Tracking earthquake sequences in real time: application of Seismicity-Scanning based on Navigated Automatic Phase-picking (S-SNAP) to the 2019 ...
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Geophys. J. Int. (2020) 223, 1511–1524 doi: 10.1093/gji/ggaa387
Advance Access publication 2020 August 17
GJI Seismology
Tracking earthquake sequences in real time: application of
Seismicity-Scanning based on Navigated Automatic Phase-picking
(S-SNAP) to the 2019 Ridgecrest, California sequence
Fengzhou Tan,1,2 Honn Kao,1,2 Edwin Nissen1 and Ryan Visser2
1 School of Earth and Ocean Sciences, University of Victoria, Victoria, V8P 5C2 BC, Canada. E-mail: ftan@uvic.ca
2 Geological Survey of Canada, Pacific Geoscience Centre, Sidney, V8L 5T5 BC, Canada
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Accepted 2020 August 10. Received 2020 July 19; in original form 2020 April 15
SUMMARY
Recent improvements in seismic data processing techniques have enhanced our ability to detail
the evolution of major earthquake sequences in space and time. One such advance is new
scanning algorithms that allow large volumes of waveform data to be analysed automatically,
removing human biases and inefficiencies that inhibit standardized monitoring. The Seismicity-
Scanning based on Navigated Automatic Phase-picking (S-SNAP) workflow has previously
been shown to be capable of producing high-quality earthquake catalogues for injection-
induced seismicity monitoring. In this study, we modify the original S-SNAP workflow to
enable it to delineate the spatiotemporal distribution of major earthquake sequences in real
time. We apply it to the 2019 Ridgecrest, southern California earthquake sequence, which
culminated in an Mw 6.4 foreshock on July 4 and an Mw 7.1 main shock on July 6 and generated
tens of thousands of smaller earthquakes. Our catalogue—which spans the period 2019 June 1
to July 16—details the spatiotemporal evolution of the sequence, including early foreshocks on
July 1 and accelerating foreshocks on July 4, a seismicity gap before the main shock around its
epicentre, seismicity on discrete structures within a broad fault zone and triggered earthquakes
outside the main fault zone. We estimate the accuracy and false detection rate of the S-SNAP
catalogue based on the reviewed catalogue reported by Southern California Seismic Network
(SCSN) and our own visual inspection. We demonstrate the advantages of S-SNAP over a
generalized automatic earthquake monitoring software, Seiscomp3, and a customized real-time
earthquake information system for southern California, TriNet. In comparison, the S-SNAP
catalogue contains five times more events than the Seiscomp3 catalogue and 1.4–2.2 times
as many events per hour as the TriNet catalogue at most times. In addition, S-SNAP is more
likely to solve phase association ambiguities correctly and provide a catalogue with consistent
quality through time. S-SNAP would be beneficial to both routine network operations and the
earthquake review process.
Key words: North America; Seismology; Earthquake source observations.
Some automatic methods focus only on the phase-picking stage
1 I N T RO D U C T I O N
of the earthquake location workflow. The simple short-term aver-
Earthquake detection and location are crucial to detailed under- age versus long-term average (STA/LTA) algorithm (Allen 1978,
standing of seismogenesis. Nowadays, seismic data volumes are in- 1982) is still popular today, while more advanced methods include
creasing rapidly, enabling ever smaller earthquakes to be detected, use of the kurtosis function (Baillard et al. 2014) and the neural
but causing difficulties in manually analysing these events. This network (Dokht et al. 2019; Zhu & Beroza 2019). However, when
issue becomes especially problematic during major earthquake se- seismic signals from different sources are clustered in time, these
quences when hundreds or thousands of events cluster in space and methods alone do not always correctly associate phases with events.
time. Thus, many seismic monitoring agencies are introducing au- Therefore, phase association methods are proposed with or with-
tomatic earthquake detection and location algorithms to aid analysts out an end-to-end workflow, such as a supervised machine learning
in their routine operations. method (Reynen & Audet 2017), the phase combination forward
search method (Tamaribuchi 2018), the PhaseLink method (Ross
C Crown copyright 2020. This article contains public sector information licensed under the Open Government Licence v3.0 (http:
//www.nationalarchives.gov.uk/doc/open-government-licence/version/3/).
