Geophys. J. Int. (2007) 171, 419–434
doi: 10.1111/j.1365-246X.2007.03483.x
Geometry and brittle deformation of the subducting Nazca Plate,
Central Chile and Argentina
Megan Anderson,∗ Patricia Alvarado,† George Zandt and Susan Beck
University of Arizona, Department of Geosciences, 1040 E, 4th St., Tucson, AZ 85721, USA
SUMMARY
We use data from the Chile Argentina Geophysical Experiment (CHARGE) broad-band seismic
deployment to refine past observations of the geometry and deformation within the subducting
slab in the South American subduction zone between 30◦ S and 36◦ S. This region contains a
zone of flat slab subduction where the subducting Nazca Plate flattens at a depth of ∼100 km and
extends ∼300 km eastward before continuing its descent into the mantle. We use a grid-search
multiple-event earthquake relocation technique to relocate 1098 events within the subducting
slab and generate contours of the Wadati-Benioff zone. These contours reflect slab geometries
from previous studies of intermediate-depth seismicity in this region with some small but
important deviations. Our hypocentres indicate that the shallowest portion of the flat slab is
associated with the inferred location of the subducting Juan Fernández Ridge at 31◦ S and that
the slab deepens both to the south and the north of this region. We have also determined first
motion focal mechanisms for ∼180 of the slab earthquakes. The subhorizontal T-axis solutions
for these events are almost entirely consistent with a slab pull interpretation, especially when
compared to our newly inferred slab geometry. Deviations of T-axes from the direction of slab
dip may be explained with a gap within the subducting slab below 150 km in the vicinity of
the transition from flat to normal subducting geometry around 33◦ S.
Key words: aseismic ridge, earthquake location, earthquake-source mechanism, seismotectonics, Nazca Plate, subduction.
I N T RO D U C T I O N
One of the fundamental observations of subduction zone structure
is the shape of the subducting slab. Its geometry is a key constraint
for many other calculations, including interpretations of seismic
structure, inference of subduction zone mechanics, and dynamic
modelling. In central Chile and Argentina, intermediate-depth seismicity in the Wadati-Benioff zone is the only current constraint on
slab geometry. Therefore, it is important to obtain well-resolved
hypocentres for Wadati-Benioff zone earthquakes. Recent advances
in the development of multiple-event earthquake location algorithms
(Waldhauser & Ellsworth 2000; Rodi et al. 2002a,b; Schaff et al.
2003, 2004) take advantage of the similarity of path-dependent corrections for clustered events in order to sidestep some of the velocity model-related issues that affect the uncertainty of subduction
zone hypocentre locations. Application of these new techniques in
other subduction zones have allowed researchers to accurately locate more, smaller events than with standard traveltime tomographic
∗ Now at: Colorado College, Geology Department, 14 E. Cache La
Poudre St., Colorado Spings, CO 80903, USA. E-mail: megan.anderson@
coloradocollege.edu
†Now at: Universidad Nacional de San Juan, Argentina
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Journal compilation techniques alone and have sharpened the resolution of structures
such as double seismic zones, asperities within the subduction interface (Okada et al. 2004), and the physical properties associated
with the subducting slab (Zhang et al. 2004).
This sort of investigation has not yet been applied in central Chile
and Argentina with a regional network; past studies have either covered a local area or were limited by heterogeneous regional station
coverage (Fig. 1). These limitations notwithstanding, these studies
have shown that the Wadati-Benioff zone of this region is quite variable; it has a different character in the north compared to the south in
our study area (Fig. 1; Smalley & Isacks 1987; Cahill & Isacks 1992;
Araujo & Suarez 1994; Pardo et al. 2002). Between 28◦ S and 33◦ S,
the zone dips at a 30◦ angle near the trench, flattens at a depth of approximately 100 km for several hundred kilometres eastward under
the South American Plate, then continues its descent into the mantle.
Between 33◦ S and 36◦ S, the Wadati-Benioff zone has a more or less
constant 30◦ dip, but with most seismicity located at rather shallow
depths (less than 200 km). The widely accepted slab geometry from
the Wadati-Benioff zone of Cahill & Isacks (1992) in this region did
not involve earthquake relocation, but culling of global data sets for
the solutions with the most data, therefore, their contours show only
these first-order features. Recently, new local data from the Chile
Argentina Seismological Measurement Experiment (CHARSME)
network have been analysed and fit with a plate geometry model
which shows deviations from that of Cahill & Isacks (1992) along
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GJI Tectonics and geodynamics
Accepted 2007 April 30. Received 2007 January 22; in original form 2006 February 28
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A
LCO
RINC
HEDI
HURT
Nazca
Plate
NEGR
LLAN
PACH
PICH
PANDA
ELBO
San Juan
JUAN
50
HUER
Tren
c
h
100
75
USPA
Santiago
Mendoza
PEL
AREN
CHARSME
FCH
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CHILE
RAFA
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CONS
South
American
Plate
km
ARGENTINA
LENA
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0
50 100
-30
100
AMER
BARD
NIEB
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175
0
5
JFR
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Cordoba
SP
15
RPM
20
0
LITI
12
-30
SJAV
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PENA
LOIC
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0
12
140
160
180
MAUL
B
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Figure 1. (A) Map of the study area. White diamonds are the CHARGE stations, GSN and GEOSCOPE permanent broad-band stations are black diamonds,
and black stars are the local University of Chile and Argentinean INPRES network stations that report to the International Seismological Centre. The black box
near San Juan is the approximate region of the PANDA network (Smalley et al. 1993) and the larger box to the south is the location of the CHARSME network
(Pardo et al. 2004). Black triangles are Quaternary volcanic centres. RPM is the relative plate motion direction of the Nazca Plate with reference to the South
American Plate (Kendrick et al. 2003). JFR indicates the location of the Juan Fernández ridge within the subducting plate. Slab contours are from Cahill &
Isacks (1992) and are labelled in kilometres. SP indicates the area containing the Sierras Pampeanas. Inset (B) shows slab contours of Pardo et al. (2004) with
the CHARGE station locations for reference. Inset (C) is the location of the study area with reference to the South American continent.
the slab dip transition zone and to the north near 30◦ S (Pardo et al.
