1
Supplementary Material
2
Allison et al., Seismic evidence for lithospheric modification beneath the Mojave Neovolcanic
3
Province, southern California, Submitted to Geophys. Res. Lett., Sep. 2013
4
5
Data
6
We retrieved waveform data from broadband seismic stations (Figure 1b; see also Table
7
S1) archived at the IRIS Data Management Center (DMC) and the Southern California
8
Earthquake Data Center using the Standing Order for Data [Owens et al., 2004] seismic data
9
request framework. We selected events from May 1991 to May 2012 with mb≥5.5 from the
10
National Earthquake Information Center’s Weekly Hypocenter Data File catalog for epicentral
11
distances between 30° and 95° from our study region (Figure S1; Table S1). We filtered the raw
12
waveforms with a 0.02 Hz high pass and a 10% taper. We then viewed each individual trace and
13
removed RFs in which the coherence phase was not the highest amplitude arrival, were
14
particularly monochromatic, contained a negative coherence phase, or a coherence phase with no
15
other spikes. This trace editing yielded 7,445 RFs suitable for further analysis (Table S1).
16
Table S1: Stations and events used for the receiver function (RF) portion of this study.
Station
(Network Code
and Station
Code)
CI.ADO
Range of Events
Obtained
(Year and Julian Day)
Latitude Longitude
(°N)
(°E)
Total
RFs Used
Events
in Analyses
Retrieved
2004 (003) - 2012 (140)
34.5505
-117.4339
2504
0
CI.BBR
2000 (175) - 2012 (121)
34.2623
-116.9208
3364
0
CI.BEL
2001 (115) - 2012 (121)
34.0006
-115.9982
3106
615
CI.BKR
1999 (202) - 2002 (032)
35.2693
-116.0703
550
22
CI.BLA
2000 (166) - 2009 (002)
34.0695
-116.389
2152
380
1
CI.BLA2
2009 (242) - 2012 (121)
34.0693
-116.3899
1116
95
CI.CCC
2001 (174) - 2012 (121)
35.5249
-117.3645
3174
78
CI.CLT
1999 (353) - 2012 (121)
34.0928
-117.3169
3504
0
CI.DAN
1998 (267) - 2011 (231)
34.6375
-115.3811
3396
68
CI.DSC
2002 (320) - 2009 (292)
35.1426
-116.104
1825
185
CI.FHO
2010 (093) - 2012 (121)
34.0936
-116.9359
864
0
CI.FUR
2002 (003) - 2012 (140)
36.467
-116.8632
2670
212
CI.GMR
2006 (123) - 2011 (059)
34.7846
-115.6599
1399
299
CI.GSC
1997 (216) - 2011 (231)
35.3018
-116.8057
3645
161
CI.HEC
2004 (032) - 2011 (231)
34.8294
-116.335
2411
117
CI.IRM
2002 (013) - 2012 (121)
34.1574
-115.1451
2883
432
CI.JVA
2001 (115) - 2012 (121)
34.3662
-116.6127
3184
432
CI.LDF
2000 (002) - 2011 (091)
35.1307
-115.1842
2999
137
CI.LUG
1999 (202) - 2012 (121)
34.3656
-117.3668
2632
0
CI.MCT
2001 (032) - 2012 (121)
34.2265
-116.0407
3181
443
CI.MPM
1998 (232) - 2012 (140)
36.058
-117.489
3374
424
CI.MSC
2009 (116) - 2012 (121)
34.0385
-116.6479
1195
0
CI.MTP
1999 (202) - 2011 (091)
35.4843
-115.5532
3069
240
CI.NBS
2002 (010) - 2012 (121)
34.7803
-116.558
2889
358
CI.NEE
1993 (107) - 2006 (204)
34.823
-114.596
1975
0
CI.NEE2
2006 (217) - 2011 (231)
34.7676
-114.6188
1456
124
CI.PDM
2001 (336) - 2012 (121)
34.3034
-114.1415
2682
514
CI.RRX
2001 (019) - 2012 (121)
34.8753
-116.9968
3178
0
2
CI.SBPX
2001 (003) - 2005 (228)
34.2324
-117.2348
208
0
CI.SHO
2004 (003) - 2011 (231)
35.8995
-116.2753
2358
64
CI.SLA
1999 (079) - 2012 (140)
35.8909
-117.2833
3562
277
CI.SNO
2008 (344) - 2012 (121)
34.0351
