A new seismogeodetic approach applied to GPS and accelerometer observations of the 2012 Brawley...

A new seismogeodetic approach applied to GPSand accelerometer observations of the 2012Brawley seismic swarm: Implications forearthquake early warning

Jianghui Geng, Yehuda Bock, Diego Melgar, Brendan W. Crowell, and Jennifer S. HaaseCecil H. and Ida M. Green Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography,University of California, San Diego, La Jolla, California, USA ([email protected])

[1] The 26 August 2012 Brawley seismic swarm of hundreds of events ranging from M1.4 toM5.5 in the Salton Trough, California provides a unique data set to investigate a newseismogeodetic approach that combines Global Positioning System (GPS) and accelerometerobservations to estimate displacement and velocity waveforms. First in simulated real-time mode,we analyzed 1–5 Hz GPS data collected by 17 stations fully encircling the swarm zone at near-source distances up to about 40 km using precise point positioning with ambiguity resolution (PPP-AR). We used a reference network of North American GPS stations well outside the region ofdeformation to estimate fractional-cycle biases and satellite clock parameters, which were thencombined with ultrarapid orbits from the International GNSS Service to estimate positions duringthe Brawley seismic swarm. Next, we estimated seismogeodetic displacements and velocities fromGPS phase and pseudorange observations and 100–200 Hz accelerations collected at three pairs ofGPS and seismic stations in close proximity using a new tightly coupled Kalman filter approachas an extension of the PPP-AR process. We can clearly discern body waves in the velocitywaveforms, including P-wave arrivals not detectable with the GPS-only approach for earthquakemagnitudes as low as Mw 4.6 and significant static offsets for magnitudes as low as Mw 5.4. Ourstudy shows that GPS networks upgraded with strong motion accelerometers can provide newinformation for improved understanding of the earthquake rupture process and be of critical valuein creating a robust early warning system for any earthquake of societal significance.

Components: 10,952 words, 8 figures, 4 tables.

Keywords: GPS geodesy; seismology; seismogeodesy; tightly coupled Kalman filter; earthquake early warning; precisepoint positioning.

Index Term: 1200 Geodesy and Gravity: Seismic cycle related deformations, Instruments and techniques.

Received 11 February 2013; Revised 8 April 2013; Accepted 10 April 2013; Published 00 Month 2013.

Geng, J., Y. Bock, D. Melgar, B. W. Crowell, and J. S. Haase (2013), A new seismogeodetic approach applied to GPS andaccelerometer observations of the 2012 Brawley seismic swarm: Implications for earthquake early warning, Geochem. Geo-phys. Geosyst., 14, doi:10.1002/ggge.20144.

© 2013. American Geophysical Union. All Rights Reserved. 1

Article

Volume 14, Number 00

0 MONTH 2013

doi: 10.1002/ggge.20144

ISSN: 1525-2027

1. Introduction

[2] Global Positioning System (GPS) networks areable to observe crustal deformation throughout theentire earthquake cycle from slow interseismicslip to strong coseismic motions. For a large seis-mic event, high-rate GPS can provide rapid esti-mates of broadband displacements, includingstatic offsets and dynamic motions of arbitrarilylarge magnitude [e.g., Nikolaidis et al., 2001; Lar-son et al., 2003; Bock et al., 2004; Larson, 2009].High-rate GPS-derived displacements can quicklyestimate earthquake magnitude for tsunami warn-ings [Blewitt et al., 2006], produce centroidmoment tensor solutions [Melgar et al., 2012],model finite fault slip [Crowell et al., 2012; Ohtaet al., 2012; Wright et al., 2012], and track seismicwave fields [Grapenthin and Freymueller, 2011].

[3] It is recognized that high-rate GPS can alsoplay an important role in earthquake early warning(EEW) by providing estimates of permanent dis-placement within minutes of initiation [e.g., Cro-well et al., 2009; Allen et al., 2011]. This isespecially valuable close to the source for large(>M7) events where broadband seismometers clipand accelerometer data cannot be objectively inte-grated to produce reliable displacements in realtime [Boore and Bommer, 2005; Emore et al.,2007; Melgar et al., 2013]. Typical EEW systems[e.g., Gasparini et al., 2007; Allen et al., 2009b]depend on conventional seismic instruments, andemploy P-wave detection to predict the arrival andintensity of destructive S and surface waves [Hea-ton, 1985; Nakamura, 1988; Allen and Kanamori,2003]. However, algorithms only based on seismicdata tend to saturate; it is difficult to distinguishan event of magnitude 7 from a larger magnitudeof 8 or 9 [Wu and Zhao, 2006; Brown et al., 2009,2011]. Although GPS excels in providing criticalestimates of static offsets, GPS-derived dynamicmotions by themselves are not accurate enough toidentify millimeter-level or even smaller ampli-tude P-waves. Furthermore, P-wave arrivals havemost of their energy in the vertical direction, mak-ing it more difficult for GPS because of the signifi-cantly less precise vertical component [e.g., Bocket al., 2000]. To be able to detect P-wave arrivalsand address the problem of magnitude saturation,Bock et al. [2011] applied a multirate Kalman fil-ter [Smyth and Wu, 2006] to combine high-rateGPS (1–50 Hz) displacements and accelerometer(100–200 Hz) data in near real time to estimate3-D seismogeodetic waveforms with millimeter-level or better precision in displacement and 1