15111512 F. Tan et al.
et al. 2019b) and the rapid earthquake association and location their analysis on 2019 July 4, and earlier background or foreshock
method (Zhang et al. 2019). Even with these improvements, mon- seismicity is rarely discussed. In this study, we modify the original
itoring large earthquake sequences in real time is still challenging S-SNAP workflow to study the source area between 2019 June 1
and automatic catalogues are rarely reported as final because of and July 16, helping us to better constrain foreshock activity.
concerns over incompleteness and/or insufficient accuracy. We explain the S-SNAP method in Section 2 and present the
Waveform-based methods usually detect and locate events at the spatiotemporal evolution of the earthquake sequence revealed by
same time, without requiring phases to be identified and associ- its catalogue in Section 3.1. Then, we demonstrate the accu-
ated (e.g. Kao & Shan 2004; Liao et al. 2012; Langet et al. 2014). racy and false detection rate of S-SNAP by comparing our re-
However, large location uncertainties and false detection rates raise sults with reviewed events in the SCSN catalogue and visually
concerns. Template matching (Gibbons & Ringdal 2006) has be- inspecting their discrepancies (Section 3.2). Next, we compare
come a popular automated way of enhancing earthquake catalogue our results with other automatic catalogues, derived by Seiscomp3
completeness. In this approach, waveforms of the best-recorded (https://www.seiscomp3.org) and TriNet (https://www.trinet.org),
earthquakes are used as templates to identify events of poorer qual- to highlight some of the advantages of the S-SNAP approach, in-
ity, usually reducing the magnitude of completeness by at least one. cluding detection ability, recall rate and robustness (Section 3.3).
However, this approach may be unsuitable for real-time earthquake We specifically discuss the phase association ambiguity in the
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monitoring because it requires cross-correlating thousands of tem- TriNet/SCSN catalogue and how S-SNAP can help resolve it (Sec-
plates with continuous data at all times, with the template set ever tion 4.1), followed by temporal resolution (Section 4.2) and loca-
growing. tion uncertainty (Section 4.3) measurements. Finally, we discuss
A recently developed algorithm, Seismicity-Scanning based on the magnitude of completeness of the S-SNAP catalogue and the
Navigated Automatic Phase-picking (S-SNAP), takes advantage of parameter settings when using the S-SNAP method in Section 4.4.
both waveform- and phase-based methods, and can successfully
characterize highly localized (Real-time aftershock by S-SNAP 1513
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Figure 1. The study area (black rectangle) and station coverage (green triangles) of the 2019 Ridgecrest, California, earthquake sequence. The red star
represents the Mw 7.1 main-shock epicentre. Circles show background seismicity (M > 2.5) from 2010 to 2019 from the U.S. Geological Survey (USGS)
catalogue at https://earthquake.usgs.gov/earthquakes/search/. The inset shows the location of the main map within the southwestern United States.
Supporting Information Fig. S1) and the simplification here has phases are not mixed up and most other disturbance signals in the
very minor influence on the final results in our tests with the station seismogram are excluded. If not enough phases are identified, this
distribution in Fig. 1. This approach might be risky when the array is potential event will be removed from the list. The picking technique
much denser, which would require additional experiments or other is simplified from the kurtosis-based phase picker (Baillard et al.
strategies. 2014).
In the second process, we only allow phases to be picked around Because major aftershock sequences are more broadly distributed
the theoretical arrival times calculated in process 1 so that P and S than the induced earthquake cluster studied by Tan et al. (2019b),1514 F. Tan et al.
there is a much higher chance that process 1 can falsely associate of a few to tens of minutes is acceptable for most real-time moni-
phases but still give peaks in both space and time. We make sev- toring applications, with the notable exception of earthquake early
eral changes in process 2 in this version to eliminate as many false warning.
detections as possible. We sort the preliminary events by decreas-
ing brightness values instead of following the chronological order.
Once a phase is associated with a particular event, it is marked 3 D ATA P R O C E S S I N G A N D R E S U LT S
and excluded from being used again in locating events with smaller In this study, we apply the modified S-SNAP to the 2019 Ridgecrest,
brightness. We set a threshold for the number of stations with both California sequence and use our results to demonstrate its perfor-
P and S picks, rather than just the number of total picks, to identify mance. We selected 37 seismograph stations of the SCSN (network
real events. And any potential event with more than half of the picks code: CI), the Plate Boundary Observatory Borehole Seismic Net-
marked as used in any horizontal or vertical component is not clas- work (network code: PB) and the Nevada Seismic Network (network
sified as a real event, even though the number of unmarked phases code: NN), all within 130 km of the Mw 7.1 main-shock epicentre
themselves passes the threshold. For example, a potential event in (Fig. 1), gathering their waveform data from the Incorporated Re-
process 1 leads to 40 P picks in process 2; however, 22 of them search Institutions for Seismology repository from 2019 June 1 to
have been used by previous events with larger brightness values. July 16. The exact data availability varies with time, with around
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Consequently, this potential event will be considered an artefact 36 of the stations recording in any one day. 19 temporary seismic
even though the remaining 18 P phases alone still have the chance stations deployed from 2019 July 7 to 19 (Network code: GS and
to pass the threshold. To identify as many picks as possible, we pick ZY, Cochran et al. 2020) overlap with part of our study period, but
phases in two different frequency bands (5–20 and 1–5 Hz), giving are excluded in order to maintain a consistent of detection thresh-
preference to those detected in the higher frequency band. old. The SCSN Hadley-Kanamori 1-D velocity model (Hutton et al.
