, we find that aseismic slip may be an important
factor in the generation of earthquake swarms, and that earthquake
swarms may indicate variations in fault properties (i.e., coupling) and
the limits of large megathrust earthquakes.
4.1. Magnitude-duration relations of earthquake swarms
Since earthquake swarms generally have abrupt onset and
termination of seismicity with respect to the background seismicity
rate, we can use them to quantify the duration of each earthquake
swarm. We find that megathrust swarms last as short as less than a
day and as long as several months. Combining this duration with
the total cumulative seismic moment for each swarm, Fig. 9 shows
that the magnitude-duration relation of circum-Pacific earthquake
swarms matches fairly well with the proposed scaling law for slow
earthquakes by Ide et al. (2007). However, our selection criteria and
the magnitude completeness of the PDE catalog restrict the domain
in Fig. 9 which we are able to sample. While we are not able to quantify this restriction absolutely, we have highlighted the portion of
Fig. 9 sampled by our search and warn that, to some extent, the absolute position of the swarm points in this figure follows from our
methodology.
We note that there is not a clear linear trend in our magnitude vs
duration observations. There are potentially large and unquantifiable errors of duration and magnitude estimates. For duration, we
222
S.G. Holtkamp, M.R. Brudzinski / Earth and Planetary Science Letters 305 (2011) 215–225
Fig. 8. Example of swarm with a migration of epicenters. Layout is similar to Fig. 1. (Top Panel) Map view of the seismicity displayed in the middle panel and associated with the 2008
Tonga/New Zealand swarm. (Middle Panel) Earthquake magnitude vs. time for ~7 weeks around the swarm. (Bottom Panel) Along strike migration of epicenters.
have tried to address this by only showing megathrust swarms that
occur in areas of low background seismicity (event magnitudes
~ 1 Mw above background seismicity or rates several orders of
magnitude higher than background seismicity rates) in order to
reduce the impact of incorrectly estimating the duration of an event.
We are left with a little over half (94) of the originally detected
megathrust swarms that are then plotted in Fig. 9. For further
justification of the culling process for swarm durations, see
Supplementary Figs. S1 and S2.
For the estimate of magnitude, it is not clear how the aseismic
moment release, which is plotted in the Ide et al. (2007) figure,
should correspond with seismic moment release, which we plot on
top of the Ide et al. (2007) figure. The ratio of seismic to aseismic
moment release may vary by region, and thus perhaps be governed by
coupling, stressing rate, or some other factor. For example, linear
trends consistent with the slow slip scaling law can be shown for the
Izu-Bonin-Mariana (IBM) and Kurile-Kamchatka regions (Supplementary Fig. S6). If swarms in these regions are controlled by slow
slip, this would imply that the ratio of seismic to aseismic moment
release is greater than 1 for IBM and less than 1 for Kurile-Kamchatka.
There has also been recent discussion on whether individual slow slip
phenomena show the same linear scaling relation between moment
and duration as the overall trend (e.g., Japan slow earthquakes, shown
on the original Ide et al. (2007) plot, and Houston (2008) on low
frequency earthquakes).
So, while individual earthquakes within the swarms follow the
traditional scaling law for earthquakes, the correlation of swarms
with other slow earthquake processes implies that slow slip may
play a causative role in the occurrence of earthquake swarms. In fact,
correlation between earthquake swarms and aseismic slip has
already been observed by Lohman and McGuire (2007), Ozawa et
al. (2007), and Wolfe et al. (2007) in other settings. Aseismic slip has
S.G. Holtkamp, M.R. Brudzinski / Earth and Planetary Science Letters 305 (2011) 215–225
Mw
1
2
3
4
9
6
7
Magnitude
Threshold
8
Silent EQ
1 year
7
e
Megathrust earthquake swarms
1 month
ETS
SSE
6
1 day
5
d
4
ea
rth
qu
ak
es
c
1 hour
ow
3
Gap
Temporal
Proximity
Threshold
Sl
log [Characteristic duration (s)]
5
a
2
VLF
b
1
es
uak
rt hq
a
e
ular
Reg
0
LFE
LFE
-1
-2
10
11
12
13
14
15
16
17
18
19
20
21
log [Seismic moment (N m)]
Fig. 9. Magnitude–duration relationships for earthquake swarms and MSAS sequences
plotted on top of Fig. 2 from Ide et al. (2007). Gray shaded region indicates areas where
global seismicity catalogs cannot sample. Duration is time between the first and last
events in the swarm or sequence, and magnitude is the total seismic moment of
earthquakes that comprise the swarm or sequence.