2004). Reanalysis of global locations of (Engdahl et al. 1998) also
show a similar result (Berrocal & Fernandez 2005). The location of
many more earthquakes in these areas and in other local regions of
sparse seismicity are necessary to validate the accepted shape of the
subducted slab.
The subduction zone of central Chile and Argentina is a region
of interest not only for the unusual shape of the Wadati-Benioff
zone, but also for atypical deformation within the subducting slab.
The flattened region has been a cause of much speculation about
subduction dynamics and several hypotheses have been put forth as
to why the slab is flat. Among them is the idea that the subducting
aseismic Juan Fernández ridge (JFR) and its associated overthickened oceanic crust could provide enough buoyancy to catalyse slab
flattening (Pilger 1981; von Huene et al. 1997; Gutscher et al. 2000;
Gutscher 2002). Much of the seismicity within the flat part of the
subducting slab is clustered along the inferred extension of the JFR
into the subduction zone (Barazangi & Isacks 1976; Smalley &
Isacks 1987; Yañez et al. 2002), and this suggests that there may indeed be something unusual about the rheology of the ridge crust or
upper mantle. Further detailed study of intermediate-depth seismic-
ity, its location and depth, and the type of deformation associated
with these earthquakes may help us better understand the physical
properties of the JFR and will enable us to relate the ridge to the
process of flat subduction.
Towards this end, we present new hypocentre locations for
intermediate-depth seismicity using the Chile Argentina Geophysical Experiment (CHARGE) PASSCAL broad-band network data
for this region (Fig. 1) and an earthquake relocation algorithm used
for the first time in this subduction zone. We compare these determinations to past studies, especially the local PANDA network results
(Reta 1992; Reta et al. 1992; Smalley et al. 1993). Our new locations
cover a larger area than previous studies, thus allows for more thorough study of variations in depth of seismicity from north to south
across the flat slab region. The data confirm the gross interpreted
structure of the Wadati-Benioff zone of Cahill & Isacks (1992), yet
suggest that the shallowest portion of the slab is spatially correlated with the JFR. We also determine focal mechanism solutions
for these earthquakes from P and S wave first motion polarities. The
resulting patterns of strain define a new relation between extension
in the slab and the subducting slab geometry which is consistent
with a buoyant ridge hypothesis for slab-flattening.
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Journal compilation Geometry of the Nazca Plate, South America
421
Table 1. Mean and standard deviation of the assessed pick error for P- and S-wave picks in the CHARGE network catalogue.
Phase
Quality
Mean error (s)
Std. error (s)
N
Associated location error (km)
P
P
P
P
S
S
S
S
e
g
f
p
e
g
f
p
−0.016
−0.040
0.010
0.023
−0.032
0.077
0.036
0.009
0.148
0.171
0.367
0.496
0.138
0.289
0.350
0.646
185
116
95
85
59
74
79
76
1.2
1.3
2.9
4.0
0.6
1.3
1.6
2.9
Notes: Quality designations are as follows: e = excellent, g = good, f = fair, p = poor. Negative numbers for the mean indicate the alternate picks were on
average earlier than the original picks (Error = alternate arrival time – original arrival time). N indicates the number of picks used to estimate the error, and
standard error is equivalent to standard deviation. Location error is the distance travelled by a single phase over the duration of the standard error through a
mantle with P-wave velocity of 8.0 km s–1 and S-wave velocity of 4.55 km s–1 .
D AT A
The CHARGE PASSCAL broad-band seismic network was deployed for 18 months from 2000 to 2002 along two transects across
the Andes of central Chile and Argentina with several stations in
between (Fig. 1). We developed a catalogue of 18,897 P and S
wave first arrivals for 1098 intermediate-depth earthquakes for the
time duration of the CHARGE network, including data from GEOSCOPE station PEL and GSN station LCO (Fig. 1). We used twopass Butterworth filters with different corner frequencies for different event/station pairs to reduce noise before picking the arrivals.
Bandwidth for the filters ranged from a low corner of 0.4–0.8 Hz
to a high corner of 8–15 Hz, and depended on the size of the event,
station-dependent response, and attenuation. We picked P arrivals
on the vertical component and validated those picks using the radial
component where necessary (radial and tangential azimuths were
based on the International Seismologic Centre or ISC catalogue
locations), and S picks were made on the tangential component
to avoid contamination of the direct arrival with S to P converted
phases from either the slab or overriding crust.
Picks were graded as excellent, good, fair and poor on a qualitative basis and after initial relocation, a quantitative pick error was
assigned to each of these grades. The error assessment was made on
picks for a random subset of waveforms for ∼100 events which we
relocated with the single-event relocation code Hypocentre (Lienert
& Havskov 1995) and then rerotated the horizontal components to
azimuths defined by the new location. All of these waveforms were
filtered with a 0.8–8 Hz bandpass filter and we chose the alternate
pick farthest from the original pick that was still feasible as the first
arrival. The separation between the two picks is the pick error. We
assessed the average pick error and used the standard deviations of
the error distributions as the nominal standard error in the relocation
code (see Table 1 for error summary). This method quantifies pick
error due to phase misidentification, the use of different filters for
different arrivals, misrotation of radial and tangential components
due to event mislocation in the ISC catalogue, emergent phase onset
and anti-aliasing FIR filter effects (Scherbaum & Bouin 1997). The
mean of these errors are small for each grade, indicating that alternate picks are relatively evenly distributed around the originals and,
therefore, are appropriately modelled with an L2 norm. We deem
our qualitative pick designations appropriate because the quantitative assessment shows increasing standard error with decreasing
pick quality (Table 1). Based on our waveform observations, we
determined waveguide effects to have little systematic influence on
our pick uncertainty (Anderson 2005).