In process 3, we use the maximum intersection location method 2010) is used to calculate the traveltimes for S-SNAP. We use the
(Font et al. 2004) to locate the qualified events from process 2. S-SNAP parameter settings suggested by Tan et al. (2019b) except
Considering P arrivals at two stations, all the grid nodes that sat- for the small changes detailed in Supporting Information Table S1.
isfy the traveltime difference to the two stations (within tolerable In this section, we will first describe the main features of the
error) form an equal-differential-time (EDT) layer. The earthquake S-SNAP real-time catalogue (Section 3.1) and then assess its accu-
hypocentre should lie in this layer. As any given phase pair, P–P, racy and false detection rate (Section 3.2). Finally, we compare our
P–S and S–S, can form an EDT layer, the grid node with the most results with other automatic real-time catalogues (Section 3.3).
layer intersections is considered as the preliminary hypocentre. One
advantage of using EDT intersection to locate a hypocentre is that
all outlier picks are automatically excluded in the process and thus 3.1 The S-SNAP real-time catalogue: seismicity in stages
cannot bias the final solution. The ratio between the actual number We started our analysis on 2019 June 1 in order to search for
of intersections and the largest possible number of intersection is early foreshocks, and continued it until 2019 July 16, ∼12 d into
defined as event quality Q. A threshold for Q is applied to further the main sequence. Since no seismicity rate anomaly is observed
eliminate low-quality events and artefacts. The final solution is ob- near the fault zone in June, we focus on the period of July 1–16.
tained by searching the minimum traveltime residual around the S-SNAP detects and locates 19 827 earthquakes (see the Supporting
preliminary hypocentre. Information for the full catalogue), with an average of 1612 events
In this version, we consider all the phase pairs between P and S per day (67 events hr−1 ) after the Mw 6.4 foreshock. Fig. 2 shows
phases to maximize data constraints on the hypocentre, instead of the spatiotemporal distribution of all the earthquakes. Overall, the
using only P–P and S–S pairs as in Tan et al. (2019b). We force focal epicentral distribution clearly illuminates the shorter, NE-trending,
depths to be positive (below ground surface) and apply multiple left-lateral ALFZ and the longer, NW-trending, right-lateral LLFZ.
EDT layers with different thicknesses as described in Theunissen Nearby faults including the Garlock fault were also activated, con-
et al. (2012). sistent with other geodetic and seismological observations (e.g.
In the last process, we calculate the local magnitude ML (Richter Barnhart et al. 2019; Ross et al. 2019a).
1935) using the current SCSN methodology (Uhrhammer et al. Though we acknowledge that a multi-event relocation would help
2011) and station corrections. to further refine these epicentral distributions, our basic catalogue
The processing time depends upon several factors, including the adequately demonstrates the performance of S-SNAP and empha-
size of the study area, number of stations, the scanning grid spacing, sizes some features that may or may not have been highlighted
the computational hardware and the number of events. In our exper- elsewhere. We examine the distribution of seismic events in three
iment, to process a 1-hr-long waveform segment with 37 stations, stages: before the Mw 6.4 large foreshock; between the Mw 6.4 fore-
1 km grid spacing over a 1◦ × 1◦ study area, S-SNAP can finish in shock and the Mw 7.1 main shock; and after the Mw 7.1 main shock.
∼13 min (single processing) on a desktop computer with an Intel While most studies of the Ridgecrest sequence focus on seismic-
i7-8700 CPU and 64 GB memory. This means that for events at the ity on 2019 July 4 onwards (e.g. Ross et al. 2019a; Liu et al. 2020;
beginning of the hour, we can get the solutions by ∼73 min, while Shelly 2020), we observe a cluster of four earthquakes on July 1
the events at the end of the hour will be reported only ∼13 min (with ML 0.3–1.3) along the LLFZ, located between the epicentres
after their occurrence. It is possible to dramatically reduce the delay of the Mw 6.4 foreshock and Mw 7.1 main shock (Fig. 3a), double
time with shorter waveform segments. For examples, a data set con- the number listed in the SCSN catalogue. Given an earthquake rate
sisting of 10 min waveform segments would require only 2–3 min of ∼11 events per month in 2018 around the source zone (from
to process and the entire process can be finished in 1–2 min if the the USGS, at https://earthquake.usgs.gov/earthquakes/search/), we
segments are only 5 min long. It is not advisable to use segments consider these four events to be the first recognizable foreshock
shorter than 2 min due to the trade-off between the size of the mon- activity in the Ridgecrest sequence. To confirm that this seismicity
itoring area and the traveltimes of P and S phases. A delay timeReal-time aftershock by S-SNAP 1515
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Figure 2. Map of all earthquakes detected and located by S-SNAP between 2019 July 1 and 16 across the study area. Circle size and colour indicate event
magnitude and origin time, respectively, with three different colour scales distinguishing the three stages: before the Mw 6.4 foreshock, between the Mw
6.4 and Mw 7.1 earthquakes and after the Mw 7.1 event. 2019 surface ruptures are indicated by continuous black lines (Brandenberg et al. 2019) and other
active faults by dashed lines (USGS and California Geological Survey, Quaternary fault and fold database for the United States, accessed 2019 August 21, at:
https://www.usgs.gov/natural-hazards/earthquake-hazards/faults).