been documented in every major subduction zone with sufficient
geodetic observation capabilities, either in the form of slow slip
events (e.g. Japan (Hirose et al., 1999), Cascadia (Rogers and
Dragert, 2003), New Zealand (Douglas et al., 2005), Alaska (Ohta
et al., 2006), and Central America (Kostoglodov et al., 2003)) or
aseismic afterslip (e.g. South America (Pritchard et al., 2007),
Kamchatka (Bürgmann et al., 2001), and Sumatra (Hsu et al.,
2006)). Aseismic slip events commonly show along strike propagation velocities of ~ 10 km/day (Boyarko and Brudzinski, 2010;
Lohman and McGuire, 2007; Shelly et al., 2007; Wech and Creager,
2008), so the observation that some swarms appear to be
propagating with this approximate velocity (Fig. 8) also suggests
that aseismic slip may be an important factor.
Based on the magnitude-duration scaling law proposed in Fig. 9, a
Mw = 8+ swarm could last on the order of years to decades (Meade
and Loveless, 2009), such that one would currently be indistinguishable from the background seismicity rate. This is noteworthy as it
cautions against over-interpreting that our catalog of megathrust
swarms saturates at magnitude ~ 7.0, which could imply there is a
limiting factor on source size for the swarms. We do not appear to
have enough time or catalog resolution yet to determine if the size
of megathrust swarms saturates. We see that volcanic swarms start
to saturate between Mw = 6 and Mw = 6.5. We expect the size of
volcanic swarms to saturate as there is simply not enough fault area
around a volcano to produce large slip areas.
4.2. Relationships between earthquake swarms and the megathrust
seismogenic zone
Holtkamp et al. (2011) noted that in South America, swarms that
concentrated along the Carnegie Ridge in Ecuador and the Nazca
Ridge in Peru occurred in areas of long standing seismic gaps along the
megathrust seismogenic zone. If these earthquake swarms are caused
by aseismic moment release, this would suggest that these areas are
223
not accumulating significant long term strain and may never rupture
as part of a large megathrust event. If the extensive and pervasive
swarms we find in Marianas-type subduction zones such as Vanuatu
and Tonga (Supplementary Figs. S4 and S5) are releasing significant
moment aseismically, this may help explain why earthquakes in these
regions do not exceed Mw ~7, as a significant area of the megathrust is
frequently releasing moment aseismically and preventing megathrust
rupture growth through these regions.
To test the hypothesis that swarms are affecting the megathrust
rupture cycle by presenting barriers to rupture propagation, either
by releasing strain aseismically or signifying an area of the plate
interface that is not accumulating significant strain, we have
attempted to quantify the pervasiveness of swarms and compare
this figure to key subduction parameters. To quantify the pervasiveness of swarms, we measure the largest along strike distance
(separation, or gap) that has not had an earthquake associated
with an earthquake swarm. We measure this for each 500 km
segment along strike, defined by Syracuse and Abers (2006). For
each along strike segment, we also characterize the largest megathrust earthquake in the past century (for earthquakes since 1964,
we filter the CMT for focal mechanisms with plunges greater than
45° and for earthquakes prior to that we perform a literature search).
This allows us to compare our measurement of swarm pervasiveness
(inversely swarm separation), largest characteristic earthquake, and
key subduction parameters listed in Syracuse and Abers (2006)
(Fig. 10).
If swarms represent barriers to megathrust earthquake propagation, we could expect that large gaps between earthquake swarms are
more likely to rupture with a single earthquake. In Fig. 10 (a), we see
that for regions with swarm separations greater than 600 km, 100% of
the regions had earthquakes Mw N 8. It appears that fault sections with
large swarm gaps are more likely to rupture with larger magnitude
events. This conclusion is reinforced by the observation that the
northern 10° of Tonga is nearly devoid of megathrust swarms and
contains three times as many Mw7 earthquakes as Kermadec to the
south, which is almost saturated with earthquake swarms (background seismicity does not vary much between these two regions, so
there is little chance for bias).