-116.8078
1290
0
CI.SVD
1991 (142) - 2012 (140)
34.1065
-117.0982
4036
329
CI.TUQ
2004 (001) - 2011 (059)
35.4358
-115.9239
2168
148
CI.VTV
1993 (107) - 2012 (140)
34.5606
-117.3296
4389
387
TA.U10A
2006 (169) - 2008 (286)
36.4193
-116.3297
657
0
TA.U11A
2006 (173) - 2008 (286)
36.423
-115.3835
652
0
TA.U12A
2006 (173) - 2008 (285)
36.4321
-114.5388
646
85
TA.U13A
2007 (048) - 2008 (313)
36.4151
-113.9653
506
0
TA.V11A
2006 (169) - 2008 (286)
35.8384
-115.4305
635
51
TA.V12A
2006 (173) - 2008 (286)
35.7266
-114.8511
643
135
TA.V13A
2007 (070) - 2008 (286)
35.8522
-113.984
475
104
TA.W12A
2006 (086) - 2008 (286)
35.301
-114.8701
711
51
TA.W13A
2006 (087) - 2010 (148)
35.099
-113.8854
1213
278
TA.X13A
2006 (074) - 2008 (286)
34.5935
-113.8302
705
200
17
18
We note that while we retrieved events for stations ADO, BBR, CLT, FHO, LUG, MSC,
19
NEE, RRX, SBPX, SNO, U10A, U11A, and U13A we did not use the data in our RF study, as
20
the seismograms were of significantly lower quality. Most of these stations with lower quality
21
seismograms are located near the San Andreas Fault where we were able to maintain very good
22
coverage despite their removal. Stations U10A, U11A, and U13A are located along the northern
3
23
boundary of our study area where coverage is more sparse, however their exclusion was
24
necessary to maintain the quality of our analysis. Figure S1 shows a map of events used in this
25
study.
26
27
Methods
28
Receiver Functions
29
We used the Ligorria and Ammon [1999] iterative deconvolution approach, which
30
minimizes the misfit between the observed horizontal (radial or transverse) seismogram
31
compared to the vertical seismogram convolved with the predicted receiver function. In this
32
method, we first rotated the horizontal seismograms to radial (R) and transverse (T). We then
33
used
34
(http://eqseis.geosc.psu.edu/~cammon/HTML/RftnDocs/rftn01.html
35
compute receiver functions. In this method, we first cross-correlate the vertical component with
36
the radial to estimate the lag and amplitude of the first and largest spike which of the RF. Next,
37
we convolve the RF, at first consisting of just one spike, with the vertical-component
38
seismogram, and subtract it from the radial component. We repeat the procedure for other spikes,
39
estimating both the lag and amplitude, to complete the final RF. In this way, we reduce the
40
difference between the RF+vertical and the radial seismogram until it is insignificant. We repeat
41
this same process for the transverse component RFs. We used a Gaussian parameter of 2.5 for
42
this study.
the
software
package
distributed
by
Chuck
and
links
Ammon
therein)
to
43
To manage the thousands of seismograms computed for this study, we utilized the
44
FuncLab system [Eagar and Fouch, 2012]. FuncLab is a package of MATLAB scripts that is
45
capable of handling large datasets for commonly utilized RF analyses. FuncLab contains a set of
4
46
graphic user interfaces (GUIs) to guide both basic and advanced users through a range of RF
47
analyses. We use FuncLab version 1.5.3 in our study for both the H-κ and common conversion
48
point stacking methods outlined in the following two paragraphs.