mm/s or better in seismic velocity. They demon-strated the capability of measuring P-wave arrivalsfor near-source stations deployed in southern Cali-fornia during the 2010 Mw 7.2 El Mayor-Cucapahearthquake in northern Baja California. Thus, seis-mogeodesy improves on both seismic-only andGPS-only methods, by providing the full spectrumof seismic motions from the detection of P-wavearrivals to the estimation of static displacements.

[4] Because of the relatively small magnitudes ofthe earthquakes and the excellent distribution ofnearby GPS stations, the 26 August 2012 Brawleyswarm provides a unique data set to examine thelower bound on the sensitivity of seismic wave-forms estimated from high-rate GPS with andwithout strong-motion accelerometer data.Several hundred events were recorded by theCalifornia Integrated Seismic Network (http://www.cisn.org/). The swarm started at about 15:30UTC with six events of M< 2.0 and three M2.5events occurring within a few minutes. The largestevents and the focus of this study (Table 1)occurred at 19:20.04.5 UTC (Mw¼ 4.6) (‘‘Event1’’), 19:31:22.9 (Mw¼ 5.4) followed immediatelyat 19:33:00.8 (Mw¼ 4.9) (‘‘Event 2––doublet’’),20:57:58.2 (Mw¼ 5.5) (‘‘Event 3’’), and23:33:25.1 (Mw¼ 4.6) (‘‘Event 4’’) (USGS/NEICPDE-Q database, htp://earthquake.usgs.gov/earth-quakes/eqarchives). The earthquakes occurred ona northeast striking fault zone located about 6 kmnorth of the northwest end of the Imperial fault[http://www.scsn.org/2012Brawley.html], an areathat has a history of seismic swarms including oneearthquake with a maximum magnitude of M5.1 in2005 at Obsidian Buttes [Lohman and McGuire,2007], and another in June 2008 in the same gen-eral location as the 2012 event. Chen and Shearer[2011] summarized the history of swarm data inthe Imperial Valley and characterized the migra-tion patterns and earthquake mechanisms since1981. Initial 3-D earthquake relocations andmoment tensor inversions [Hauksson et al., 2012]using the double-difference method [Haukssonand Shearer, 2005] indicated a left-lateral strike-slip motion on near vertical fault planes for Events

Table 1. Location and Magnitude of the Events Analyzeda

Event Mw Latitude Longitude Depth (km) Time (UTC)

1 4.6 33.0380 �115.5553 5 19:20:04.52—doublet 5.4 33.0193 �115.5632 5 19:31:22.9

4.9 33.0210 �115.5540 14.5 19:33:00.83 5.5 33.0243 �115.5495 9 20:57:58.24 4.6 33.0390 �115.5260 11 23:33:25.1

aSource is the SCEC database (http://www.data.scec.org).

GENG ET AL: SEISMOGEODETIC EARTHQUAKE EARLY WARNING 10.1002/ggge.20144

2

http://www.cisn.org/

http://www.cisn.org/

htp://earthquake.usgs.gov/earthquakes/eqarchives

htp://earthquake.usgs.gov/earthquakes/eqarchives

http://www.scsn.org/2012Brawley.html

http://www.data.scec.org

1–3, with a normal faulting component for Event2, and predominantly normal motion for Event 4(Figure 1).

[5] We present a novel seismogeodetic analysismethod to analyze the earthquake swarm, based onprecise point positioning with ambiguity resolu-tion (PPP-AR) [Ge et al., 2008; Geng et al.,2012], supplemented by accelerometer data atlocations where GPS receivers and strong motionaccelerometers are in close proximity. Precisepoint positioning methods are attractive becausecurrent relative network positioning approachesbecome more cumbersome as the number of GPSstations to be processed increases from hundredsto thousands at active plate boundaries. The seis-mogeodetic approach applies a tightly coupledKalman filter to GPS and accelerometer data at theobservation level as an extension of the GPS-only

PPP-AR approach to estimate displacement andvelocity waveforms. This one-step approach dif-fers from the two-step approach presented by Bocket al. [2011] to apply a multirate Kalman filter topreviously estimated GPS displacements and rawaccelerometer data.