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Journal compilation M E T H O D A N D R E S U LT S
Earthquake hypocentres
We use the grid-search multiple-event (GMEL) location algorithm
(Rodi et al. 2002a,b), which is most similar to the multiple-event
algorithm hypocentral decomposition (HD; Jordan & Sverdrup
1981). It performs similarly to HD, double-difference (Waldhauser
& Ellsworth 2000), progressive multiple event location (PMEL;
Pavlis & Booker 1983), and other joint hypocentre determination
algorithms (JHD; Douglas 1967) when all codes were tested on the
same data set (Rodi et al. 2002a). It differs from other methods
in the way it determines solutions through an iterative single-event
grid search process, alternately determining station corrections and
earthquake locations, rather than through more direct inverse methods. This implementation allows one to fix more than one master
event. This method offers two distinct advantages for our particular
data set: it is a multiple-event method and it allows constraint of one
or more events of more well-determined location.
Single event location algorithms used on regional data sets produce less scattered locations than available in the ISC catalogue
(Fig. 2), but suffer from poorly modelled regional velocity variations, an effect that is decreased with the use of a multiple-event
location algorithm. In addition, it has recently been confirmed that
large azimuthal gaps within a network can lead to greater errors
in single-event epicentre location (Bondar et al. 2004). Walter
et al. (2003) showed that multiple-event location algorithms mitigate this effect, hence in a network such as CHARGE, which has
azimuthal gaps often greater than 180◦ , this is a significant advantage. Initial test locations calculated with a double-difference
method (Waldhauser & Ellsworth 2000) gave unstable results with
respect to hypocentre depth. One way to fix this problem is to include
a reference (or master) event (Pujol 2000). However, recent work
on relocations of a validation data set on the Nevada Test Site has
shown that locations determined with one set of data can change depending on the geographic location of the master event and variation
of the actual velocity model with respect to the 1-D model used for
locations (Walter et al. 2003; Anderson 2005). This effect would be
exacerbated for the CHARGE network because of the large velocity variations that are observed across a subduction zone. However,
Anderson (2005) also showed that locations improve when using
more than one master event, which GMEL allows us to do. Our
data set includes a subset of larger magnitude events used in the regional tomography (Wagner et al. 2005), thus we have many events
with independently determined locations using a heterogeneous and
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Figure 2. The preferred GMEL earthquake locations determined from this study compared to the ISC locations for these events (A and B), and compared to
the single-event locations determined from Hypocentre (C and D). GMEL locations are the grey stars and ISC/Hypocentre locations are black dots. Stations
used for the locations are the black diamonds. Red lines connect locations for the same event. Locations of the cross sections are indicated by the bars in
(A) and (C) and earthquakes and stations are projected from 100 km away from this line. Crustal thickness profiles are from Gilbert et al. (2006).
more realistic velocity model to help constrain locations of smaller
magnitude events.
We chose only events with eight or more station-phase pairs for
determining hypocentre locations to ensure well-constrained solu-
tions. We applied ellipticity and elevation corrections with the station information presented in Wagner et al. (2005). Velocity models
were determined using crustal structure defined by receiver functions (Gilbert et al. 2006) and moment tensor waveform modelling
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C
0
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D
200
Figure 2. (Continued.)
studies (Alvarado et al. 2005) using CHARGE data and above-slab
mantle velocity from local tomography (Wagner et al. 2005). Large
variations in velocity model across the network present obvious
problems with earthquake location using a 1-D model. Therefore,
we tested four different velocity structures and their effect on our
locations (Table 2). One velocity structure represents the average velocity values for the entire region (Average model), which are close
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Journal compilation to the standard IASP91 model (Kennett & Engdahl 1991). Two others represent average structure along the Andes from 36◦ S to 34◦ S
(Andes model) and average structure over the flat slab (Flat Slab
model); both of these models are overall slower than the average
model. The final model is that of Smalley et al. (1993), who apply
the velocity model of the Instituto Nacional de Prevención Sı́smica
(INPRES), which is faster than the average model. The models are
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Table 2. Velocity models used for relocation.
Model
Depth (km)
Vp (km s–1 )
Vs (km s–1 )
Vp/Vs
Density (kg m–3 )
Average
0–40
40–110
110–150
0–45
45–150
0–45
45–110
0–10
10–32
32–45
45–110
110–150
6.2
8.0
8.11
6.2
7.9
6.2
8.0
5.88
6.2
7.3
8.1
8.11
3.54
4.55
4.55
3.44
4.5
3.44
4.6
3.36
3.54
4.17
4.55
4.55
1.75
1.76
1.78
1.8
1.76
1.8
1.74
1.75
1.75
1.75
1.78
1.78
2.85
3.34
3.37
2.85
3.35
2.85
3.34
2.65
2.85
2.85
3.34
3.37
Andes
Flat
Smalley et al. (1993)
Notes: Velocities are based on crustal and mantle structure of Gilbert et al. (2006), Wagner et al. (2005), and Alvarado et al. (2005), determined with the
CHARGE data. IASP91 (Kennett & Engdahl 1991) velocity model was used below 150 km.
Figure 3. Weighted residual norm (WRN) plotted against number of station-phase pairs (N) recorded for each event. There is a slight correlation of higher
WRN for events with greater number of station-phase pairs. Box indicates events that were fixed in a relocation of all the events.
necessarily simple with few layers because they represent horizontal
averages.
