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Research Paper
Earthquake Spectra
1–25
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The Author(s) 2024
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DOI: 10.1177/87552930241267749
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Usability of Community
Seismic Network recordings
for ground-motion modeling
Shako Mohammed
1
, Rashid Shams
2
,
Chukwuebuka C Nweke
2
, Tristan E Buckreis
1
,
Monica D Kohler
3
, Yousef Bozorgnia
1
, and
Jonathan P Stewart
1
Abstract
A source of ground-motion recordings in urban Los Angeles that has seen limited
prior application is the Community Seismic Network (CSN), which uses low-cost,
micro–electro–mechanical system (MEMS) sensors that are deployed at much
higher densities than stations for other networks. We processed CSN data for the
29 earthquakes with
M
.
4 between July 2012 and January 2023 that produced
CSN recordings, including selection of high- and low-pass corner frequencies (
f
cHP
and
f
cLP
, respectively). Each record was classified as follows: (1) Broadband Record
(BBR)—relatively broad usable frequency range from
f
cHP
\
0.5 to
f
cLP
.
10 Hz; (2)
Narrowband Record (NBR)—limited usable frequency range relative to those for
BBR; and (3) Rejected Record (REJ)—noise-dominated. We compare recordings
from proximate (within 3 km) CSN and non-CSN stations (screened to only include
cases of similar surface geology and favorable CSN instrument housing). We find
similar high- to medium-frequency ground motions (i.e. peak ground acceleration
(PGA) and Sa for
T
\
5 s) from CSN BBR and non-CSN stations, whereas NBRs
have lower amplitudes. We examine PGA distributions for BBR and REJ records and
find them to be distinguished, on average across the network, at 0.005 g, whereas
0.0015 g was found to be the threshold between usable records (BBR and NBR)
and pre-event noise. Recordings with amplitudes near or below these thresholds
are generally noise-dominated, reflecting environmental and anthropogenic ground
vibrations and instrument noise. We find nominally higher noise levels in areas of
high-population density and lower noise levels by a factor of about 1.5 in low-
population density areas. By applying the 0.0015 g threshold, limiting distances for
1
Samueli Engineering, University of California, Los Angeles, CA, USA
2
Civil & Environ. Engineering, University of Southern California, Los Angeles, CA, USA
3
California Institute of Technology, Pasadena, CA, USA
Corresponding author:
Jonathan P Stewart, Samueli Engineering, University of California, 5731 Boelter Hall, Los Angeles, CA 90095, USA.
Email: jstewart@seas.ucla.edu
the network-average site condition, based on the expected fifth-percentile ground-
motion levels, are 89, 210, 280, and 370 km for
M
5, 6, 7, and 8 events, respectively.
Keywords
Accelerometers, ground-motion networks, ambient vibrations, ground-motion mod-
els, record processing
Date received: 11 March 2024; accepted: 28 June 2024
Introduction
The Community Seismic Network (CSN) is a ground-motion network currently with more
than 1200 three-component stations, mainly in Southern California (Clayton et al., 2011,
2020 http://csn.caltech.edu/), which are operated as a collaborative research effort between
Caltech and the University of California, Los Angeles. Approximately 800 of those sta-
tions were considered in this work (the additional 400 have only recently become opera-
tional). CSN uses low-cost, three-component, micro–electro–mechanical system (MEMS)
accelerometers (Phidget 1056-1) capable of recording accelerations up to twice the level of
gravity. This amplitude level was chosen to ensure that large earthquake amplitudes would
be recorded without clipping; as with any sensor, there is a trade-off between maximum
amplitude recording capability and its sensitivity (e.g. a threshold level of 0.5 g would be
four times as sensitive). The primary product of the network is the measurements of shak-
ing of the ground or in buildings from a major earthquake.
In terms of its layout and configuration, CSN differs from other seismic networks in
two principal respects. First, the sensors are spatially concentrated in parts of Southern
California, including the San Fernando Valley, San Gabriel Valley, the Los Angeles basin,
and Downtown Los Angeles. These areas have high population densities with substantial
human and industrial activity and hence are culturally noisy. This noise takes the form of
background ambient vibrations that have been found to depend on time (i.e. time of day,
day of week, and season; Clayton et al., 2020). CSN is effective at capturing ground-
motion characteristics over relatively short length scales due to its small station spacing
(
;
0.5 km). However, the instruments have relatively high noise levels compared to broad-
band seismometers and modern digital accelerometers. The instrument-related noise,
coupled with the environmental (both anthropogenic and geological) noise levels of the
current CSN station locations, limits the distance and magnitude range for which the data
can be used for traditional applications, such as locating event hypocenters.