[6] We discuss the applicability of seismogeodeticPPP-AR to EEW systems, both in the determina-tion of static offsets and the detection of P-wavearrivals, in light of our analysis of the 2012 Braw-ley seismic swarm data.

2. Theory

[7] In this section we describe the technical detailsof the seismogeodetic PPP-AR approach and sum-marize its advantages compared to current GPS-only and loosely coupled seismogeodetic methods.

Figure 1. Brawley seismic swarm (red dots) surrounded by real-time continuous GPS stations (blue dia-monds) and available continuous strong motion stations (open yellow circles) for the period of the swarm.The focal mechanisms are those computed by the SCEC for the four events considered in this study (Table 1).Coseismic displacements and 95% confidence ellipses are from the 24 h SOPAC/JPL combination.


3

2.1. Ambiguity Resolution for a SingleGPS Station

[8] The two main approaches to GPS analysis canbe classified as relative network positioning [e.g.,Dong and Bock, 1989; Blewitt, 1989] and precisepoint positioning [Zumberge et al., 1997]. In eithercase, observations to GPS satellites from a groundreceiver consist of phase and pseudorange meas-urements at two radio frequencies (‘‘L1’’ at1575.42 MHz and ‘‘L2’’ at 1227.60 MHz). Thephase measurements have integer-cycle phaseambiguities, which are the total number of cyclesfrom the satellite to the receiver. It is critical forprecise real-time positioning applications to beable to resolve the phase ambiguities [e.g., Bocket al., 2000].

[9] Baseline vectors between stations in a networkare estimated as part of relative network position-ing. Common-mode errors due to clock and hard-ware biases completely cancel in doublydifferenced (between satellites and between sta-tions) phase observations, thereby revealing the in-teger nature of the phase ambiguities. Likewise,common-mode atmospheric errors due to tropo-spheric and ionospheric refraction are reduced asthe distances between stations shorten. Absoluteposition estimates are then derived by fixing (ortightly constraining) the true-of-date coordinatesof one or more reference stations within the net-work to precise a priori values with respect to aglobal terrestrial reference frame.

[10] Ambiguity resolution for a single GPS stationis difficult because undifferenced phase ambiguityestimates contain noninteger biases, which originatein receiver and satellite hardware. To recover the in-teger properties of undifferenced ambiguities in PPPanalysis, the fractional cycle parts of the nonintegerbiases can be estimated using a network of referencestations [e.g., Ge et al. 2008; Geng et al., 2012] out-side the region of active deformation. Then the frac-tional-cycle bias (FCB) estimates allow clients toattempt ambiguity resolution for a single stationusing the PPP-AR method [e.g., Geng et al., 2011].This approach also requires precise satellite clockand orbit information. In the following section, wedescribe a tightly coupled Kalman filter that extendsPPP-AR analysis by adding very high-rate strongmotion accelerometer data.

2.2. Tightly Coupled Seismogeodetic Filter

[11] For seismogeodesy, we have available veryhigh-rate collocated accelerometer data in addition

to high-rate GPS data. Bock et al. [2011] presenteda loosely coupled multirate Kalman filter that opti-mally combines GPS displacement and accelerom-eter data in a two-step process that can beimplemented in real time. In the first step, the GPSphase and pseudorange data are analyzed to esti-mate station displacements––this can be doneusing either relative network positioning or PPP-AR. In the second step, the GPS displacements arecombined with the accelerometer data. There is nofeedback between the two steps so we call this aloosely coupled Kalman filter. Here we present atightly coupled Kalman filter that operates on theraw GPS and accelerometer data in a single step.This formulation is applicable to the estimation ofFCBs as part of the PPP analysis of the referenceGPS network outside of the zone of deformation,as well as for individual PPP-AR clients within theseismically active region.

[12] Without loss of generality, we assume that theinteger-cycle ‘‘wide-lane’’ (the difference betweenL1 and L2) ambiguities have been resolvedthrough a linear combination of phase and pseu-dorange measurements [e.g., Teunissen and Kleus-berg, 1998]. Since the wide-lane wavelength isabout 86.2 cm compared to the 19 and 24 cmwavelengths of the L1 and L2 phases, respectively,this is straightforward even for reference networksof global extent. The wide-lane ambiguities arethen applied to the analysis of ionosphere-free car-rier phase observations (which have nonintegerambiguities), leaving integer-cycle ‘‘narrow-lane’’ambiguities with a wavelength of 10.7 cm[e.g., Dong and Bock, 1989]. Then, the narrow-lane carrier-phase measurement for reference sta-tion i (i¼ 1, . . . ,r) to satellite j (j¼ 1, . . . ,s) isgiven by

