Journal of Geophysical Research Solid Earth
Supporting Information for
Seismicity and shallow slab geometry in the central Vanuatu subduction zone
C. Baillard1, W.C. Crawford1, V. Ballu2, M. Régnier3, B. Pelletier4, E. Garaebiti5
1Université
Paris Diderot – PRES Sorbonne Paris Cité - Institut de Physique du Globe de Paris, UMR CNRS 7154, Paris,
France, 2Université de La Rochelle - Littoral, Environnement et Sociétés (LIENSs), UMR CNRS 7266, La Rochelle, France,
3Université Nice-Sophia Antipolis - Géoazur, UMR CNRS 6526, IRD, Valbonne, France, 4Institut pour la Recherche et le
Développement (IRD), Nouméa, Nouvelle Calédonie, 5Vanuatu Meteorology and Geohazards Department, Port Vila,
Vanuatu
Contents of this file
Text S1 to S3
Figures S1 to S6
Tables S1 to S2
Introduction
This supporting information includes texts explaining the methodology used to 1) test the sensibility
of our velocity model, 2) assess the spatial errors of our locations (bootstrap method) and 3),
relocate our events and assess relocated spatial errors.
Figures are provided to:
 Support the methodology section. They provide information about the stability test and the
bootstrap method.
 Show the concepts used to compare cluster orientations with composite focal mechanisms.
 Show the time evolution of events within the main clusters.
 Provide details about historical seismicity along the Vanuatu arc.
Tables give details about the focal mechanisms computed and shown on Figure 9 of the article.
1
Text S1.
The following text describes the velocity stability test we used.
The test consists of inputting the final velocity model and randomly shifted hypocenter positions
into the joint inversion for hypocenters and the velocity model. If the original hypocenters are
recovered, we consider the velocity model to be stable. We randomly shifted 285 “high-quality”
hypocenters calculated using the final velocity model by 5-10 km. The original hypocenter positions
are recovered to within ~0.2 km (Figure S1), indicating that the velocity model is stable.
Text S2.
The following text describes the principle of the bootstrap method used to assess spatial errors in
our catalog.
The HYPOCENTER software provides error estimates that are calculated using assumptions of
linearity that may be invalid for the velocity model and/or the network/earthquake geometry. We
use a “bootstrap” method [e.g., Billings et al., 1994] to separately quantify the influence of picking
errors and network geometry on the location error. This method makes no linearizing assumptions,
is easy to implement and gives a direct and comprehensible overview of location errors. It also
allows us to introduce a different estimate of picking P- and S- wave picking errors for each station
of the network.
First, we define a set of synthetic hypocenters in the vicinity of the network and compute theoretical
travel times from these hypocenters to a set of stations in the network. We then perturb these travel
times at each station using a random Gaussian distribution centered at zero and with a standard
deviation equal to the picking uncertainty at that station. Finally, we use the "perturbed" travel
times to calculate the hypocenter. We repeat the last two steps many times to obtain a “cloud” of
hypocenters that statistically images hypocenter location error.
We defined a set of 225 synthetic hypocenters near and under the network, with a horizontal
spacing of 50 km and vertical spacing of 30 km starting at 10 km depth. To reflect the most common
picking conditions, we only use 10 onshore stations (Figure S2a). The Gaussian perturbation applied
to theoretical travel times is based on the standard deviation of our automatic picks with respect to
a subset of manual picks: 0.1 s for P-picks and 0.2 s for S-picks [Baillard et al., 2014]. We repeat the
perturbation-inversion process 300 times to generate sets large enough to image the location errors
(Figure S2b-c). Hypocenters are well defined in the first 75 km beneath the network (errors < 10 km)
but horizontal errors grow rapidly with depth (errors > 20 km for depths > 100 km). Vertical errors
are also important outside the network, reaching 20 km at 40 km depth and 70 km away from the
network. As is often stated (but rarely quantified), hypocenters located at a distance larger from the
network than the network aperture (~ 100 km in our case) can not be used for proper interpretation
due to their large spatial uncertainties.