If swarms are a result of decreased coupling, we might expect a
trend with subducting plate dip angle (higher dip angles are often
associated with slab roll back and trench retreat which results in
little to no forearc subduction erosion, indicating that there is
decreased coupling on the interface). Fig. 10 (b) shows that higher
dip angles (dip angles N 50°) are associated with smaller swarm
separations, indicating that decreased coupling may aid in swarm
generation. Some potential correlations with plate age/thermal
parameter also exist for larger values of age/thermal parameter
and swarm separation: large age/thermal parameter is always
associated with small swarm separation and large swarm separation
is always associated with small age/thermal parameter. While we
find broad positive correlations between some parameters, we find
no evidence for linear relationships at this point. Nevertheless, each
piece of evidence suggests that swarms thrive on a weakly coupled
interface.
5. Conclusions
We present results from a search of the PDE catalog for
earthquake swarms along major circum-Pacific subduction zones,
finding 182 megathrust and 68 volcanic earthquake swarms. Many of
these are documented in literature and most are likely felt by local
populations, but our study presents the first comprehensive catalog
of these events in the shallow subduction environment.
Preliminary analysis of the individual swarms in the catalog
reveals 3 main discussion points. (1) For several swarms which are
large enough, we notice an apparent along strike migration of
224
S.G. Holtkamp, M.R. Brudzinski / Earth and Planetary Science Letters 305 (2011) 215–225
1400
1200
1000
800
600
400
200
1400
Swarm Separation (km)
Swarm Separation (km)
Swarm Separation (km)
1400
1200
1000
800
600
400
200
0
6
7
8
9
0
10
1000
800
600
400
200
10
20
30
Largest EQ in Region
40
50
60
70
80
0
90
0
20
Dip angle (degrees)
40
60
80
100 120 140
Incoming Plate Age (Ma)
1400
Swarm Separation (km)
1400
Swarm Separation (km)
1200
1200
1000
800
600
400
200
1200
1000
800
600
400
200
0
0
0
20
40
60
80
100
0
10
Plate Convergence Velocity (mm/yr)
20
30
40
50
60
70
80
Thermal Parameter
Fig. 10. Investigation of controls on swarm pervasiveness represented by swarm separation, which is the largest gap between swarms measured in each region. Key subduction
parameters are from (Syracuse and Abers, 2006) for 500 km along strike sections of circum-Pacific margins used in this study.
epicenters at a rate of ~ 10 km/day, a rate typically associated with
aseismic slip events such as ETS events. (2) We show that swarms are
more common in some subduction zones than others, perhaps more
pervasive in Marianas-type margins. (3) Swarms can cover large (e.g.,
N~10000 km2) areas without the occurrence of a large earthquake, as
was the case in Vanuatu, 1980–1982, in which a series of swarms filled
in 200 km along strike of the megathrust without an earthquake
greater than Mw = 6.2.
Further analysis shows that earthquakes associated with our
detected swarms have b-values between 1.5 and 2. We quantify the
swarminess of our catalog by showing that the number of events in
the swarm is not a function of the largest event, that there is an
approximately constant rate of seismicity which lasts until after the
mean event timing in the swarm, and that the timing of the largest
earthquake is identical to the timing of any other earthquake (the
largest earthquake is just another earthquake). These characteristics
of swarminess, along with the observation that swarms can cover a
much larger area than their cumulative moment release would
suggest, rule out a simple static stress triggering driving mechanism
for this catalog of earthquake swarms. Since earthquake swarms
generally have abrupt onset and termination, we use them to
quantify the swarm duration. We found that the moment-duration
relationship for swarms agrees remarkably well with the moment
proportional to duration relationship presented by Ide et al. (2007)
for slow earthquakes, as opposed to the moment proportional to the
cube of duration for traditional earthquakes. These pieces of
evidence lead us to suggest that aseismic slip is an important
mechanism for the generation of megathrust earthquake swarms in
circum-Pacific subduction zones. Additionally, specific fault properties along Marianas-type margins may allow for earthquake
swarms to occur regularly but other margins may rely on other
features, such as the subduction of a ridge or seamount, to occur. The
pervasiveness of earthquake swarms along margins such as TongaKermadec and Vanuatu may indicate the release of larger moment
aseismically, which would help explain the lack of great (Mw N 8)
megathrust earthquakes along their margins: pervasive strain
release along the margin prevents the growth of large contiguous
ruptures.
Supplementary materials related to this article can be found online
at doi:10.1016/j.epsl.2011.03.004.
Acknowledgments
This project was supported by the NSF-EAR/EarthScope CAREER
Award 847688 (MB) and a NASA NESSF Fellowship (SH). The research
builds on the initial swarm investigation in South America of SH, M.