As with any other network, the effective noise levels of CSN stations located in the
greater Los Angeles area are important because recorded earthquake ground motions are
subject to amplitude sampling errors for low amplitudes. In the case of triggered instru-
ments, amplitude sampling errors occur when the ground shaking level at a site falls below
the trigger threshold. In the case of continuously recording instruments such as those in
the CSN, amplitude sampling errors occur when signal amplitudes do not exceed environ-
mental or instrument noise thresholds. This is typically the case at large distances and is
more limiting for small magnitude events than for large magnitude events. For a
magnitude-distance condition where the mean ground-motion amplitude is near the
threshold, unusually strong motions that exceed trigger thresholds or that fall above the
noise floor are recorded. However, weaker motions that do not exceed trigger thresholds
or that fall near or below the noise floor are not provided. Accordingly, the ground-
2
Earthquake Spectra 00(0)
motion sampling problem associated with earthquake scenarios that are likely to generate
motions below the trigger threshold is not that no records are obtained, but that the
recorded ground motions become biased toward larger values.
The objectives of this study were to evaluate effective noise thresholds of CSN data
based on the currently available recordings, to validate the recordings against those from
higher-resolution sensors, and to make the data that is judged to be reliable available in a
Ground-Motion Database used for ground-motion modeling projects (Buckreis et al.,
2023a). Two types of noise thresholds are considered: (1) threshold between clear earth-
quake motions and signals recorded during earthquakes but for which no typical earth-
quake characteristics are visually apparent and (2) threshold between earthquake signals
and 1–2 min of pre-event ambient vibrations. The ambient ground vibrations considered
in these analyses represent the combined effects of environmental and cultural sources,
and hardware-related sources (electronic, sensor and digitizer resolution, power). The
thresholds presented here are averaged across the network and may differ from the effec-
tive thresholds located in specific geographical regions and for an earthquake occurring at
a particular time.
Following this introduction, we provide background information on the CSN, the data
produced by the network, and the events considered in this study. We then describe data
processing and assignment of classes that indicate record quality, compare CSN data to
data from other networks, analyze noise recordings from CSN sensors to evaluate spatial
variations and amplitude thresholds separating usable from noise-dominated records, and
identify usable distance ranges for CSN data for application in ground-motion modeling
projects, such as Next-Generation Attenuation (NGA)-West3 (https://www.risksciences.u-
cla.edu/nhr3-nga-west3-home). Results of this study were previously presented in a project
report (Stewart et al., 2023).
CSN overview
Over the duration of this project, the data development portion of which effectively con-
cluded in September 2023, the CSN comprised 769 seismic station locations, most of
which are in southern California (Clayton et al., 2020). In addition, there are 339 previ-
ously active but now decommissioned station locations, some of which produced data that
are evaluated. Figure 1 shows the locations of CSN stations overlaid on a regional map
that also shows stations from other regional networks (California Strong Motion
Instrumentation Program, CSMIP; US Geological Survey, USGS; Southern California
Seismic Network, SCSN).
CSN uses low-cost, three-component, MEMS accelerometers. The primary products of
the network are measurements of shaking of the ground and upper floors in buildings.
Each sensor records time series data in real time at 250 samples per second (sps), which are
then downsampled to 50 sps. Prior to
;
2014, most CSN stations consisted of plug-in sen-
sors that were attached to community hosts’ laptops and desktop computers. This deploy-
ment type no longer exists. After 2014, all CSN sensors are stand-alone devices deployed
by a CSN field engineer who determines location and physical coupling with the floor.
For this study, we focused on all ground level and basement stations in southern
California, each of which has been assigned an instrument housing code using guidelines
provided in Table 6 of Consortium of Organizations for Strong—Motion Observation
Systems (COSMOS, 2001). That table provides categories for classifying stations using
Mohammed et al.
3
two main categories (free-field and structural / array stations); within each category, a
series of specific codes are provided. This information is provided as metadata accompa-
nying the CSN sites in the ground-motion database (GMDB; Buckreis et al., 2023a). The
applicable codes that were applied to CSN stations, including those on upper floors of
buildings, are as follows:
1. ‘‘04’’—ground-floor in a one- to two-story building without a basement (1250 CSN
stations)
2. ‘‘05’’—ground-floor in a larger structure (118 CSN stations)
3. ‘‘09’’—basement or underground in a large vault (27 CSN stations)
4. ‘‘10’’—upper levels of a structure (463 CSN stations)
While none of these conditions can be considered as ‘‘free-field,’’ experience has shown
that instruments in small (in plan dimension) structures without embedment can reason-
ably approximate free-field conditions (Stewart, 2000). Such conditions correspond to sta-
tions in Group 04. Stations in 05 and 09 might be approximated as free-field depending
on the depth of embedment (for 09) and plan size of the structure (for 05). Stations from
groups 04, 05, and 09 are the ones used here. The difference between the 769 figure
Figure 1.