Text S3.
The following text describes the relocation process and error estimate methodology.
The first step in our double-difference relocation is the calculation of the time delays between
waveforms, using cross-correlations. We computed cross-correlations between all events in the
Total catalog (31,019 events), using a 3-second window for P-waves and an 8-second window for Swaves to establish the correlation value and the time delay (signals were bandpass filtered from 320Hz prior to cross-correlation). Only 15% of event pairs have correlations higher than 0.8.
We next used HypoDD [Waldhauser, 2000] to relocate the paired events using their cross-correlation
delays. We only relocate pairs that 1) show a correlation value > 0.8, 2) are observed on more than 2
2
stations and 3) have inter-event distance < 5 km. 1621 of the 9514 events in the Local catalog satisfy
these criteria and 837 of them (in 118 clusters) could be relocated. The largest cluster contains 70
events.
The relative error of events inside the clusters can be assessed using the bootstrap approach. This
time we perturb the time differences by the cross-correlation measurement error, which is on the
order of the sampling rate of the data, 10 ms in our case [Waldhauser, 2001]. We then relocate
cluster events using the perturbed travel time differences. We repeat the perturbation/relocation
steps 200 times. We applied this bootstrap approach to the 30 largest clusters, which contain 64 %
of the relocated events. Spatial errors are obtained by taking the 95% error ellipses on relocated
positions and by adding a systematic error of 40 m due to possible velocity variations inside a given
cluster. For all clusters studied the relative spatial error is less than 240 meters, approximately 12 %
of the average cluster size.
3
Figure S1. Illustration of the velocity model stability test. Grey dots represent the offset of 285
hypocenters from their initial positions after being randomly shifted by 5-10 km. Black dots show
the offset after a joint inversion using the best velocity model. Mean values (m) and standard
deviations (σ) are also indicated for each direction.
4
Figure S2. Bootstrap analysis of location errors. (a) Map view of the test configuration with
synthetic hypocenters shown by orange dots (there are 9 hypocenters beneath each dot, equally
spaced from 10 to 250 km depth). Triangles represent the stations. The perturbation/inversion
process is applied 300 times, with applied perturbations following a Gaussian law with 0 mean and
0.1 (0.2) s standard deviations for P (S) travel times. (b) Cross-section along profile 1 using all
stations. Red dots indicate hypocenters obtained, black ellipses indicate uncertainties estimated
using HYPOCENTER’s linearizing assumption. (c) Same as b, but representing a more common case
for this experiment were travel-times are only picked on 10 onshore stations (green triangles in a).
5
Figure S3. Comparison of focal mechanisms with cluster geometries. (a) Focal mechanism
geometry and two nodal plane poles. (b) A cluster’s mean pole, calculated using the three points
method. (c) The poles are compared using a polar diagram (φ and θ are respectively the azimuth and
the colatitude of the poles), stars represent the focal mechanism poles and the thin white contour
their error estimated using HASH [Hardebeck and Shearer, 2002]. The color-filled contours represent
the probability density distribution of the cluster pole. The case shown here represents a good fit
between the focal mechanism pole 1 and the cluster pole, indicating that the earthquakes are
distributed on nodal plane 1.
6
Figure S4. Comparison of nodal poles and pole distribution using the three points method for
clusters 1 and 7. These clusters have a linear shape (poles aligned in a circle around the cluster axis)
and this distribution of poles intersects one of the focal mechanism poles, indicating that the
earthquakes lie on its plane, which may be interpreted as the rupture plane.
7
Figure S5. Magnitude and time distribution of events in the 13 clusters with composite focal
mechanisms. (a) Map view of the locations of clusters and their composite focal mechanisms. (b)
Distribution of cluster events with time (x-axis).
8
Figure S6. Vanuatu region earthquakes having M > 7.5, since 1900 (ISC catalog). The lower panel
shows the distribution of these earthquakes with time.