We relocated events without constraining any hypocentres and
also using a couple of different subsets of constrained event
hypocentres. In one test, we constrained a subset of hypocentres
determined by tomography from Wagner et al. (2005). In a second
test, we chose a subset of events from our database with the most
constraining data and lowest residuals (constraining station-phase
pairs >20; weighted residual norm, WRN <0.5 after initial location). We separately relocated this subset of events using GMEL and
then constrained these events in a relocation of all events; these are
our preferred solutions (Fig. 2) which are catalogued in Anderson
(2005).
All results are stable with respect to starting depth and moderate
(1◦ ) deviations in starting epicentral location. The residuals have
weak dependence on N, the number of constraining station-phase
pairs (Fig. 3). This is more notable for hypocentres with lower residuals (<0.25 s). The small increase in weighted residual norm (WRN)
with N can be explained in two ways. It either indicates that we are
fitting less noise in the data set with a higher number of picks or that
pick error is smaller and, therefore, calculated residuals are smaller
with smaller magnitude events that have a greater percentage of total picks at closer stations. However, this effect is mitigated by the
culling process for constrained hypocentres, which selects events
with both high N and low WRN from the initial locations (box in
Fig. 3). Using these events as a constraint smoothes the stations
corrections and reduces the standard deviation of station residuals.
An initial examination of our solutions compared to solutions
given by the ISC and solutions determined by the single-event
location algorithm Hypocentre (Lienert & Havskov 1995) shows
less scatter in depth for hypocentres located with GMEL (Fig. 2).
Both GMEL and Hypocentre solutions reduce the scatter in depth
compared to the ISC solutions (Figs 2c and d); in addition, solutions are shifted on average 25 km to the east relative to ISC locations. This may reflect location error introduced into ISC locations
by having most reporting stations located in Chile and fewer in
Argentina (Fig. 1). This network geometry would have large azimuthal gaps, which could lead to error for earthquake locations
produced from single event location algorithms (Bondar et al. 2004).
The Hypocentre solutions, while they have less scatter than the ISC
locations, outline a subducting slab geometry at around 32◦ S and
between 68◦ W and 70◦ W that dips westward, or towards the trench
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Journal compilation P-wave Station Correction (s)
1
0.5
0
A
-0.5
-1
B
-1.5
-2
C
-2.5
-3
65
Moho Depth (km)
60
55
50
45
40
35
30
8.25
8.15
8.05
Vp (km/s)
(black dots in Fig. 2d). The GMEL solutions, while still retaining a
small apparent westward dip, lessen this effect considerably, a result
which is more in agreement with past results of studies in this region
(Pujol et al. 1991; Pujol 1992; Reta 1992; Smalley et al. 1993).
Error ellipses generated by standard analytical methods (Flinn
1965) indicate average epicentral error from ∼5 to 12 km and depth
error of ∼17 km, and for events greater than 80 km depth epicentral error from ∼4 to 11 km and depth error of ∼13 km. However,
we also give a more descriptive analysis of error rather than rely
on error ellipses because it has been shown that the lowest residuals may not reflect the most accurately determined solutions or the
most accurate velocity model (Pujol 1992, 2000; Anderson & Myers
2006). One method is to assess station corrections and whether they
match what we expect from other data available for the area. The
correspondence of station corrections to independently determined
crustal and mantle thickness/velocity changes is striking and gives
additional support for our locations (Fig. 4). Negative station corrections imply a faster travel path than the 1-D model, while a positive
one indicates a slower path. As expected, station corrections vary
proportionally to Moho depth (Fig. 4, profile B). Where uppermost
mantle velocities have significant variation, station corrections also
vary inversely with mantle velocities (Fig. 4, eastern part of the
profile C, and profile A). We have few crustal events in this data
set to compare to the intermediate depth events to determine the
influence of crustal velocity variation on the station corrections,
which has been shown to affect location depths in this region for
the PANDA network (Pujol et al. 1991; Pujol 1992; Reta 1992).
Incomplete modelling of the crustal component could account for
the slight difference between our solutions and those of Reta (1992)
for earthquake depths directly under the PANDA network. However, station corrections for JUAN and USPA, the two CHARGE
stations within the PANDA footprint, compare favourably with stations corrections for PANDA network stations that were close by.
Absolute magnitudes of station corrections are different for the two
networks, but the change in station correction between JUAN and
USPA is 0.65 s whereas the change in station correction across
the same area for PANDA stations is 0.57 s (stations O08 and O03;
Pujol et al. 1991), which indicates we are likely incorporating crustal
velocity variations as well. The apparent westward dip still retained
within our locations likely reflects true N–S variations in earthquake
depth resolved by our study that appear as a westward dip when all
earthquakes are plotted in one E–W transect.
Results were very similar when we constrained hypocentres by
the locations produced for events used in the tomography (269
events) rather than well-determined solutions from our location
study (Fig. 5), which gives additional credence to our hypocentre
depths. This may be in part due to the fact that many well-determined
events in our study were also used in the tomography. In addition,
solutions not constrained in our study, but constrained in the tomography relocated to the same place (as an example, events in the
small box in Fig. 5a, were constrained with the tomography locations but not with our preferred locations). The average difference
in hypocentre solutions for these two iterations is 4 km in epicentral location and 6 km in depth with a standard deviation of 5 and
9 km, respectively. The average difference in location and depth
for events greater than 80 km depth (which removes more unstable
forearc event hypocentres) is slightly less, 3 and 5 km, respectively,
with standard deviations of 3 and 5 km. The difference between
the events relocated with the tomography and our study may be a
good indication of true error due to pick quality and true velocity model heterogeneity not adequately compensated by the station
corrections.