rate change is not due to detection threshold differences between for more than 5 hr (Fig. 3c). Almost all the events within the ALFZ
the USGS and S-SNAP catalogues, we apply S-SNAP to the entire fall on the northwest side of the surface ruptures, implying that this
month of 2019 June. We find only 10 earthquakes—lower than the fault dips towards the NW. In contrast, most aftershocks within the
average monthly rate of 2018—with no more than one event on LLFZ occur on the northeast side of the longest continuous surface
any given day. The foreshock activity intensifies after 15:42:48 on trace, an area that contains many smaller rupture segments. Both
July 4, with 20 events of ML 0.01–4.3 occurring in the 111 min be- geodetic slip models (e.g. Barnhart et al. 2019) and moment tensor
fore the Mw 6.4 foreshock (Fig. 3a). These foreshocks are clustered solutions (e.g. from the California Integrated Seismic Network and
within 3 km of the Mw 6.4 foreshock epicentre. the USGS) indicate that the main-shock LLFZ rupture is subvertical.
Early (1516 F. Tan et al.
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Figure 3. The spatiotemporal evolution of the 2019 July Ridgecrest, California, earthquake sequence. (a) Foreshocks before the Mw 6.4 earthquake. (b–f)
Earthquakes between the Mw 6.4 event and the Mw 7.1 main shock. The epicentres and magnitudes of the two largest events are taken from the SCSN catalogue
for better accuracy. (g–i) Aftershocks within 48 hr after the Mw 7.1 main shock. Circle size and colour indicate event magnitude and origin time, respectively.
2019 surface ruptures are shown by continuous black lines (Brandenberg et al. 2019).
During the interval between the Mw 6.4 foreshock and the Mw 7.1 consistent with mapped surface ruptures. The seismicity is dens-
main shock, we observe seismicity migrating northwestwards along est near the northwestern end of the LLFZ and terminates sharply
the LLFZ (Figs 3b versus f). However, between 5 and 10 hr after the against the Garlock fault at the southeastern end. Seismicity within
Mw 6.4 earthquake, a small cluster of events occurred beyond the the ALFZ abruptly diminished after the Mw 7.1 main shock and
northwestern extent of the earlier LLFZ seismicity, leaving a ∼4 km maintained a low rate throughout the remainder of the study period.
gap (Fig. 3d). 20 hr after the Mw 6.4 earthquake, a cluster of events A northern cluster of aftershocks in the Coso area started ∼5 hr
occurred ∼2 km northeast of the gap, including an ML 4.3 event after the main shock (Figs 3g–i). Triggered seismicity on the Gar-
(Fig. 3e). The gap was not filled until the Mw 7.1 main shock, ∼34 hr lock fault gradually intensified within the first few days after the
after the Mw 6.4 earthquake. The main-shock epicentre coincides main shock (Figs 3h and i). Our catalogue also records triggered
with the gap area (Fig. 3f). These features of our results are also seismicity within the northern Searles valley (Figs 3h and i).
documented in the SCSN catalogue and by other studies that focus
on earthquake relocations (e.g. Ross et al. 2019a; Liu et al. 2020;
Lomax 2020; Shelly 2020). 3.2 Assessment of accuracy and false detection rate
Aftershocks of the Mw 7.1 main shock parallel the main rup-
ture segment of the LLFZ over a distance of ∼60 km (Figs 3g–i), Earthquakes in the S-SNAP catalogue clearly delineate the conju-
gate ALFZ and LLFZ and form tight clusters elsewhere, suggestingReal-time aftershock by S-SNAP 1517
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Figure 4. Hypocentral difference of matched event pairs between SCSN reviewed events and S-SNAP events. Red, grey and black lines show histograms of
difference (the S-SNAP solution minus the SCSN solution) in longitude, latitude and depth, respectively. Positive hypocentral distances therefore mean that
the S-SNAP hypocentre lies to the east, north and below the SCSN hypocentre.
high accuracy. In this section, we first use reviewed events in
the SCSN catalogue as references to assess the accuracy of the
S-SNAP catalogue. Secondly, we investigate the false detection
rate of S-SNAP by visually inspecting differences between the two
catalogues.