Lji ¼ �j

i þ cti þMji Ti þ � Nj

i þ Bi � Bj� �

þ "ji; ð1Þ

where �ji denotes the geometric distance between

station i and satellite j ; c is the speed of light invacuum, ti is the receiver clock error, Ti is the ze-nith tropospheric delay and Mj

i is the mappingfunction, � ¼ c= f1 þ f2ð Þ is the narrow-lanewavelength where f1 and f2 are L1 and L2 frequen-cies, respectively, Nj

i denotes the narrow-lane inte-ger ambiguity, Bi and Bjdenote the fractional partof noninteger receiver- and satellite-specific hard-ware biases, respectively, and "j

i denotes the ran-dom error with "j

i � N 0; �2L

� �. Multipath effects,

higher-order ionospheric effects, etc. are ignoredfor brevity. As part of the GPS analysis we obtaina real-valued estimate for Nj

i þ Bi � Bj ; Bi is


4

eliminated by differencing between satellites leav-ing the integer part N j

i and the fractional part Bj tobe estimated. Once the Bj estimates are collectedfrom the r reference stations, we can derive theFCB estimate for satellite j (j¼ 1, . . . ,s) by

Bj ¼

Xr

i¼ 1

Bjji

!=r; ð2Þ

where Bjji is the estimate of Bj at station i.Increasing the number of stations in the refer-ence network will improve the reliability andaccuracy of Bj [Geng et al., 2012]. Becausethe result is averaged over many stations, it isnot necessary for the stations to be the sameas the PPP client stations, only that the analy-sis in the PPP clients be consistent with thelarger reference network.

[13] The FCB products for all satellites Bj

(j¼ 1, . . . ,s) determined in the first step are thendistributed to the individual PPP clients, wheresingle-station ambiguity resolution can beattempted. Similar to equation (1), at a particularclient l (to distinguish from subscript i used forreference stations) at epoch k, we linearize the nar-row-lane carrier-phase observation

�Ljkl ¼ Dj

kl�pkl þ c�tkl þMjkl�Tkl þ � �Nj

l þ Bl � Bj� �

þ "jl;

ð3Þ

and

�pkl ¼ �xkl vkl akl½ �T

Djkl ¼

xkl � xjk

�jkl

0 0

" #;

8><>: ð4Þ

where � denotes the increment of a parameterestimate, �pkl is the state vector comprising theposition increment �xkl, the velocity �vkl and theacceleration �akl, and Dj

kl is the design matrix inwhich xkl and xj

k are the station and satellite posi-tions, respectively, and "j

l � N 0; �2L

� �. The satel-

lite bias Bj is assigned the value Bj, and the

receiver Bl is assimilated into the receiver clockestimate, or equivalently can be removed bybetween-satellite differencing. As a result, the esti-mate for �N j

l þ Bl � Bj is reduced to an estimatefor �Nj

l where the integer property has beenrecovered. Ambiguity resolution for a single sta-tion can then be attempted. Once successfully fix-ing �N j

l to integers for all visible satellites, we

have achieved the PPP-AR solution for an individ-ual station (client).

[14] For a tightly coupled GPS/accelerometer solu-tion, after correcting the raw accelerometer datafor gain, we model the accelerometer data at sta-tion l and epoch k as

Akl ¼ akl þ bkl þ "kl; ð5Þ

where Akl is the corrected accelerometer measure-ment, aklis the true acceleration, bkl is an accelera-tion bias which is estimated as a random walkparameter to accommodate slowly time-varyingchanges, especially during earthquakes when thebias can change significantly, and "kl is the randomerror with "kl � N 0; �2

A

� �. Other errors due to, for

example, instrument tilts are reasonably presumedto be minimal during the 26 August 2012 Brawleyswarm and ignored in this study. Equation (5) iscombined with equation (3) for the measurementupdate. The transition equation for the state vectorin equation (4) takes the form of

xkl

vkl

akl

24

35 ¼ 1 � �2=2

0 1 �0 0 1

24

35 xk�1;l

vk�1;l

ak�1;l

24

35; ð6Þ

where � is the sampling interval of the accelerom-eter data. Equation (6) is used for the Kalman filtertime update.

2.3. Advantages of the Tightly CoupledKalman Filter

[15] The key difference between the tightlycoupled Kalman filter presented here and theloosely coupled filter in Bock et al. [2011] is thatin the tightly coupled case the accelerations areused as additional data to resolve ambiguities. Theaccelerometer data are applied as tight constraintson the position variation between epochs. This sin-gle-step process improves cycle-slip repair forGPS carrier-phase data and rapid ambiguity reso-lution after GPS outages [Grejner-Brzezinskaet al., 1998]. This is confirmed with the Brawleyswarm data as discussed in the supporting infor-mation (section 1 and Figure S1).1

[16] As with the loosely coupled filter the tightlycoupled filter also minimizes step functionspossibly introduced by tilt in the accelerometerobservations. Furthermore, biases, bkl, in the

1Additional supporting information may be found in theonline version of this article.