Map of Southern California showing locations of ground-motion stations considered in prior
work (NGA-West2/Bozorgnia et al., 2014 and basin study by Nweke et al., 2022) (CSMIP, USGS, SCSN)
and CSN stations (active and decommissioned) considered in this project. Many of these same CSN and
non-CSN stations were also considered in a Ridgecrest earthquake study by Filippitzis et al. (2021).
4
Earthquake Spectra 00(0)
mentioned at the start of this section and the sum of 04, 05, and 09 is caused by the occur-
rence of multiple stations at a given site at the ground level or basement level.
Clayton et al. (2020) and Stewart et al. (2023) provide information on the deployment
of CSN stations over time in different parts of the greater Los Angeles area, details of the
MEMS sensors and their real-time communication with central computers, and locations
where unprocessed CSN data are archived for public use.
Figure 2 shows the locations of 13 of 29 events considered in this study (the others are
outside the limits of the map). We include events recorded by the network with
M
.
4.
The stations that recorded the events are color-coded based on their date of deployment,
which is important mainly because of the more robust instrument installations since 2014.
Table 1 lists the events and their key attributes for engineering studies. Per NGA protocols
(e.g. Contreras et al., 2022), seismic moment is taken from the global centroid moment ten-
sor catalog (Ekstro
̈
m et al., 2012; https://www.globalcmt.org/) as are other moment tensor
attributes with the exception of hypocenter location, which is taken from USGS (https://
www.usgs.gov/programs/earthquake-hazards/earthquakes). The number of CSN records
listed in Table 1 is the total number of records considered in this study, even if the data
were ultimately deemed unusable. The number of non-CSN records is the number of pro-
cessed records in the GMDB (Buckreis et al., 2023a).
Data processing and classification
Processing
The Next-Generation Attenuation (NGA) program has developed standard procedures
for processing earthquake ground motions. The aim of these procedures is to minimize the
effects of noise on recorded ground motions while optimizing the dynamic range for which
Figure 2.
Map of CSN stations and events they recorded from 2012 to mid-2023.
Mohammed et al.
5
Table 1.
Earthquakes considered in this study and numbers of CSN and non-CSN records.
Date
Name
M
0
(dyne-cm)
M
1
Hypo
Lat (
°
)
Hypo
Long (
°
)
Hypo
depth (km)
Num.
CSN recs
Num.
Non-CSN recs
1
8 August 2012
Yorba Linda 2
1.90E
+
22
4.12
33.904
2
117.791
10.2
561
831
2
26 August 2012
Brawley
1.64E
+
24
5.41
33.019
2
115.54
8.3
609
668
3
11 March 2013
Anza
1.74E
+
23
4.79
33.501
2
116.458
10.9
576
687
4
29 May 2013
Santa Barbara
Channel
3.26E
+
23
4.97
34.406
2
119.92
7.1
648
522
5
24 July 2013
Weldon
4.29
L
35.486
2
118.288
6.6
675
614
6
25 August 2013
Weldon 2
2.39E
+
22
4.19
35.48
2
118.285
1.2
672
678
7
6 October 2013
Joshua Tree
1.23E
+
22
4.03
34.709
2
116.294
0.8
597
420
8
15 January 2014
Fontana 3
5.59E
+
22
4.46
34.143
2
117.443
2.9
423
747
9
17 March 2014
Westwood
5.03E
+
22
4.43
34.134
2
118.486
9.2
585
957
10
29 March 2014
La Habra
6.29E
+
23
5.17
33.933
2
117.916
5.1
525
1455
11
4 January 2015
Lake Castaic
2.95E
+
22
4.28
34.617
2
118.63
7.8
702
783
12
27 December 2015
Johannesburg
2.96E
+
22
4.25
35.214
2
117.282
3.2
1056
807
13
30 December 2015
Devore
4.94E
+
22
4.43
34.191
2
117.413
7
1059
930
14
6 January 2016
Banning
4.80E
+
22
4.42
33.959
2
116.888
16.7
1044
987
15
20 February 2016
Lucerne Valley
3.68E
+
22
4.34
34.61
2
116.629
6.7
1104
816
16
24 February 2016
Wasco
3.32E
+
23
4.98
35.542
2
119.373
22.1
1113
677
17
10 June 2016
Borrego Springs
1.03E
+
24
5.31
33.432
2
116.443
12.3
996
1471
18
7 December 2017
Julian
1.09E
+
22
3.96
34.148
2
116.479
11.1
1488
777
19
25 January 2018
Trabuco Canyon
1.13E
+
22
3.97
33.741
2
117.491
11.2
1479
1155
20
5 April 2018
Santa Cruz Island
1.37E
+
24
5.39
33.82
2
119.734
9.8
1197
1104
21
8 May 2018
Cabazon
6.80E
+
22
4.49
34.016
2
116.78
12.9
1155
1319
22
29 August 2018
La Verne 3
4.66E
+
22
4.38