We downloaded the SCSN catalogue on 2020 May 24 (SCEDC
2013). It contains 17 981 events in our study area over the interval
of 2019 July 1–16. However, only 5958 of these events had been
reviewed nearly a year after the main shock. These reviewed events
span a wide magnitude range, making them a good test of the
overall accuracy of the S-SNAP catalogue. On the other hand, we
are unable to calculate the recall rate based on these events because
they cannot be considered as a complete catalogue. We will measure
the recall rate later when comparing different automatic methods
(Section 3.3).
The criterion that we use to recognize the same event in both
catalogues (a matched pair) is that the origin time difference must
be less than 2 s. Among the 5958 reviewed events in the SCSN
catalogue and the 19 827 events in the S-SNAP catalogue, we find
3331 matched pairs. Most matched pairs have epicentral differences
of1518 F. Tan et al.
in this interval, the SCSN may have simply detected others events checked the SCSN catalogue for the interval 2019 July 1–16 (down-
with the same origin times, potentially causing the large epicentral loaded on 2020 May 24) and this peak still exists, which may in-
differences. Alternatively, large uncertainties can occasionally oc- troduce bias to statistical analyses and earthquake catalogue-based
cur in both the SCSN and S-SNAP catalogues. We visually inspect physical models. In contrast, the seismicity rate in the S-SNAP
the remaining 12 unmatched events, confirming that 11 of them catalogue decreases gradually after the main shock, in agreement
are real and correctly located and that one is an artefact caused by with Seiscomp3 and other studies, suggesting a more consistent and
incorrect phase association. Therefore, 99.7 per cent (322/323) of reliable quality.
the events in the S-SNAP catalogue during these 4 hr are real. The The recall rate of an automatic method is important. However,
distribution of these events (including the artefact) is shown in Sup- due to the significant delay of the review process, we lack a com-
porting Information Fig. S2. We expect the false detection rate to plete reviewed catalogue that captures the whole study period. In
be even lower during other, less busy hours since phase association the SCSN catalogue downloaded on 2020 May 24, hours 88–118
is most challenging when seismic signals from unknown sources and 124–127 have significantly more events than the TriNet cata-
arrive closely spaced in time. logue and all of these events are marked as reviewed. Therefore,
we assume that during these intervals the SCSN catalogue is com-
plete, and we use this for our recall calculation. There are 3915
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events in the SCSN catalogue during the 35 fully reviewed hours,
corresponding to 112 events hr−1 on average. The S-SNAP, TriNet
and Seiscomp3 catalogues recall 2155, 1661 and 378 events during
3.3 Comparison with other automatic solutions
these intervals, yielding recall rates of 55, 42 and 10 per cent, re-
To demonstrate the detection sensitivity of the S-SNAP approach, spectively. The selected time periods include the busiest hours after
we compare our results to those obtained with a generalized au- the Mw 6.4 and the Mw 7.1 events, which are especially challenging
tomatic earthquake monitoring software, Seiscomp3, and a cus- for phase association and event detection; we can therefore expect
tomized real-time earthquake information system for southern Cal- S-SNAP recall rates to be greater than 55 per cent at quieter times.
ifornia, TriNet. To obtain the Seiscomp3 catalogue, we use its In summary, we observe clear improvements in recall rate with
base open-source version and input the same waveforms used by S-SNAP compared to other real-time automatic methods, but S-
S-SNAP. After running tests using different parameters, we use the SNAP cannot completely replace the human review process.
final settings described in the Supporting Information. TriNet is used
by SCSN in routine operations (https://www.scsn.org/index.php/sei
smologists-tools/eqprocessing/scsn-catalog-status/index.html) and
4 DISCUSSION
the automatic catalogue is available upon request.
Fig. 6 shows the seismicity rate (the number of earthquakes per
4.1 Resolving ambiguity in phase association
hour) within the study area from July 1 to 16 as derived by S-SNAP,
TriNet and Seiscmop3. Seiscomp3 detects 3021 earthquakes dur- Aftershocks can occur very closely in time and space, or even
ing this interval with only six events before the Mw 6.4 earthquake concurrently, making correct phase association challenging. Most
in hour 89 (compared with 44 events in the S-SNAP catalogue). automatic approaches take only traveltimes (phase arrivals) as in-
Seiscomp3 therefore misses almost all the foreshock activity pre- put and rely upon their chronological order for phase association.
ceding the Mw 6.4 earthquake. Two peaks arise in the Seiscomp3 As a result, ambiguities arise when different phase combinations
catalogue with the occurrence of the Mw 6.4 and 7.1 earthquakes, can satisfy different hypocentres. This often occurs in aftershock
after which the seismicity rate gradually decreases. Seiscomp3 has sequences when phases of clustered events arrive within seconds
∼14–20 events hr−1 during the peak hours andReal-time aftershock by S-SNAP 1519
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Figure 6. Seismicity rate comparison among different catalogues. Black, red and blue lines show the number of earthquakes per hour in the S-SNAP, TriNet
and Seiscomp3 catalogues, respectively. The first hour corresponds to July 1 00:00:00 to 01:00:00; the Mw 6.4 and Mw 7.1 earthquakes occurred in hours 89
and 123, respectively.