49
To evaluate individual station characteristics, we used the H-κ stacking method to
50
estimate the depth to the Moho (H) and the ratio Vp/Vs (κ) beneath a seismic station [Zhu and
51
Kanamori, 2000]. This method assumes a simple configuration of an isotropic, flat-lying,
52
homogeneous layer over a half-space. In this computation, we used equations from Eagar et al.
53
[2011], modified for a spherical Earth from Zhu and Kanamori [2000], to calculate the travel
54
time between the direct P and the Ps, as well as crustal reverberations PpPs and PsPs+PpSs, for
55
each RF within an H-κ grid space. We use all three phases in the calculation to allow for a more
56
accurate determination of H and κ. As travel times are calculated for each grid value of H and κ,
57
the amplitudes of all RFs from a single station are summed using the stacking equation from
58
Eagar et al. [2011], using different weighting terms for each arrival. For the direct Ps, as well as
59
crustal reverberations PpPs and PsPs+PpSs, we used relative stacking weights of 0.5, 0.3, and
60
0.2, respectively, although we adjusted these values at stations with very few RFs. We use a grid
61
range of 20-40 km for H and 1.4-2.1 for κ, with step sizes of 0.5 km and 0.01, respectively, but
62
altered the range somewhat at a few stations where these values were broad enough for the
63
stacking method to select secondary peaks. The final H and κ values are selected where the
64
stacking equation is maximized. From κ, we can calculate Poisson’s ratio (σ), an elastic property
65
that measures the fractional deformation of a solid, which generally ranges from 0.25-0.35 in
66
crustal rocks, but increases rapidly beyond that with partial melt content.
67
To determine 3D impedance contrast structure, we computed Common Conversion Point
68
(CCP) stacks using all viable RF traces. We first divided the study volume into imaging points,
5
69
which later become the center points of stacking bins. We computed piercing points of the
70
converted Ps waves using the spherical travel time equation from Eagar et al. [2011]. In
71
FuncLab, we completed a three-step process to compute CCP stacks, beginning with 1D ray
72
tracing, followed by a time-to-depth migration (assignment of RF amplitudes at each depth to
73
CCP bins), and finally the stacking itself [Eagar and Fouch, 2012]. We divided our imaging
74
volume into 10 km wide bins laterally with a step of 5 km between bins, and 1 km bins in depth
75
to 60 km. We utilize the resulting 3D CCP imaging volume of relative impedance contrasts in
76
concert with H-κ results and ANT models to interpret detailed 3D structure of the MNVP and
77
surrounding region.
78
Ambient Noise Tomography
79
Ambient-noise tomography is based on the principle that the cross-correlation of
80
background seismic noise recorded at two seismometers approximates the Green’s function of
81
seismic waves traveling between the two recorders. While ambient noise has been used to
82
calculate P-wave velocities [e.g., Roux et al., 2005], it is generally used to measure Rayleigh
83
waves [e.g., Shapiro et al., 2005; Yang et al., 2007; Bensen et al., 2008]. Due to the geometry of
84
wave front spreading and their slower rate of attenuation, surface waves have larger amplitudes
85
than body waves, making them easier to identify and measure. Ambient-noise tomography is
86
advantageous over earthquake-generated surface wave tomography in that it does not require
87
earthquakes and measures shorter periods and is therefore sensitive to shallower structures. The
88
primary energy sources for ambient-noise tomography are ocean storms and atmospheric
89
disturbances, which typically have peaks in amplitude at periods of 15 and 7.5 seconds
90
[Friedrich et al., 1998; Bensen et al., 2007].