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300
400
500
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Distance (km)
Figure 4. Station corrections and corresponding Moho depth and uppermost
mantle velocity for selected stations within the CHARGE network. Profile A
(green) is a N–S transect consisting of stations RINC, JUAN, USPA, AREN,
LENA, and BARD (Fig. 1) and is plotted in kilometers south from RINC.
Profile B (blue) is an E–W transect across the line of stations near −30◦ S
and profile C (red) is a similar transect near −36◦ S. Both profiles B and C
are plotted in kilometres east from the trench. Station RINC (highlighted
with a black square) is common to profiles A and B and station BARD
(black triangle) is common to profiles A and C. Uppermost mantle velocities
correspond to tomographic velocities at 65 km depth (Wagner et al. 2005).
Moho depths are from Gilbert et al. (2006).
Different 1-D velocity models tend to produce a shift in the
hypocentres relative to the centre of the network. The Andes and
Flat Slab models tended to shift epicentres slightly towards the centre of the network and bring solutions shallower. The opposite effect
is seen with the slightly faster Smalley et al. (1993) model; events
tend to shift towards the perimeter of the network and are deeper.
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Figure 6. GMEL locations (grey stars) from an iteration in which all initial
event locations were at 32◦ S and 69◦ W and 70 km depth. Dots are locations
for an iteration of GMEL in which all events were started at the nearest
1-degree grid node to the ISC locations. Orange triangles are constrained
events. All other symbols as in Fig. 2.
100
150
B
200
Figure 5. Preferred GMEL earthquake locations compared to locations constrained using event locations determined with the Wagner et al. (2005) tomography for this region. Grey stars are GMEL constrained locations and
black dots are the tomography-constrained locations. The black box near the
bottom of panel A encloses two events that were located with the tomography but are not among the solutions constraining the best GMEL calculation.
Other symbols as in Fig. 2.
This was true for events both within and outside of the network
illustrating the dominant effect of influence within the network on
station corrections. We find epicentre and depth differences between
the Average and Andes models of 6 and 5 km with standard deviations of 4 and 7 km, respectively, and for events below 80 km the
epicentre difference is 5 km and depth difference is 3 km with standard deviations of 3 and 5 km, respectively. The differences between
the Average and Smalley et al. (1993) model are within a kilometre
of these numbers. We estimated location error due to picks of a certain quality based on mantle velocities (assuming pick error maps
into near-source variations in location). These errors are generally
much smaller (2 km on average) than the differences in location
due to different constrained event locations or changes in velocity
model (Table 1).
Some regions in our network had solutions that were overall more
poorly determined. The uncertainty is quite large for event hypocentres north of 30◦ S and south of 36◦ S in this study and earthquakes
near the edges of our network are more likely to be mislocated than
events at the centre of our network. This is well demonstrated by the
results generated with the starting locations collapsed to the centre of the network (Fig. 6). This calculation had trouble converging
to a solution; after ∼300 iterations, events at the very edge of the
network still had not migrated to the locations determined with ISC
starting locations. Although this effect was mitigated within the network by fixing locations, this points to the very real problem of the
instability of the locations of events outside a network. Solutions
shallower than 80 km in the forearc region of Chile are likely more
well determined by the ISC, which includes many more stations in
Chile than our network (Fig. 1). We believe our most reliable solutions are between 30◦ S and 35◦ S and between 67◦ W and 71◦ W.
Our solutions are more accurate than ISC locations in this region
because the ISC network coverage of this region is not as complete
as the CHARGE network coverage. A few very deep events appear
within the slab below the main clustering of events even within this
zone; these events tend to be unstable in their location and for this
reason, we did not interpret them.
Focal mechanism solutions
Although most of the events are too small (MD ∼3.0) for the waveforms to have sufficient low-frequency energy for reliable waveform modelling (with our sampling rate of 40 samples per second),
they have clear P- and S-wave arrivals recorded by the CHARGE
network, which is in an optimal geometry for determining the focal mechanisms from first arrivals. The angle of the rays incident
on the focal sphere was calculated using the ray tracing program
TauP (Crotwell et al. 1999) using the same velocity model used to
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Figure 7. An example of a well-determined focal mechanism solution (lower hemisphere projections) determined from first motion data. The small map shows
the location of this event with respect to the CHARGE stations. P-waveform data used to determine this solution are shown and the first motion is marked with
a vertical line in each vertical component trace (each trace is ∼3–5 s long). Red dots indicate a compressional (up) arrival and blue dots indicate a tensional
(down) arrival. Stations that also had S wave first motions are indicated with a ‘+’ sign, although waveforms are not shown.
determine the preferred hypocentre locations (Average; Table 2) and
then potential solutions were calculated using the grid-search program Focmec (Snoke 2002). Many of the earthquakes gave multiple
potential solutions that fit the polarity data. We determined the reliability of the solutions in several ways. Determinations were made
with a grid search in strike, dip, and rake using 5◦ increments. We
increased the search increment if too many solutions were possible until fewer than 25 solutions were determined. Therefore, the
smaller the grid size, the fewer solutions fit the data, and the more
similar the potential solutions are. In addition, we statistically characterized the clustering of P- and T-axes for potential solutions obtained in the 5◦ increment search using a Fisher distribution (Fisher
1953). High values (greater than 20) of κ, the precision parameter,
indicate potential solutions that are similar. For some earthquakes,
one of the axes was well constrained while the other was not. We
catalogued the average direction of the well-clustered axes as well
as axes of more well-constrained solutions (Anderson 2005). Interpreted solutions were limited to those with a grid search size less
than 10◦ , κ values generally greater than 30 (which removed earthquakes having outlier solutions, which should be considered fully
possible solutions in this case), and a 95% confidence level (α 95 )
for P- and T-axes less than 10◦ . In this way, we have limited useful
data to include only those earthquakes which have a suite of potential solutions that are contained in one distinct family that are
similar enough to each other to have the same interpretation within
the larger tectonic context. We then chose a representative solution
that best fit the pattern of impulsive and emergent arrivals. Overall,
the P-axes had a κ value averaging 128 with α 95 on average 8◦ . The
T-axes had similar average confidence levels with average κ values
higher, at 263.