9
Date
2008-05-06T01:20:15.2
2008-05-06T21:18:52.8
2008-05-30T01:29:53.4
2008-07-07T09:41:27.1
2008-08-16T02:03:44.8
2008-08-16T22:49:11.2
2008-09-01T17:22:53.0
2008-09-07T11:30:08.0
2008-11-25T07:20:31.7
2009-01-15T18:45:58.1
ML
Lon. (°E)
Lat. (°N)
3.4
3.2
3.2
3.1
3.4
3.2
3.0
3.0
3.3
3.0
167.222
167.261
166.786
166.706
167.242
167.249
167.129
166.931
167.220
167.086
-15.928
-15.762
-15.730
-15.721
-15.724
-15.777
-15.807
-15.679
-15.714
-15.756
Depth
(km)
23.7
28.0
35.0
29.9
35.0
30.1
22.8
23.4
25.0
22.6
Strike
(N°)
149.9
271.6
267.5
275.7
344.3
322.8
116.2
215.3
301.9
297.5
Dip
(°)
34.8
29.2
50.8
54.4
30.9
32.8
35.0
71.3
7.9
33.9
Rake
(°)
-105.6
28.7
-11.4
-7.3
84.1
60.9
40.7
-160.3
100.6
48.3
Table S1. Characteristics of the focal mechanisms shown in Figure 9
Clus. ID
1
3
5
6
7
8
11
12
13
14
15
17
18
Number
of events
70
47
27
25
26
25
19
16
16
13
15
11
11
ML range
Lon. (°E)
Lat. (°N)
0.8
0.7
0.7
0.7
0.4
0.9
0.6
0.9
0.9
0.7
1.0
1.0
1.1
166.905
166.919
166.950
167.021
166.969
167.183
167.343
167.189
166.788
167.150
167.130
166.995
166.916
-15.177
-15.442
-15.247
-15.373
-15.224
-15.763
-15.620
-15.504
-15.672
-15.330
-15.677
-15.336
-15.256
-
2.8
2.7
2.7
2.1
2.5
3.0
2.3
2.3
2.2
2.2
2.7
2.6
2.7
Depth
(km)
24.2
31.5
23.8
29.8
15.8
22.6
22.1
33.1
23.1
17.7
21.9
31.5
16.9
Strike
(N°)
358.4
191.7
35.4
148.6
48.7
212.2
164.8
226.8
274.6
141.3
359.7
289.5
306.5
Dip
(°)
45.6
45.0
52.7
40.4
33.6
68.6
48.0
31.0
43.1
51.4
76.3
12.3
18.4
Rake
(°)
128.2
-103.6
137.4
-100.2
69.9
-172.1
-1.0
60.1
-3.3
-141.4
149.2
-45.9
157.5
Table S2. Characteristics of the composite focal mechanisms (associated to relocated clusters)
shown in Figure 9.
10
References
Baillard, C., W. C. Crawford, V. Ballu, C. Hibert, and A. Mangeney (2014), An automatic kurtosisbased P-and S-phase picker designed for local seismic networks, Bull. Seismol. Soc. Am.,
104(1), 394–409, doi:10.1785/0120120347.
Billings, S., M. S. Sambridge, and B. L. N. Kennett (1994), Errors in hypocenter location: picking,
model, and magnitude dependence, Bull. Seismol. Soc. Am., 84(6), 1978–1990.
Hardebeck, J. L., and P. M. Shearer (2002), A new method for determining first-motion focal
mechanisms, Bull. Seismol. Soc. Am., 92(6), 2264–2276, doi:10.1785/0120010200.
Waldhauser, F. (2000), A Double-Difference Earthquake Location Algorithm: Method and
Application to the Northern Hayward Fault, California, Bull. Seismol. Soc. Am., 90(6), 1353–
1368, doi:10.1785/0120000006.
Waldhauser, F. (2001), hypoDD - A Program to Compute Double-Difference Hypocenter Locations.
11