5

accelerometer data are estimated along with otherparameters of interest. Hence, no pre-event meanneeds to be eliminated from the acceleration databefore starting the Kalman filter. In addition, bkl isestimated at each epoch as a random walk parame-ter to mitigate possible drift in the accelerometerdata due to translations (indistinguishable fromrotations) [Trifunac and Todorovska, 2001] andtemperature changes.

2.4. Advantages of GPS PPP-AR OverRelative Network Positioning

[17] There are several advantages to the PPP-ARapproach. First, it is highly efficient because thesatellite clocks and FCBs are estimated only oncefor positioning any number of clients. The dataprocessing at each client is independent and doesnot affect any other clients. Relative network posi-tioning is complicated by the need to assign base-lines, overlapping Delaunay triangles [Crowellet al., 2009], or overlapping subnetworks [Bocket al., 2011]. This is a critical difference as one isfaced with the challenge of analyzing hundreds tothousands of stations in real time. Furthermore,intermittent station dropouts complicate relativenetwork positioning. Therefore, PPP-AR can beefficiently applied to large GPS networks deployedover a wide area such as around the Circum-Pa-cific Seismic Belt or at isolated stations in remoteareas. Second, the PPP-AR approach compared to

relative network positioning does not require alocal reference station, which might be displacedduring a large event. Instead, PPP-AR requires acontinental- or global-scale reference networkwell outside the zone of expected deformation, yetstill has the same satellites visible as the client sta-tions. Finally, if undifferenced ambiguities can besuccessfully fixed the positioning accuracy is com-parable to that of relative network positioning[e.g., Bertiger et al., 2010; Geng et al., 2010].

3. Data Analysis

3.1. An Operational Real-Time PPP-ARSystem at SOPAC

[18] At the time of the 2012 Brawley swarm andas part of a prototype EEW system for the WesternU.S., we had already implemented at the ScrippsOrbit and Permanent Array Center (SOPAC) anoperational PPP-AR service center for estimatingsatellite clocks and FCBs. These parameters areintended for distribution to PPP clients in real timewhether at a centralized processing facility, at aremote computer with internet access to data froma specific station, at a local processor at the remotestation or within the GPS receiver itself. With pre-dicted ultrarapid satellite orbits from the interna-tional GNSS service (IGS), the SOPAC servicecenter generates satellite clocks every second for

Figure 2. Distribution of high-rate GPS stations used by the SOPAC PPP-AR system. Solid green circlesdenote the 46 stations used for clock estimation whereas solid red triangles denote the 48 stations used forFCB determination. The solid black star shows the location of the 26 August 2012 Brawley seismic swarm.


6

each visible GPS satellite using 46 reference sta-tions, and FCBs every 5 s using 48 stations acrossNorth America (Figure 2—Some of the stationsare overlapping). The 1 Hz reference station dataare collected from IGS and UNAVCO’s PlateBoundary Observatory (UNAVCO/PBO) servers.The reference network for satellite clock estima-tion is chosen to be of continental scale to reduceerrors; another option is to use satellite clocksfrom existing sources based on a global distribu-tion of stations. On the other hand, the FCB net-work is chosen to be as close to the area of interestas possible, while staying sufficiently outside theregion of expected deformation. The reference sta-tions for the SOPAC service are chosen to be fur-ther than 200 km from the primary zones oftectonic deformation in California, Oregon andWashington to avoid contamination of the satelliteclocks and FCBs during a large seismic event inthat region. For the FCB determination, we choosea reference network that is close to the westernU.S. coast (rather than a single reference stationthat is chosen for relative network positioning).Should one of the FCB network stations be subjectto dynamic motions, the impact would be mini-mized by the averaging in equation (2). We notethat for the western U.S. the FCB determination ischallenged by the sparseness of real-time off-shorereference stations in the Pacific. The reference sta-tion positions are fixed to true-of-date estimateswith respect to ITRF2008 [Altamimi et al., 2011]produced through an (1–2 week) extrapolation ofcombined modeled time series based on SOPAC’sand Jet Propulsion Laboratory’s (JPL)’s routineweekly analysis of 24 h, 30 s sampled data from aglobal and regional set of continuous GPS stations(http://sopac.ucsd.edu/processing/coordinates/sec-tor.shtml) and made available through the GPSExplorer data portal (http://geoapp.ucsd.edu/).Real-time SOPAC PPP-AR results for the high-rate station GLRS in southern California (Figure1) from 11 August to 5 September 2012 show thatambiguity resolution in PPP improves the root-mean-square (RMS) difference between the posi-tion estimates and ground truth from 20, 30, and61 mm without ambiguity resolution to 15, 12, and40 mm for the North, East, and Up components,respectively.