Some earthquakes were of large enough magnitude to attempt
a separate solution with regional waveform moment tensor inversion, but only one had a Harvard CMT (HCMT) solution. We compared results from first motions with the moment tensor solutions for
two events. We used the seismic moment tensor inversion method
by (Randall et al. 1995) which involves modelling regional broadband waveforms for the three seismic displacement components.
We used the epicentral GMEL locations, a fixed focal depth and
one of the models in Table 2 to generate synthetic seismograms to
fit the CHARGE waveforms. As a result, we obtained the seismic
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and observed data. We also tested the focal depth by running an
inversion for each of a set of focal depths. We compared the solutions and obtained the best depth for the minimum amplitude-misfit
error. In addition, we tested the seismic moment tensor inversion
solutions for different velocity structure models in Table 2. Thus
moment tensor solutions provide an independent constraint for the
focal mechanism depth for these earthquakes and a check of the
velocity model used in our determinations.
Fig. 7 shows an example of a focal mechanism determination
from first motions for an event with magnitude m b 4.2. There is
good agreement between the nodal plane positions and emergent
and impulsive P-wave arrivals. One earthquake (event 1, Figs 8a–e)
was large enough, M w 5.4, to have an HCMT solution (Fig. 8c).
For this event, we also modelled regional CHARGE waveforms
using our Average model to determine the seismic moment tensor
(Figs 8d and e). Results from the regional waveform modelling and
the HCMT solution are similar to the potential solutions from first
motions (Fig. 8). We note that this particular earthquake had enough
variation between potential first motion solutions that we did not
pick a representative solution, but both the HCMT and our regional
waveform inversion solutions are among the potential first motion
solutions (Fig. 8a). We also show the solutions obtained for this
event using the location and depth determined by the single-event
relocation method, which was deeper than the final solution from our
best GMEL result (Fig. 8b). Consequently, the ray paths’ intercepts
with the focal sphere and the potential solutions are slightly different,
such that a solution with a slight oblique component predicted by
waveform modelling does not appear. While these results are a very
encouraging test of our first motion solutions, it also suggests that
with the depth uncertainties in our data set, slight variations in our
focal mechanism solutions are likely.
In addition, we tested the sensitivity of focal mechanisms to velocity structure models in the regional waveform inversion. Fig. 8(e)
shows the results using models Average, Smalley et al. (1993) and
Flat Slab in Table 2 for this event. Note that focal mechanism solutions remain stable around the best depth for the depth range
explored. However, the best fits are obtained using the Flat Slab
model (Table 2). To compare focal mechanism results and test velocity models in a different area, we completed regional moment
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Figure 8. First motion focal mechanism solutions for two events compared to moment tensor inversion solutions. (A) Potential solutions with first motion data
and GMEL location for event 1. (B) Potential solutions with first motion data and Hypocentre location for event 1. (C) Harvard CMT solution for event 1.
(D) Regional waveform inversion for event 1 using CHARGE data. Vertical (z), radial (r), and tangential (t) components are shown for synthetic and observed
data filtered between 20 and 30 s for the best depth (118 km) using Flat Slab velocity model (Table 2). (E) Amplitude misfit versus focal depth results from
the regional waveform inversion for event 1. Depths (in km) of solutions are labelled. (F) Solution with first motion data and GMEL location for event 2. (G)
Regional waveform inversion for event 2 using CHARGE data and the Andes velocity model (Table 2). (H) Same as (E) for event 2.
tensor inversion for a M w 5.1 event under the high Andes (event 2,
Figs 8f–h). The first motion solution is very similar to the bestfitting waveform modelling solution and in this case, the depths are
also similar using the Andes or Average model (114 km for the first
motion solution).
G E O D Y N A M I C I N T E R P R E T AT I O N
One of the aspects of this subduction zone that we may address with
more confidence than previous studies is the shape of the subducting
slab. We present new contours to describe the shape of the subducting slab (Fig. 9), which we infer from the top of the Wadati-Benioff
zone seismicity. We gridded the shallowest value within each grid
cell with a nearest neighbour algorithm then smoothed the result and
generated the contours from this grid. These contours have elements
of both previous generations of slab contours shown in Fig. 1 and
inset of Fig. 9. In the southern part of our study area and near the
slab-dip transition zone at 32◦ S, the contours are quite similar to
the Cahill & Isacks (1992) contours (inset CI in Fig. 9). However, to
the north the new contours indicate a deeper slab closer to the trench,
and thus shows that the cluster of seismicity near the inferred location of the JFR is the shallowest in the flat slab region (see profile at
31◦ W). This is more similar to the contours of Pardo et al. (2004) (inset P in Fig. 9) than Cahill & Isacks (1992). A necessary question to
answer is if the depths of the earthquakes in the region near stations
NEGR, HEDI and PACH (Figs 1 and 9) are stable and reliable, for
they define the shape of the slab in this region. Since they are near the
edge of the network, this is a valid concern. Event depth is more dependent on vertical ray paths to stations directly above them than on
more horizontal ray paths to stations farther away, therefore, station
corrections for NEGR, PACH and HEDI likely exert much control
on the depths of these events. Reta (1992), Pujol et al. (1991), and
Smalley et al. (1993) relocated earthquakes in this region and concluded that the events closer to the trench were influenced by poor
determination of station corrections using single-event location algorithms and a seismic network near San Juan (Fig. 1). Their relocations using JHD defined a completely flattened slab geometry. The
results of this study indicate that locations fully outside a network
can be quite unstable, even when using a multiple-event relocation
code. Therefore, location results for events just outside networks
such as PANDA (which was confined to an area east of 69◦ W (Fig. 1),
well southeast of the many of the deeper earthquakes) or the local
networks with no stations in this region may not accurately represent
the depths of these events. For this reason, we believe that our results
in the region just north and west of the main cluster of events near
the JFR may be more reliable than results from the previous, smaller
networks, but acknowledge that the exact shape of the slab in that
region is on the edge of our resolution. Thickness of the WadatiBenioff zone for the cluster of events near the JFR is likely better
determined by the Reta (1992) study, ∼10 km as opposed to our
∼20 km, because they had so many stations immediately above this
cluster.