Figure 7. An example of a phase association ambiguity. Grey seismograms are arranged according to the distance from the S-SNAP epicentre with black
characters showing corresponding predicted arrival times. Red and blue characters represent the predicted arrival times of the two events in the TriNet catalogue.
The rectangle shows the phase shared by the two events in the TriNet catalogue.1520 F. Tan et al.
second event (phase data downloaded on 2020 February 29 at and 1.5 at 10:37:09.0 and 10:37:12.0 on 2019 July 14 are success-
https://earthquake.usgs.gov/earthquakes/search/). In one extreme fully distinguished and correctly located. The two earthquakes are
case, one phase is assigned to two events (i.e. the rectangle in 34 km apart and at this distance the 3.0 s separation time is suffi-
Fig. 7). cient. In another example, two earthquakes with ML 1.1 and 1.3 at
The S-SNAP phase association becomes convincing when the 18:23:02.3 and 18:23:04.7 on 2019 July 11 are both detected even
seismograms are arranged properly with increasing epicentral dis- though the origin times are only 2.4 s apart. This is well expected
tance. The corresponding brightness map (Fig. 8) clearly shows the since the epicentral distance of the two events is 61 km.
stacked array energy concentrating in a single location, which leads In the second experiment, we explore how magnitude differences
to the correct phase association. By falsely associating the phases, influence the temporal resolution. We find three nearly co-located
TriNet mislocates the event by ∼15 km and creates a false event events with ML 1.2 (10:10:05 on July 14), ML 2.2 (18:58:59 on
∼35 km away. July 7) and ML 3.2 (20:41:31 on July 6) and concatenate their wave-
Assigning one phase to multiple events is not unusual in the forms with different separation times. When the ML 1.2 event is
TriNet catalogue. We further investigate two time periods (hour 02 followed by the ML 2.2 event, a ≥25 s separation time is needed for
on 2019 July 11 and hour 11 on 2019 July 12) and find at least eight S-SNAP to distinguish the two. Otherwise, the smaller event will be
cases in which some phases are reused in two near-simultaneous buried and only the larger event can be detected. When the ML 1.2
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events (phase data downloaded on 2020 February 29 at https://ea event is followed by the ML 3.2 event, the minimum separation time
rthquake.usgs.gov/earthquakes/search/). Even when phases are not increases to 33 s. In these cases, the temporal resolution is more
reused, their associations can still be incorrect and we suspect that strongly related to the size of the study area than the magnitude dif-
many scattered events in the TriNet catalogue are artefacts caused ference itself. For a larger study area, there is more space in which
in this manner (Fig. 9). These questionable automatic solutions will amplitudes from the larger event are stacked, adding to brightness
stay in the SCSN/USGS catalogue until being reviewed by analysts. maps up to tens of seconds before and after its origin time. Conse-
However, with the help of brightness maps, the S-SNAP catalogue quently, the concentrated energy from the smaller earthquake within
has significantly fewer scattered events (Fig. 9). The review process this time period will no longer be a global maximum, and when
could also benefit from these advantages by integrating S-SNAP S-SNAP searches for the global maximum in the brightness maps,
into routine operations. the smaller event is easily overlooked.
To summarize, given a densely clustered earthquake sequence,
S-SNAP will try to maintain correct locations and deny unsolved
events, rather than report artefacts or mislocate events. S-SNAP
shows acceptable temporal resolution and robustness as a real-
4.2 Temporal resolution and limitations
time automatic earthquake detection and location method. How-
While there is no required minimum separation time for events in ever, given the limitations, the current version of S-SNAP cannot
S-SNAP, when they are too close in time, S-SNAP may miss one entirely replace the human review process, but might complement
or both of them. In order to quantify the temporal resolution in our or enhance it.
application to the 2019 Ridgecrest sequence, we conduct synthetic
tests with earthquake samples within the sequence.