6
91
We briefly outline the general process for calculating seismic velocities using ambient-
92
noise tomography in this paper. For a more detailed explanation of the data processing refer to
93
Bensen et al. [2007]. We first retrieved 1 Hz vertical component data were retrieved from
94
regional temporary and permanent stations archived at the IRIS (Incorporated Research
95
Institutions for Seismology) DMC (Data Management Center). Most stations are part of either
96
the USArray Transportable Array (TA) or Caltech regional networks. The instrument response
97
was removed from all seismograms and data were cut into 1-day increments. Seismograms from
98
each day were then bandpass filtered from 5 to 150 seconds and the data were normalized to
99
remove signals resulting from earthquakes or other discrete events. This was done using a
100
running-absolute-mean normalization method [Bensen et al., 2007]. Once normalized, a Fast
101
Fourier Transform (FFT) was used to convert the data into the frequency domain where the data
102
were whitened and cross-correlations were calculated every day for station pairs. These daily
103
station cross-correlations were transformed back in the time domain and stacked into month and
104
then yearlong increments. From these stacked cross-correlations, the signal-to-noise ratio (SNR)
105
was calculated and those inter-station measurements with a SNR less than 15 were discarded.
106
The resulting waveform for each station pair was roughly symmetric, centered on 0 seconds. The
107
positive and negative times represent the waves traveling in opposite directions along the great
108
circle path between the two stations. Dissymmetry in the waveform would result from an
109
irregular distribution of noise sources around the station. This effect is largely due to storms
110
concentrating in different hemispheres throughout the year. To increase the SNR, the time axis
111
for the negative component was multiplied by -1 and the resulting waveform stacked with the
112
positive time component, essentially folding it over the 0 second time axis and producing the
113
symmetric component.
7
114
To calculate Rayleigh wave phase velocities between stations, the stacked symmetrical
115
component of the cross-correlations were filtered over narrow bandwidths and the phase
116
velocities were measured at several periods. Measurements were made at 8, 10, 12, 14, 16, 20,
117
25, and 30 s periods. Waveforms measured at 6, 35 and 40-s periods generally had low signal-to-
118
noise ratios and were discarded. The method utilized for measuring ambient noise requires at
119
least 3 wavelengths between the stations to accurately calculate group velocity and we apply that
120
cutoff to phase velocity as well [Bensen et al., 2007]. Given the relatively small size of the array
121
compared to more common continent wide studies [e.g., Yang et al., 2007; Bensen et al., 2008]
122
there was not enough distance between many of the stations to make measurements at these
123
longer wavelengths. For these reasons, we only included ambient noise phase velocity
124
measurements made at periods between 8 and 30 s in the inversion for shear-wave velocities.
125
Using the method outlined in Barmin et al. [2001], we calculated phase velocity maps for
126
the study area at each period listed above. This is done by combining phase velocities along
127
inter-station paths to determine dispersion curves at defined grid points. In this step, we
128
experimented with grid spacing ranging from 0.5° to 0.1° and a variety of different damping
129
parameters. Given the dense station spacing, grid points located every 0.1° produced detailed
130
results that did not appear to introduce artificial error into the inversion. The damping is
131
dependent on the path density with the variable α controlling the strength of the spatial
132
smoothing, β controlling how data is merged into areas of poor data coverage and σ is the
133
smoothing length in km. We used values of α=300, β=100, and σ=100 in the penalty function to
134
best match earthquake-generated surface wave measurements. For a more detailed description of
135
the penalty functions refer to Barmin et al. [2001]. As a final quality control on the data, station
136
pairs with residuals greater than 2 seconds were removed and resolution maps were made. Phase
8
137
velocity measurements in areas with resolution lengths greater than 100 km were not included in
138
the final inversion for shear- wave velocity. Resolution can be interpreted as the minimum
139
distance at which two δ- shaped functions can be resolved [Barmin et al., 2001]. Figure S2
140
shows the resolution for this dataset.
141
142
Results
143
Table S2: Results for individual station receiver function analyses. The 13 stations with RFs
144
retrieved that did not meet our standards for use in the analysis have been excluded from this
145
table. Group numbers are described in the text. Some stations (8) did not possess simple enough
146
structure to utilize the H-κ method; in these cases, Moho depth was measured from moveout
147
curves. “--“ refers to no value for this analysis method.