Pritchard and R. Lohman. We thank J. Vidale for providing data and
comments.
References
Benoit, J.P., McNutt, S.R., 1996. Global volcanic earthquake swarm database and preliminary analysis of volcanic earthquake swarm duration. Ann. Geofisica 39, 221–229.
Bird, P., 2003. An updated digital model of plate boundaries. Geochem. Geophys.
Geosyst. 4, 1027.
Boyarko, D.C., Brudzinski, M.R., 2010. Spatial and temporal patterns of nonvolcanic
tremor along the southern Cascadia subduction zone. J. Geophys. Res. 115.
Bürgmann, R., Kogan, M.G., Levin, V.E., Scholz, C.H., King, R.W., Steblov, G.M., 2001.
Rapid aseismic moment release following the 5 December, 1997 Kronotsky,
Kamchatka, earthquake. Geophys. Res. Lett. 28, 1331–1334.
Douglas, A., Beavan, J., Wallace, L., Townend, J., 2005. Slow slip on the northern
Hikurangi subduction interface, New Zealand. Geophys. Res. Lett. 32.
Evison, F.F., Rhoades, D.A., 1993. The precursory earthquake swarm in New Zealand—
hypothesis tests. N Z J. Geol. Geophys. 36, 51–60.
S.G. Holtkamp, M.R. Brudzinski / Earth and Planetary Science Letters 305 (2011) 215–225
Fischer, T., Horalek, J., 2003. Space-time distribution of earthquake swarms in the
principal focal zone of the NW Bohemia/Vogtland seismoactive region: period
1985–2001. J. Geodyn. 35, 125–144.
Flueh, E., Vidal, N., Ranero, C.R., Hojka, A., von Huene, R., Bialas, J., Hinz, K., Cordoba, D.,
Danobeitia, J.J., Zelt, C., 1998. Seismic investigation of the continental margin offand onshore Valparaiso, Chile. Tectonophysics 288, 251–263.
Fujinawa, Y., Eguchi, T., Ukawa, M., Matsumoto, H., Yokota, T., Kishio, M., 1983. The 1981
earthquake swarm off the Kii peninsula observed by the ocean bottom
seismometer array. J. Phys. Earth 31, 407–428.
Heinike, J., Fischer, T., Gaupp, R., Gotze, J., Koch, U., Konietzky, H., Stanek, K., 2009.
Hydrothermal alteration as a trigger mechanism for earthquake swarms: the
Vogtland/NW Bohemia region as a case study. Geophys. J. Int. 178, 1–13.
Hill, D.P., 1977. Model for earthquake swarms. J. Geophys. Res. 82, 1347–1352.
Hirose, H., Hirahara, K., Kimata, F., Fukii, N., Miyazaki, S., 1999. A slow thrust slip event
following the two 1996 Hyugada earthquakes beneath the Bungo Channel,
Southwest Japan. Geophys. Res. Lett. 26, 3237–3240.
Holtkamp, S., Pritchard, M., Lohman, R., 2011. Earthquake swarms in South America.
Geophys. J. Int. In revision GJI–S–09–0667.R1.
Houston, H., 2008. Toward a spectral source model of the ETS process. Eos Trans. AGU
89 (53).
Hsu, Y.J., Simons, M., Avouac, J.P., Galetzka, J., Sieh, K., Chlieh, M., Natawidjaja, D.,
Prawirodirdjo, L., Bock, Y., 2006. Frictional afterslip following the 2005 NiasSimeulue earthquake, Sumatra. Science 312, 1921–1926.
Ide, S., Beroza, G.C., Shelly, D.R., Uchide, T., 2007. A scaling law for slow earthquakes.
Nature 447, 76–79.
Kostoglodov, V., Singh, S.K., Santiago, J.A., Franco, S.I., Larson, K.M., Lowry, A.R., Bilham,
R., 2003. A large silent earthquake in the Guerrero seismic gap, Mexico. Geophys.
Res. Lett. 30, 1807.
Lay, T., Wallace, T.C., 1995. Modern Global Seismology. Academic Press.
Lemoine, A., Madariaga, R., Campos, J., 2001. Evidence for earthquake interaction in central
Chile: the July 1997-September 1998 sequence. Geophys. Res. Lett. 28, 2743–2746.
Llenos, A.L., McGuire, J.J., Ogata, Y., 2009. Modeling seismic swarms triggered by
aseismic transients. Earth Planet. Sci. Lett. 281, 59–69.