In the first experiment, we explore the minimum time separa-
4.3 Uncertainty measurements
tion that S-SNAP needs to fully distinguish two co-located events
of same magnitude. We select eight earthquakes (ML 1.1–1.7) in Though S-SNAP calculates average traveltime residuals for individ-
different parts of LLFZ and ALFZ, on the Garlock fault, in the ual earthquakes, its relationship with the hypocentral uncertainty is
Coso area and in the Searles Valley as representatives (Supporting not straightforward. We estimate the hypocentral uncertainty in the
Information Fig. S3). For each event, the seismograms are cut and S-SNAP catalogue using a bootstrapping approach (Efron 1979;
then duplicated several seconds later to create the synthetic wave- Tichelaar & Ruff 1989; Tan et al. 2019a). For this assessment,
forms. The event details and results for each test are available in we take 3 hr from different stages of the sequence (hour 04 on
Supporting Information Table S2. Generally, when the origin times July 5, hour 07 on July 6 and hour 13 on July 12) containing a total
are separated by ≥8–9 s, two events are both identified and correctly of 207 earthquakes. Then, we run the first process (source scanning)
located. When the separation time is ∼5–7 s, S-SNAP misses the in S-SNAP with all N stations. For the remaining processes, we ran-
first event and correctly locates the second one. In contrast, when domly select 0.9 × N stations (repetition allowed) and repeat the
the separation time is ≤3 s, S-SNAP only detects the first one. In- phase-picking and location processes 100 times. The station num-
terestingly, when the separation time is ∼4 s, it denies both events ber threshold for detection is also decreased by 10 per cent (from
in some areas. This is because the program coincidentally identifies 10 to 9). After 100 loops, we would have at most 100 solutions for
comparable numbers of phases from the two earthquakes and tries any earthquake (in some subdata sets, stations with no picks are
to associate them together as a single event. In this case, around half selected and even repeated, so that the event has insufficient picks
of the phases are eliminated as outliers, which significantly lowers to pass the threshold). The distance between any new epicentre and
the quality Q of the event (see Section 2). When Q is below a thresh- the original epicentre is considered random error. We use the small-
old value, the event is denied and both events are missed. In these est range that covers 90 per cent of the error values to represent the
very rare circumstances, S-SNAP tends toward denying low-quality epicentral uncertainty. We also estimate depth uncertainties using
events rather than producing artefacts. the same measurements.
From the experiment above, we estimate a temporal resolution Generally, we find that the epicentral uncertainty increases with
of ∼8–9 s for the most challenging situation of two co-located the average traveltime residual (Supporting Information Fig. S4).
events. However, the temporal resolution for earthquakes of the The mean and median traveltime residuals for all events are both
same magnitude will generally be finer than 9 s, since most events 0.57 s, while the mean and median epicentral uncertainties are 2.9
are separated in space. For example, two earthquakes with ML 1.3 and 2.6 km, respectively. The mean depth uncertainty is 5.3 km,Real-time aftershock by S-SNAP 1521
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Figure 8. The brightness (stacked array energy) map of the event at 02:55:34 on 2019 July 11. The green dot represents the epicentral location in the S-SNAP
catalogue while the red dots represent the two events in the TriNet catalogue. Seismograms are normalized against noise level (by dividing by the median)
before stacking, such that the brightness values are unitless.
Figure 9. Map view of all located earthquakes in the S-SNAP catalogue (left) and TriNet catalogue (right) between July 1 and 16 in the study area shown in
Fig. 1. Circle size and colour of the dots indicates event magnitude and origin time, respectively. Historical faults and 2019 ruptures are the same as in Fig. 2.1522 F. Tan et al.
larger than the epicentral uncertainty and less related to traveltime locates 19 827 events from July 1 to 16 that reveal important features
residuals. These results are in agreement with the accuracy test in including (1) early foreshock activities on July 1, (2) immediate af-
Section 3.2. tershocks on conjugate faults after the Mw 6.4 major foreshock, (3)
a seismicity gap before the Mw 7.1 main shock around its epicentre
and (4) triggered earthquake clusters outside these fault zones. We
4.4 Magnitude of completeness and parameter settings estimate that 99.7 per cent of the S-SNAP automatic catalogue are
real events, and their location accuracy is sufficient to distinguish
We calculate the magnitude of completeness (Mc ) of the S-SNAP
seismicity on a secondary fault within the LLFZ only ∼ 3 km from
catalogue and SCSN catalogue (downloaded on 2020 May 24) us-
the main fault. Compared to Seiscomp3 and TriNet, S-SNAP is
ing the maximum curvature method with a 0.2 magnitude correction
able to detect more events especially in the first few hours after a
(Wiemer & Wyss 2000; Woessner & Wiemer 2005). From 2019 July
large earthquake, retain a consistent high quality through time and
1 to 16, the S-SNAP catalogue has an Mc of 0.9, while the SCSN
is more likely to provide correct phase associations. The current
has an Mc of 1.4 (Supporting Information Figs S5a and d). If we
version of S-SNAP has the potential to become a powerful tool
divide the study period into the interval between the Mw 6.4 and
in routine operations, complementing the earthquake review pro-
Mw 7.1 earthquakes and the interval after the Mw 7.1 earthquake, the
cess and supporting detailed seismological and other geophysical
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S-SNAP catalogue maintains the same Mc of 0.9 (Supporting Infor-
studies.