First motion focal mechanism solutions are plotted in Fig. 10
and illustrate the predominance of normal mechanisms at intermediate depths (70–200 km) in this zone. There are a few thrust focal
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Figure 9. Contours and profiles of the Wadati-Benioff zone from this study, labelled in km. Each cross-section is either an east–west or north–south transect
at the labelled latitude or longitude. Earthquakes as well as stations are projected 50 km away from each line for the east–west transects and 25 km away for the
north–south transects. The vertical axis of each cross-section is depth in km and the horizontal is longitude in degrees. The few events that appear to be located
much deeper in the subducting slab than the main cluster of events are generally events with more unstable locations. The insets to the map are the Cahill &
Isacks (1992) (CI) and Pardo et al. (2004) (P) contours for reference. Constrained events are shown with orange triangles, other symbols as in Fig. 2.
mechanism solutions, especially closer to the trench and north of
31◦ S. Overall, many solutions have a tensional axis that is subperpendicular to the trench, indicating that the likely mechanism
for deformation is slab pull, similar to conclusions from previous
studies of this subduction zone (Cahill & Isacks 1992; Reta 1992;
Araujo & Suarez 1994; Slancova et al. 2000; Pardo et al. 2002;
Brudzinski & Chen 2005). However, pairing the focal mechanisms
with the slab contours from our study reveals a consistent pattern
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trench (Fig. 11a). Between 33◦ S and 36◦ S, most subhorizontal Taxes are trench-normal with a few exceptions, however, around 32◦ S,
the predominant T-axis direction is no longer trench normal, but
perpendicular to the local strike of the slab. This observation is
still quite consistent with the slab pull mechanism because of the
changing slab geometry. For the most part, the regions in which the
T-axis direction does not follow the dip direction of the Cahill &
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Figure 10. (A) Interpreted focal mechanism solutions for the CHARGE region plotted in lower hemisphere projection. Slab contours are labelled in km. Dots
indicate earthquakes for which P- and/or T-axes were determined. Black diamonds are the stations used in this study. Box indicates the area shown in part B.
Contour depths and other symbols as in Fig. 1. (B) Focal mechanism solutions in the large cluster of events in the centre of the network.
Isacks (1992) contours, they do follow the dip direction of our slab
contours.
With the exception of one or two T-axis orientations here and
there, the only major break from this trend with our slab contours is
in the region just east of the Sierra Pie de Palo (see box in Fig. 11a),
where the T-axes are subparallel to the slab contours. Pardo et al.
(2002) compiled all HCMT solutions for events in this region and
found the same trend in this part of the slab. One potential interpretation of this pattern is that strain is being partitioned on pre-existing
faults like those characterized in the outer-rise region (e.g. Fromm
et al. 2006) and are thus rotated from the local slab dip (e.g. Reta
1992). Another possibility is that these events may be influenced by a
tear or gap in the subducting slab. The dotted circle in Fig. 11 encompass a region of the subducting slab that has not been characterized
by well-located seismicity in any study. Gaps in the intermediatedepth seismicity exist in this location in the hypocentres presented
by Cahill & Isacks (1992), Pardo et al. (2002), and this study. If a
tear or gap exists in the subducting slab in this region, it would likely
influence the deformation of the slab nearby such that T-axes to the
north would be oriented in the direction of slab pull determined
by the dip of the slab to the north rather than to the south. This is
consistent with our observations, therefore, we hypothesize that if a
gap exists in the subducting plate it is likely located just to the south
and east of the events with slab-contour-parallel T-axis orientations.
Recent magnetotelluric investigation of this region shows high conductivity, perhaps due to a tear or gap in the slab and thus mantle
flow in this region which also supports this interpretation (Booker
et al. 2005b).
Our observations have implications for the ridge buoyancy hypothesis and the cause of increased seismicity along the subducted
ridge. Ridge buoyancy predicts that the shallowest part of the flatslab region should be at the ridge itself, which is consistent with
our slab contours. Another possible interpretation is that the region
near the JFR has earthquakes that are simply occurring at a shallower
level within the subducting slab, for instance within the crust rather
than within deeper levels of the slab. However, our observations of
dispersed body waves (as described by Abers 2005) for earthquakes
throughout the flat slab region for the CHARGE data set indicate
that many earthquakes are occurring close to or within a low velocity waveguide. This low velocity zone may be the subducted oceanic
crust, however a complete study of these phases in conjunction with
the earthquake locations is necessary to draw definitive conclusions
from these observations. This may be an important point to pursue
because the thickness of the Wadati-Benioff zone is approximately
10 km, which may have important geodynamic consequences if it
can be formally linked with crustal thickness. If our contours indicate the true geometry of the subducting slab, our interpretation of
subhorizontal T-axes suggests that the slab pulls away from the JFR
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Figure 11. (A) Subhorizontal T-axes determined from first-motion data. Dots indicate earthquakes for which P- and/or T-axes were determined. Red arrows
mark the dominant orientation of the T-axes in different parts of the study area. The box marks the location of T-axes that parallel slab contours. The dashed
circle indicates a region where intermediate-depth seismicity is absent in all studies of this region. Contour depths are labelled in km. Other symbols as in
Fig. 10. (B) Subhorizontal P-axes determined from first-motion data.
cluster of seismicity both to the south and north. This would also
be expected if the ridge were buoyant: the opposing forces of the
buoyant ridge and the slab sinking around it would put the entire
slab into extension perpendicular to the slab contours. This interpretation would explain scatter of focal mechanism orientations in
the flat slab segments analysed by Brudzinski & Chen (2005).