34.136
2
117.775
5.5
975
937
23
4 July 2019
Searles Valley
5.95E
+
25
6.48
35.705
2
117.504
9.78
1608
1488
24
6 July 2019
Ridgecrest
4.39E
+
26
7.06
35.77
2
117.599
8
1602
2217
25
30 July 2020
Pacoima
2.25E
+
22
4.17
34.302
2
118.438
8.9
2112
1148
26
19 September 2020
El Monte
8.11E
+
22
4.54
34.038
2
118.08
16.9
2115
1465
27
5 April 2021
Lennox
1.25E
+
22
4.03
33.941
2
118.333
19.3
2118
1128
28
18 September 2021
Carson
3.29E
+
22
4.28
33.831
2
118.264
11.9
2124
1090
29
25 January 2023
Malibu
2.44E
+
22
4.19
33.885
2
118.705
14.7
2055
741
1
Moment magnitude;
L
Local magnitude.
6
Earthquake Spectra 00(0)
a given recording can be considered to accurately represent the ground shaking at the site.
The most recent procedures are described in the work by Goulet et al. (2021) and Kishida
et al. (2020), although the main elements of the procedure were presented earlier in the
work by Boore (2005), Boore and Bommer (2005), and Douglas and Boore (2011). The
principal steps are as follows:
1. Visual screening of records to remove signals that are noise-dominated. This is a
critical step with CSN data, particularly for recordings from small
M
events located
outside of the main instrumentation region in Figure 2.
2. Identify noise and signal windows in the time domain.
3. Compute normalized Fourier Amplitude Spectra (FAS) of the noise and signal
windows. FAS are computed after zero-padding the end of the record to increase
the number of data points to a power of 2. The normalization involves division of
Fourier coefficients by the square-root of the signal duration to ensure consistent
(duration-insensitive) Fourier amplitudes.
4. Application of high- and low-pass acausal filters. High-pass corner frequencies
(
f
CHP
) were selected to ensure adequate signal-to-noise ratio and to remove numer-
ical artifacts (wobble) from double integration to displacement. This involved itera-
tions of
f
CHP
selection followed by inspection of displacement time series and FAS.
Final values of
f
CHP
were those that minimized numerical artifacts while maximiz-
ing bandwidth (i.e. minimizing
f
CHP
). Low-pass corner frequencies were selected as
the smaller of 0.75
3
f
Nyq
or the frequency where signal-to-noise ratio falls below
a threshold.
5. Remove zero-padding in the filtered signal and baseline correct it, as needed, to
remove drift.
The above steps were applied using a version of the processing code gmprocess (Hearne
et al., 2019) modified to facilitate the above workflow (Ramos-Sepulveda et al., 2023). The
workflow involves automated processing in gmprocess that produces initial estimates of
f
CHP
that are then checked, and as needed adjusted, in a graphical user interface. Stewart
et al. (2023) provide additional details and illustrations of the processing steps for CSN
data. The procedures were applied to all CSN recordings from the events listed in Table 1.
We also queried the GMDB for those events. Some of our events were not already in the
GMDB, so we added those and processed the non-CSN data in a similar manner. The pro-
cessing of non-CSN data was performed to enable between-network signal comparisons as
described subsequently.
CSN data classification
In our evaluations of the CSN data, we observed three general categories of records. The
‘‘best’’ records (Broadband records, BBR) clearly reflect earthquake shaking, have wave-
forms where the different wave arrivals are evident, and exhibit only modest effects of
noise. Records deemed unusable (Rejected, REJ) appear to be noise-dominated, generally
based on visual inspection of time series, but sometimes from similar levels of signal and
noise FAS. The intermediate case (Narrowband records, NBR) has the visual appearance
of earthquakes, but the signal is of modest strength in comparison to noise, and the record
bandwidths are relatively limited.
Mohammed et al.
7
We developed criteria to distinguish different record categories for threshold analyses
and data comparisons. After some trial and error, the category definitions are as follows:
1. BBR: relatively broad usable frequency range from
f
cHP
\
0.5 to
f
cLP
.