3.2. GPS PPP-AR Analysis

[19] For the 26 August 2012 Brawley earthquakeswarm, we used SOPAC’s PPP-AR system to pro-cess high-rate GPS data from 16 PBO stations and1 Southern California Integrated GPS Network

(SCIGN) station in this region (Figure 1). The sta-tions stream 1 Hz GPS data, the normal opera-tional setting for most real-time GPS networks.For this study, we requested after the fact thatUNAVCO/PBO download 5 Hz data from the re-ceiver buffers at the 16 PBO stations. We thenprocessed the 5 Hz data in a simulated real-timemode using SOPAC’s 1 Hz satellite clocks, 5 sFCBs, and the predicted IGS ultrarapid orbits pub-lished for that period. The 1 Hz data at USGS sta-tion BOMG were also processed in this way.Further details on PPP-AR estimation are providedin section 2 of the supporting information.

[20] In practice, the transmission of high-rate GPSdata from the reference network (or a client sta-tion) to server has a typical latency of 0.4–1.0 s.The satellite clocks and FCB parameters are con-tinuously estimated and made immediately avail-able to clients. The PPP-AR processing atindividual stations (clients) can then be performedat each epoch with a delay about 1 s after data ar-rival at the data analysis center.

3.3. Tightly Coupled Kalman FilterAnalysis

[21] There are only a few collocated GPS and seis-mic stations in southern California (Figure 1). Forthis study we identified three suitable collocations.The first pair consists of GPS station P506 sam-pling at 5 Hz and accelerometer site WLA sam-pling at 200 Hz, separated by 2.6 km andapproximately 8 km from the source. This site pairwas the only collocation available within theBrawley Seismic Zone. Emore et al. [2007] dem-onstrated good agreement of 1 Hz GPS andstrong-motion data with instrument separations ofup to 4 km. The second pair is GPS station P494sampling at 5 Hz and strong motion site WESsampling at 100 Hz, separated by about 80 m and34 km from the largest event. The third and mostclosely spaced pair (within 10 m) is BOMG (1 Hz)and strong motion site BOM (200 Hz), about42 km from the swarm events. The strong motionsensors are observatory grade EpiSensor acceler-ometers and the broadband sensors are STS2instruments, both on 24 bit Quanterra data loggers.For each pair, we combined the 1–5 Hz PPP-ARderived displacements with 100–200 Hz accelera-tion measurements. We also considered pair P493/NP.286 (Figure 1) but the strong-motion siteNP.286 is located inside the second story of abuilding and its record is complicated by the build-ing response.


7

http://sopac.ucsd.edu/processing/coordinates/sector.shtml

http://sopac.ucsd.edu/processing/coordinates/sector.shtml

http://geoapp.ucsd.edu/

[22] As a first step the raw accelerometer datawere corrected for gain. In the tightly coupled Kal-man filtering, the precision of the raw accelerome-ter data from site WLA was taken to be 10 mm/s2

for all three channels to account for its relativelylong distance from station P506 (alternatively,we could have applied a time shift to the acceler-ometer data). In contrast, we applied 0.1 mm/s2

for sites WES and BOM, which are closer to theirGPS counterparts. The process noise of the accel-erometer bias parameters is presumed to be 0.001mm/s2.5. As a comparison, in the loosely coupledapproach of Bock et al. [2011] the pre-event meansare removed from the accelerometer data. Then,the two filter parameters for the periods of shak-ing, the system variance q and the measurementvariance r, are determined from 60 s of pre-eventnoise on each channel of the accelerometer andGPS, respectively. Also, a near-real-time smootherwith 5 s lag is applied to the loosely coupled filter.

4. Results

4.1. PPP-AR Analysis Using Only GPSData

[23] We evaluated the PPP-AR approach, firstusing only GPS data in the context of real-timeanalysis for EEW. The results are of interest in thecase that only GPS data are available in real time

near the source of a large earthquake. Further-more, the GPS-only approach can be used forrapid and reliable estimates of earthquake magni-tude, fault geometry and rupture characteristics forlarge events often faster than traditional seismicmethods on their own [e.g., Melgar et al., 2012;Crowell et al., 2012; Ohta et al., 2012; Wrightet al., 2012].