mation Figs S5b and c), but demonstrates an increase in the b value
(Gutenberg & Richter 1944) from 0.64 to 0.71. The SCSN catalogue
has a smaller Mc of 1.3 before the main shock and returns a very
similar value of 1.4 afterwards (Supporting Information Figs S5e AC K N OW L E D G E M E N T S
and f). To be clear, the Mc of 0.9 in the S-SNAP catalogue does not
We thank the two anonymous reviewers and the editor Margarita
mean that the S-SNAP catalogue has all the M ≥ 0.9 events. Both
Segou for their constructive suggestions to improve this paper. We
catalogues could miss events with magnitudes above the Mc . The
thank Ellen Yu (SCSN/SCEDC products manager) for providing
missing rate in a (partial) manual catalogue can vary due to differ-
the TriNet catalogue, current SCSN station correction values for
ences in effort and skill level of analysts. In contrast, the constant
magnitude calculation and other important information about
value of Mc in the S-SNAP catalogue suggests a consistent quality
the SCSN. Topography data were downloaded from GMTSAR
and objectivity throughout time, which is one of the advantages of
(Sandwell et al. 2011). The ObsPy package (Krischer et al.
a completely automatic catalogue.
2015) and Matplotlib (Hunter 2007) were used in the study. The
Nevertheless, the S-SNAP catalogue still depends on choices of
modified S-SNAP code (Python 3) is available on Github at
parameters. For example, the event number is strongly related to
https://github.com/tanfengzhou/S-SNAP1.1.git. This research was
both thresholds for the number of picks and the quality parameter
supported by the Natural Sciences and Engineering Research Coun-
Q. Lower thresholds will lead to many more events but also more
cil of Canada (NSERC) Discovery grants RGPIN/418268-2013
false detections. However, varying these parameters will have a
(HK) and 2017 04029 (EN), the Energy Innovation Program of Nat-
global effect on the catalogue rather than causing quality variations
ural Resources Canada (HK) and a Canada Research Chair Program
through time. From our experience, only minor changes to the values
(EN). This paper is NRCan contribution number 20200360.
of a small group of controlling parameters will be required (based
on Supporting Information Table S1) when applying S-SNAP to
other earthquake sequences.
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Recorder, 45(2). scanning, phase picking, maximum intersection location and mag-
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July 2019 Ridgecrest, California, earthquake sequence, Bull. seism. Soc. is the quality threshold for events.
Am., 110, 1688–1700.
Figure S2. Events in the S-SNAP catalogue from 04:00:00 to
Reynen, A. & Audet, P., 2017. Supervised machine learning on a network
scale: application to seismic event classification and detection, Geophys.
08:00:00, 2019 July 6. Black dots represent real events while the
J. Int., 210(3), 1394–1409. red dot represents the only artefact within the 4 hr time period. Cir-
Richter, C.F., 1935. An instrumental earthquake magnitude scale, Bull. cle size indicates event magnitude. Traces of surface ruptures and
seism. Soc. Am., 25(1), 1–32. background faults are the same as in Fig. 2.
Ross, Z.E., et al., 2019a. Hierarchical interlocked orthogonal faulting in the Figure S3. Map of selected event locations in the synthetic test (red
2019 Ridgecrest earthquake sequence, Science, 366(6463), 346–351. dots).
Ross, Z.E., Yue, Y., Meier, M.-A., Hauksson, E. & Heaton, T.H., 2019b. Figure S4. Relationship between the epicentral uncertainty derived
PhaseLink: a deep learning approach to seismic phase association, J. from bootstrapping test (Y-axis) and average traveltime residual
geophys. Res., 124(1), 856–869. (X-axis).
Sandwell, D., Mellors, R., Tong, X., Wei, M. & Wesse, P., 2011. Open radar
Figure S5. Magnitude–frequency relationship for the S-SNAP cat-
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AGU, 92(28):234–235.
alogue and SCSN catalogue in different periods of time. (a–c)
SCEDC, 2013, Southern California Earthquake Data Center. Cal- Magnitude–frequency relationship for the S-SNAP catalogue from
tech.Dataset.,1524 F. Tan et al.
2019 July 1 to 16, between Mw 6.4 and Mw 7.1 earthquakes, and af- Table S2. Results for the synthetic test of co-located events with
ter the Mw 7.1 main shock, respectively. (d–f) Magnitude–frequency the same magnitude.
relationship for the SCSN catalogue from 2019 July 1 to 16, between Please note: Oxford University Press is not responsible for the con-
Mw 6.4 and Mw 7.1 earthquakes, and after the Mw 7.1 main shock, tent or functionality of any supporting materials supplied by the
respectively. authors. Any queries (other than missing material) should be di-
Table S1. Summary of parameters used in the S-SNAP processing. rected to the corresponding author for the paper.
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