10 Hz
2. NBR: limited usable frequency range because corner frequencies do not meet the
criteria for BBR (i.e.
f
cHP
.
0.5 or
f
cLP
\
10 Hz)
3. REJ: visual evidence suggests seismic waves cannot be distinguished from noise
Figure 3(a), (b), and (c) show examples of records assigned as BBR, NBR, and REJ,
respectively. In each case, the figures were generated by gmprocess. At the top of each col-
umn, an indication is given of whether the record ‘‘passed’’ or ‘‘failed’’ depending on the
visual screening criteria described in the previous section. Corner frequencies applied in
the filtering are those from automated algorithms, and as a result, there are cases where
low-frequency numerical artifacts in displacement occur that were removed in subsequent
processing. In Figure 3(a), the signal FAS exceed that of noise over a wide frequency
range, resulting in a BBR assignment. In Figure 3(b), the signal FAS exceeds that of noise
over a narrower frequency range—in particular, the values of
f
cHP
are
.
0.5 Hz, which
causes the NBR assignment. In Figure 3(c), the signal FAS are generally similar to, or in
some cases below, the noise FAS. Those relative amplitudes, along with the obvious effects
of noise in the time series, are the reason for the REJ assignment.
Table 2 indicates the number of CSN individual-component recordings in each cate-
gory for each of the 29 considered events. The number of usable ground motions for mod-
eling purposes, which combine horizontal components typically as the median component
(RotD50; Boore, 2010), is approximately 1/2 to 1/3 of the numbers shown in Table 1.
For some events (e.g. 2019 Searles Valley, 2019 Ridgecrest, and 2020 El Monte), large
percentages of ground motions have usable bandwidth (BBR and NBR), whereas for oth-
ers (2013 Weldon and Joshua Tree), all records are rejected based on the criteria presented
in the previous section. Figure 4 shows that the correlation of ground motion with magni-
tude and distance is driving the classification of CSN records. In the upper portion of the
plot (large magnitudes) and the close distance portion of the plot for
M
\
5 events, most
records are BBR, whereas in the lower-right portions of the plot (
M
\
5 event and dis-
tances
.
50–100 km) most records are REJ. This is also reflected in summary statistics
for the dataset. Among events since 2018, large magnitude events and events generally
closer than 70–80 km to the network (Malibu, Carson, Lennox, El Monte, Pacoima,
Searles Valley, Ridgecrest, La Verne) have the following aggregate component record
classifications:

Usable records (BBR and NBR): 5784 (1122 BBR, 4662 NBR)

REJ: 4024
The database as a whole, which includes many events with small magnitude and large dis-
tances, breaks down as

Usable records (BBR and NBR): 9950 (1446 BBR, 8504 NBR)

REJ: 11987
8
Earthquake Spectra 00(0)
CSN usable amplitude thresholds
The effects of noise on CSN data due to the station location environments (cultural, geo-
logical) and instrument can be significant (e.g. Figure 3(c)). As such, it is important to
establish a threshold level of ground motion that separates CSN earthquake signals (BBR
or NBR) from noise-dominated data (REJ signals or pre-event noise). In this section, we
address this question on a network-wide level using the ground-motion parameter of
individual-component peak ground acceleration (PGA). Individual components are used
in lieu of combinations of components (RotD50) because for some stations, individual
components can have different classifications (e.g. one component may be BBR and the
other NBR). Various intensity measures (
IM
s) were considered for the derivation of this
threshold, and PGA was found to be the most effective. Only data from ground-level
instruments (COSMOS codes 4 and 5) were considered for threshold analyses.
Figure 3.
Plots from gmprocess showing three-component records from (a) the 2019 Ridgecrest
earthquake in which the component was assigned as BBR and the filter corners were automatically
selected within gmprocess, (b) 2020 El Monte earthquake in which each component was assigned as
NBR and the filter corners were automatically selected within gmprocess, and (c) the 2020 Pacoima
earthquake in which each component was assigned as REJ and no filtering was applied because the
record failed screening criteria. The top three rows are time series, the fourth row is FAS, and the
bottom row is signal-to-noise ratio (SNR). In the time-series plots, the vertical red line indicates p-wave
arrival. In the FAS plots, raw and smoothed FAS are shown for the signal (blue) and noise (red). The
dashed black curve indicates a Brune’s spectrum (Brune, 1970) fit to the signal FAS with the corner
frequency indicated by the vertical dashed line. The gray line in SNR plots indicates unity.
Mohammed et al.
9