[24] The RMS of the differences between the 1 or5 Hz GPS-derived ambiguity-fixed position esti-mates and the true-of-date SOPAC/JPL positionsare 31, 14, and 32 mm on average while the stand-ard deviations (1�) are 9, 6, and 29 mm in theNorth, East and Up directions, respectively, basedon 5 h of data for all sites before the start of Braw-ley swarm activity. In contrast, the RMS for theambiguity-float positions are 29, 20, and 57 mm,and the standard deviations (1�) are 9, 17, and 50mm, respectively. This is consistent with earlierresults that indicate that ambiguity resolutionimproves the positioning accuracy of real-timePPP, especially in East and Up components [e.g.,Geng et al., 2011]. In addition, for the 5 h dataspan, we compared PPP-AR to instantaneous rela-tive positioning [Bock et al., 2000, 2011] (Table2). We were able to assign for the latter a referencestation just outside the zone of deformationbecause the earthquake magnitudes were so small.We can see that PPP-AR outperforms instantane-ous relative positioning by 31%, 60%, and 48% inthe standard deviations for the North, East and Upcomponents, respectively. This improvement isattributed to the forward (smoothing) Kalman filterused in PPP-AR, whereas instantaneous relativepositioning processes each epoch of data inde-pendently [Bock et al., 2000], and to improved sin-gle-epoch ambiguity resolution in the PPP-ARprocess. Other relative positioning methods thatuse Kalman filter estimation should provide thesame level of precision as PPP-AR [e.g., Bertigeret al., 2010; Geng et al., 2010], but are more com-plicated to apply in real-time scenarios. In the re-mainder of this paper, we take the individualstandard deviation (1�) from the real-time PPP-AR processing as the precision of the GPS positiontime series (Table 2).

[25] We show the displacement waveforms for the17 GPS stations during all four events in the sup-porting information (Figures S2–S5). From thehorizontal components of the three stations P506,P499, and P495 that are nearest to the epicenter(<8 km) for the largest event (Event 3, Mw 5.5;Figure S4 in supporting information), earthquake-

Table 2. Standard Deviation (1�, mm) of the DifferencesBetween the 1–5 Hz GPS-Derived Ambiguity Fixed PPP andInstantaneous Position Estimates, and the True-of-DateSOPAC/JPL Positions Over 5 h Preceding the BrawleySwarm

PPP-ARInstantaneous RelativeNetwork Positioning

Stations North East Up North East Up

P506 8 7 40 13 16 54P499 8 6 35 13 15 53P495 8 8 34 13 15 53P502 9 6 25 13 15 54P498 9 5 22 12 15 58P503 9 6 21 13 14 53P507 9 7 37 15 16 51P497 7 5 24 12 15 56P501 10 6 29 13 14 56P744 6 4 30 12 14 58P510 11 8 33 14 17 53P493 7 4 19 12 14 48P508 9 7 25 14 16 56P509 9 7 30 13 17 57P496 9 5 24 13 16 65P494 8 5 25 13 15 65BOMG 11 10 41 N/AMean 9 6 29 13 15 56


8

induced displacements can be clearly perceived ataround 20:58:05 UTC, with a peak-to-peak ampli-tude of up to 100 mm, followed by about 20 s ofshaking of up to 50 mm amplitude. For more dis-tant stations, the peak-to-peak amplitudes arereduced gradually down to about 20 mm in East atstation P744, which is over three times its standarddeviation (see Table 2). For stations farther than30 km from the epicenter, seismic signatures canhardly be discerned in either North or East compo-nents. For Event 2 with two subevents of Mw 5.4and Mw 4.9 (Figure S2 in supporting information),seismic displacements can also be distinguished atstations up to 20 km away from the epicenters,although the peak-to-peak amplitudes are below30 mm.

[26] For Event 1 and 4 with magnitudes of Mw 4.6,we cannot find any significant coseismic displace-ments (Figures S2 and S5 in supporting informa-tion). As expected, no significant coseismicdisplacements are observed in the vertical compo-nent for any events in the sequence, even at thestations closest to the epicenters. The 24 hSOPAC/JPL analysis show significant coseismicoffsets at 11 of the 17 stations (Table 3). Maxi-mum horizontal magnitudes are not greater than25 mm for all events, except for station P499 witha magnitude of 49 mm. For P499, the estimatedcoseismic displacement based on all 4 events is 30mm (Table 3), or only about 60% of the estimatesfor the 24 h solutions. This might be due to post-seismic deformation present in the 24 h solutions,but most likely is due to the precision of theepoch-wise GPS estimates. We can conclude thatreal-time GPS-only results alone, although quiteimpressive compared to postprocessed analysis of

longer spans of data, are not precise enough toreliably estimate the small coseismic offsets forthe Brawley swarm data set, except for the twonear-field stations (P499 and P506), and are cer-tainly not precise enough to detect P-wavearrivals.