Lohman, R.B., McGuire, J.J., 2007. Earthquake swarms driven by aseismic creep in the
Salton Trough, California. J. Geophys. Res. Solid Earth 112, B04405.
Matsuzawa, T., Uchida, N., Igarashi, T., Okada, T., Hasegawa, A., 2004. Repeating earthquakes and quasi-static slip on the plate boundary east off northern Honshu, Japan.
Earth Planet. Space 56, 803–811.
Meade, B.J., Loveless, J.P., 2009. Predicting the geodetic signature of mw 8 slow slip
events. Geophys. Res. Lett. 36.
Mogi, K., 1963. Some discussions on aftershocks, foreshocks, and earthquake swarms—
the fracture of a semi finite body caused by an inner stress origin and its relation to
the earthquake phenomena. Bull. Earthquake Res. Inst. 41, 615–658.
225
Mora, C., Comte, D., Russo, R., Gallego, A., Mocanu, V., 2008. The Liquiñe-Ofqui fault
system and its relationship to the Aysen (Southern Chile) 2007 seismic swarm. Eos
Trans. AGU 89.
Ohta, Y., Freymueller, J., Hreinsdottir, S., Suito, H., 2006. A large slow slip event and the
depth of the seismogenic zone in the south central Alaska subduction zone. Earth
Planet. Sci. Lett. 247, 108–116.
Ozawa, S., Suito, H., Tobita, M., 2007. Occurrence of quasi-periodic slow-slip off the east
coast of the Boso peninsula, Central Japan. Earth Planet. Space 59, 1241–1245.
Pritchard, M.E., Norabuena, E.O., Ji, C., Boroschek, R., Comte, D., Simons, M., Dixon, T.H.,
Rosen, P.A., 2007. Geodetic, teleseismic, and strong motion constraints on slip from
recent southern Peru subduction zone earthquakes. J. Geophys. Res. Solid Earth 112.
Rogers, G., Dragert, H., 2003. Episodic tremor and slip on the Cascadia subduction zone:
the chatter of silent slip. Science 300, 1942–1943.
Shelly, D.R., Beroza, G.C., Ide, S., 2007. Non-volcanic tremor and low-frequency
earthquake swarms. Nature 446, 305–307.
Shibutani, T., Nakao, S., Nishida, R., Takeuchi, F., Watanabe, K., Umeda, Y., 2002. Swarmlike seismic activity in 1989, 1990 and 1997 preceding the 2000 Western Tottori
earthquake. Earth Planet. Space 54, 831–845.
Slavina, L.B., Pivovarova, N.B., Levina, V.I., 2007. a study in the velocity structure of
December 5, 1997, M-w = 7.8 Kronotskii rupture zone, Kamchatka. J. Volcanol.
Seismology 1, 254–262.
Stein, S., Wysession, M., 2003. An Introduction to Seismology, Earthquakes, and Earth
Structure. Blackwell Publishing.
Sykes, L.R., 1970. Earthquake swarms and sea-floor spreading. J. Geophys. Res. 75, 6598.
Sykes, L.R., 1971. Aftershock zones of great earthquakes, siesmicity gaps, and
earthquake prediction for Alaska and Aleutians. J. Geophys. Res. 76, 8021.
Syracuse, E.M., Abers, G.A., 2006. Global compilation of variations in slab depth beneath
arc volcanoes and implications. Geochem. Geophys. Geosyst. 7.
Utsu, T., Seki, A., 1954. Relation between the area of aftershock region and the energy of
the main shock. J. Seism. Soc. Jpn 7, 233–240.
Vidale, J.E., Shearer, P.M., 2006. A survey of 71 earthquake bursts across southern
California: exploring the role of pore fluid pressure fluctuations and aseismic slip as
drivers. J. Geophys. Res. 111.
Wech, A.G., Creager, K.C., 2008. Automated detection and location of Cascadia tremor.
Geophys. Res. Lett. 35, L20302.
Wolfe, C.J., Brooks, B.A., Foster, J.H., Okubo, P.G., 2007. Microearthquake streaks and
seismicity triggered by slow earthquakes on the mobile south flank of Kilauea
Volcano, Hawaii. Geophys. Res. Lett. 34, L23306.
Zobin, V.M., 1996. Earthquake clustering in shallow subduction zones: Kamchatka and
Mexico. Phys. Earth Planet. Inter. 97, 205–218.