From H-κ Stack
From Moveout
Station
Group
Moho Depth (km) Vp/Vs
Moho Depth (km)
CI.BEL
1
28.5
1.71
--
CI.BKR
3
--
--
25
CI.BLA
1
30
1.86
--
CI.BLA2
2
29
1.94
--
CI.CCC
3
29
1.81
--
CI.DAN
3
--
--
26
CI.DSC
2
24
1.8
--
CI.FUR
2
--
--
28.5
CI.GMR
1
25.5
1.74
--
CI.GSC
2
--
--
27
9
CI.HEC
3
24
1.9
--
CI.IRM
1
29.5
1.6
--
CI.JVA
1
30
1.81
--
CI.LDF
2
--
--
27
CI.MCT
1
29.5
1.68
--
CI.MPM
2
27
1.86
--
CI.MTP
1
23.5
1.9
--
CI.NBS
1
--
--
27
CI.NEE2
1
26
1.72
--
CI.PDM
1
26.5
1.76
--
CI.SHO
2
30.5
1.72
--
CI.SLA
2
27
1.82
--
CI.SVD
1
30
1.92
--
CI.TUQ
2
--
--
23
CI.VTV
1
28
1.77
--
TA.U12A
2
--
--
26.5
TA.V11A
3
27
1.94
--
TA.V12A
1
29.5
1.76
--
TA.V13A
1
32
1.71
--
TA.W12A
3
26.5
1.98
--
TA.W13A
1
28
1.73
--
TA.X13A
1
28
1.68
--
148
10
149
We present radial and transverse RFs binned by backazimuth for each station, sorted by
150
assigned group, in Figure S3. Group 1 stations show a strong Moho arrival that may diverge at
151
some backazimuths, a relatively weak first negative arrival, and diffuse but easy to identify
152
crustal reverberations, and produce a high percentage of high-quality RFs. Group 2 stations
153
usually display a double P-wave arrival, a strong first negative arrival, and a Moho signal that is
154
strong but widens at some backazimuths. The crustal reverberations are not as simple to
155
recognize because these stations show signs of possible basin structure, making these stations
156
only fair in terms of their data quality. Group 3 stations are located in obvious topographic basins,
157
yielding poor quality RFs that exhibit classic characteristics of basin structure exemplified by
158
strong alternating positive and negative arrivals throughout the first 15 seconds.
159
Figure S4 shows map view sections through the ambient noise tomography model, and
160
Figure S5 shows a number of vertical cross sections for both the common conversion point and
161
ambient noise tomography models centered on the Cima Volcanic Field.
162
163
Discussion
164
Table S3: Crustal model used for forward modeling RFs at station DSC. H- κ stacking analysis
165
of resulting RF indicates a 25 km thick crust with 1.825 Vp/Vs ratio, consistent with RF analysis
166
of station DSC (24 km thick crust with 1.8 Vp/Vs).
Layer #
Vp (km/s)
Vs (km/s)
Density (kg/m3)
Thickness (km)
1
5.5000
3.0556
2.6900
6.0000
2
6.2000
3.4444
2.7100
9.0000
3
5.0000
2.6000
2.7100
5.0000
4
5.6000
3.1111
2.7700
2.0000
11
5
6.4000
3.5556
2.7700
1.0000
6
8.0000
4.6188
3.3300
0.0000
167
168
169
References
170
Barmin, M.P., M.H. Ritzwoller, and A.L. Levshin, (2001), A fast and reliable method for surface
171
wave tomography, Pure Appl. Geophys., 158, 1351-1375.
172
Bensen, G.D., M.H. Ritzwoller, M.P. Barmin, A.L. Levshin, F. Lin, M.P. Moschetti, N.M.
173
Shapiro, and Y. Yang, (2007), Processing seismic ambient noise data to obtain reliable
174
broad-band surface wave dispersion measurements, Geophys. J. Int., 169, 1239–1260,
175
doi:10.1111/j.1365-246X.2007.03374.x.
176
Bensen, G.D., M.H. Ritzwoller, and N.M. Shapiro, (2008), Broad-band ambient noise surface
177
wave tomography across the United States,
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doi:10.1029/2007JB005248, 2008.