The more frequent occurrence of earthquakes in the JFR region
suggests that there could be a rheologic change between this region and the rest of the slab such as greater release of water from
the oceanic crust of the subducted JFR or a petrologic change as
suggested by Brudzinski & Chen (2005). Interpreting the cause of
intermediate-depth seismicity in this region as due to water release
is consistent with recent observations and experiments that show
that dehydration embrittlement is a catalyst for the occurrence of
intermediate-depth seismicity (Dobson et al. 2002; Hacker et al.
2003) and with magnetotelluric studies in our region that show high
conductivity in the slab at this location (Booker et al. 2005a). This
interpretation would be inconsistent, however, with observations of
upper mantle velocities for this region that indicate a dry, cold mantle above the flat slab (Wagner et al. 2005). In addition, the WadatiBenioff zone in this region is single, which is inconsistent with
current models of standard thermally induced dehydration of slabs
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as observed in other regions for the subducted Nazca Plate (Comte
et al. 1999). A different rheology, caused by a change in temperature
or mineralogy of the slab or mantle is a distinct possibility that we
cannot rule out with this study. However, increasing seismicity and
thus a brittle rheology implies lower temperatures inconsistent with
the coincidence of the seismicity along a hot spot track younger than
the surrounding oceanic lithosphere (Pilger 1981; von Huene et al.
1997).
A simpler interpretation is that the cluster of earthquakes is a
zone of increased strain due to the bending of the slab along the
buoyant part of the JFR. This interpretation is consistent with several
observations. First, not only is there increased seismicity along the
JFR, but the seismicity is on average of higher magnitude (Fig. 12;
magnitudes from the ISC catalogue), which is consistent with high
strain along the ridge. In addition, the zone of seismicity does not
extend east of 68◦ W, though based on (Yañez et al. 2001), we would
expect the JFR to extend in this direction (Fig. 9). The absence of
seismicity could be simply due to a transition of slab deformation
to a ductile mechanism because of immediate heating of the slab
as it descends into the mantle. However, the bending hypothesis
suggests an alternate explanation. If the increased seismicity is due
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to increased bending of the slab, we would expect less seismicity
eastward, where the slab is less sharply bent as it descends into
the mantle. In addition, the few thrust focal mechanisms that we
resolve are located along the centre line of the streak of seismicity
associated with the JFR (Fig. 11b). However, P-axis orientations of
these earthquakes are not consistent with compression in the NW–
SE direction (Figs 10b and 11b), as would be expected if they result
from bending.
C O N C LU S I O N S
We have taken a fresh look at intermediate-depth seismicity in the
flat slab region of the South American subduction zone around 31◦ S.
Our results are different from previous studies mainly because our
regional network is larger and more evenly distributed than previous short-term seismic deployments and local networks. We demonstrate that while modern multiple-event relocation algorithms mitigate problems with station distribution in sparse regional networks,
they cannot correct for instability in the locations of events outside
the network, thus emphasizing the use of caution when interpreting
the hypocentre locations outside a local or regional network. The
CHARGE network geometry has enabled us to effectively evaluate the location and depth of slab seismicity greater than 80 km
depth throughout the region between 30◦ S and 36◦ S in a single,
self-consistent data set. Our relocations of the intermediate-depth
seismicity with the multiple-event relocation algorithm GMEL has
revealed a more refined slab geometry that indicates that the expected position of the JFR within the subducted slab is coincident
with not only the greatest concentration of seismicity in the flat slab
region but also the shallowest. Focal mechanism solutions and the
orientation of P- and T-axes, coupled with the new slab contours
from these events are consistent with a slab-pull model for this region. The subhorizontal T-axes indicate that the slab pulls away
from the JFR, which is an indication of the buoyancy of the ridge.
The T-axes that are inconsistent with slab-pull are consistent with a
potential gap in the slab below 150 km near the transition zone from
flat- to normally subducting slab geometry. The increased moment
release coincident with the ridge could simply be due to increased
bending of the slab in the region of the slab containing the buoyant segment of the ridge rather than a rheologic or compositional
change.
AC K N OW L E D G M E N T S
The CHARGE deployment is supported by NSF Grant
#EAR9811870. The first author was also supported under a National Science Foundation Graduate Research Fellowship and by a
ChevronTexaco Student Summer Fellowship. CHARGE data were
acquired with the help of the PASSCAL program and this data,
as well as GEOSCOPE data are managed by IRIS. We would like
to thank all members of the CHARGE Working Group, INPRES
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Figure 12. Contours of the subducting slab with the slab pull directions from
Fig. 11(a). Earthquakes (circles) are shaded with magnitude (mixed m b and
MD; black being the highest M ∼ 4). Certain areas (coloured circles) are
labelled with the average magnitude for earthquakes in these regions. The
highest average moment release is within the shallowest part of the flat slab.
Inset shows a perspective view looking west of the contoured slab.
and the National University of San Juan for their labour in acquiring data, and Bill Rodi for permission and assistance in using the
GMEL location code. Thanks to Steve Myers for sharing his extensive knowledge of earthquake location methods and to the reviewers
for their help in improving this manuscript. We also acknowledge
data used from the ISC, PDE-NEIC and Harvard CMT catalogues.
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