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J. Geophys. Res., 113, B05306,
Eagar, K.C., and M.J. Fouch, (2012), FuncLab: A MATLAB interactive toolbox for handling
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Eagar, K.C., M.J. Fouch, D.E. James, and R.W. Carlson, (2011), Crustal structure beneath the
182
High Lava Plains of eastern Oregon and surrounding regions from receiver function analysis,
183
J. Geophys. Res., 116, B02313, doi:10.1029/2010JB007795.
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185
186
187
Friedrich, A., F. Kruger, and K. Klinge, (1998), Oceangenerated microseismic noise located with
the Grafenberg array, J. Seismol., 2, 47-64.
Ligorria, J.P., and C.J. Ammon, (1999), Iterative deconvolution and receiver function estimation,
Bull. Seismol. Soc. Am., 89(5), 1395-1400.
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Owens, T.J., H.P. Crotwell, C. Groves, and P. Oliver-Paul, (1984), SOD: Standing order for data,
Seis. Res. Lett., 75(4), 515-520.
Roux, P., K.G. Sabra, P. Gertsoft, W.A. Kuperman, and M.C. Fehler, (2005), P-waves from
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Shapiro, N.M. M.H. Ritzwoller, J.C. Mareschal, and C. Jaupart, (2005), Lithospheric structure of
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the Canadian Shield inferred from inversion of surface-wave dispersion with thermodynamic
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a priori constraints, Geol. Soc. Lond. Spec. Publ., Geological Prior Information: Informing
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Science and Engineering, ed. R. Wood and A. Curtis, 239, 175-194, The Geological Society
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Yang, Y., M.H. Ritzwoller, A.L. Levshin, and N.M. Shapiro, (2007), Ambient noise Rayleigh
wave tomography across Europe, Geophys. J. Int., 168(1), 259.
Zhu, L., and H. Kanamori, (2000), Moho depth variation in southern California from teleseismic
receiver functions, J. Geophys. Res., 105(B2), 2969-2980.
202
203
Figure Captions
204
Figure S1. Global event map for receiver function component of this study. Pink circles are all
205
unique events retrieved for study; grey circles (overlain) are events included in analysis for at
206
least one station. Grey lines denote 30° and 95° epicentral distance range from Mojave
207
Neovolcanic Province.
208
13
209
Figure S2. a) Resolution map for ambient noise tomography at 20 seconds. Resolution is defined
210
as the minimum distance at which two d-shaped functions can be resolved. b) Inter station paths
211
used for ambient noise tomography phase velocity calculations at 20 seconds.
212
213
Figure S3. Stacked radial receiver functions from 0-30 seconds binned by backazimuth for each
214
station in RF study. Colors denote relative amplitudes; blue positive, red negative. Bars along top
215
indicate number of RFs included in each 10° bin. Description of group categories included in the
216
text.
217
218
Figure S4. Map (plan) views of ambient noise tomography model results for depth slices from 0
219
to 90 km. Depth increment shown in lower left corner of map. Colors represent % perturbation
220
from average layer model; contours annotate Vs in 0.1 km/s intervals. White triangles denote
221
volcanism with eruptive ages of 3 Ma and less from NAVDAT (http://www.navdat.org/;
222
downloaded 06/2012).
223
224
Figure S5. Cross sectional views of receiver function common conversion point (RF CCP) and
225
ambient noise tomography (ANT) results centered on the Cima Volcanic Field in 15° azimuthal
226
increments; vertical exaggeration on top topography panels only. AC - Amboy Crater; CVF -
227
Cima Volcanic Field; PC - Pisgah Crater. Locations of cross sections shown on map at top with
228
receiver function (RF) stations labeled. RF CCP stacking results shown in first 12 pages. Colors
229
denote relative amplitudes; blue positive, red negative. ANT models shown in second 12 pages.
230
Colors denote Vs in km/s. ANT results follow along same RF slices. Colors denote Vs in km/s.
14