[27] Some systematic GPS errors, such as multi-path effects and orbit/clock errors might easilyresult in a noise level of up to 20 mm in the real-time horizontal displacements. To ascertainwhether or not multipath is a major contributor,we processed the GPS data for 25 August andshifted the resultant time series by 246 s to takeinto account the sidereal time shift [e.g., Genrichand Bock, 1992]; the GPS satellites have approxi-mately 12-hour periods so that the geometric rela-tion of the satellite pass to the location of localreflectors is repeated every 24 hours. The resultsare shown in Figure 3 for the largest event (Event3, Mw 5.5) for five representative stations, the threeclosest ones (P506, P499, and P498) and two atdistances of about 20 km (P497 and P507). Theresults for the other three (smaller) events areshown in Figures S6–S8 in the supporting infor-mation. We find that the time series for 25 and 26August do not resemble each other, which makesit unlikely that multipath is contributing to theerror. Satellite orbit/clock errors will affect allsites uniformly within a regional area, but we donot see any common characteristics across thetime series from 15 to 30 s. Moreover, for stationsP506, P499, P498, and P497 their peak displace-ments from 10 to 30 s are significant at the 95%confidence level since they exceed twice the GPSstandard deviations. Also significant is the factthat stations P506 and P499 clearly manifest staticoffsets, with the P499 East component showingmore than 20 mm of static offset after the earth-quake. For Event 2 in Figure S7 in the supportinginformation, we also see no significant correlationbetween the time series for 25–26 August thatwould be indicative of repeating multipath noise.The peak displacements at stations P506, P499,and P498, which are nearer to the epicenter, aresignificant at the 95% confidence level. We cansurmise that the noise level is primarily due to theinherent precision of real-time GPS displacements[e.g., Langbein and Bock, 2004; Genrich andBock, 2006], rather than to any unmodeled system-atic effects.

[28] Our results are consistent with previousreports of PPP-AR precision in postprocessingmode [e.g., Bertiger et al., 2010] and single-epoch,real-time, relative network positioning [e.g.,

Table 3. Total Coseismic Offsets in mm From the SOPAC/JPL 24 h Solutions From the Day Preceding the Four Eventsto the Day Aftera

Site East North Up

CRRS �8.6 6 0.3 4.5 6 0.4 �0.1 6 0.4GLRS �4.6 6 0.3 �2.6 60.4 3.0 6 0.4P495 �27.3 6 0.3 7.7 6 0.4 1.7 6 0.4P497 �1.2 6 0.4 8.4 6 0.4 �0.0 6 0.4P498 1.2 6 0.4 20.7 6 0.4 �3.6 6 0.5P499 45.6 6 0.4 �18.1 6 0.4 9.5 6 0.5

28.1 6 3.5b �10.8 6 2.7b 23.5 6 6.2b

P501 2.6 6 0.4 �0.4 6 0.5 �1.5 6 0.5P502 17.2 6 0.4 �3.7 6 0.4 2.0 6 0.5P503 �7.5 6 0.4 0.9 6 0.4 1.7 6 0.5P506/WLA �8.2 6 0.4 �26.4 6 0.4 �6.5 6 0.6

�3.8 6 2.4c �13.4 6 1.9c 11.6 6 5.4c

P509 1.7 6 0.3 �0.2 6 0.4 �0.7 6 0.4

aUncertainties are one sigma values.bCoseismic offsets from GPS-only solutions for all four events.cCoseismic offsets from seismogeodetic solution of event 3.


9

Langbein and Bock, 2004; Genrich and Bock,2006]. Thus, PPP-AR can be applied in real timeat about the same level of precision as relative net-work positioning. Although we cannot gauge thevertical sensitivity in these examples, earlier stud-ies indicate that single-epoch horizontal compo-nents are more sensitive by as much as a factor of5–10 than the vertical components [e.g., Bock

et al., 2000]. Also as expected from earlier studies,GPS-only displacements are sensitive enough todetect coseismic displacements for earthquakes ofabout Mw 5.5 and greater with near-field (�10 km)observations. Some improvements in sensitivitycould be obtained using sidereal filtering [e.g.,Choi et al., 2004]. However, we have not testedthis because it is more cumbersome in real-time

Figure 3. GPS-derived horizontal displacements of five stations for Event 3 after 20:57:50 UTC on 26 Au-gust 2012 (black traces), compared to those (blue traces) for the same period on the previous day (25 August).All blue traces are offset by 4 cm for clarity. Dashed red lines show the ranges of twice the noise level (2�)from the value prior to the earthquake. Vertical red lines mark the origin time of earthquake. Top-left cornersof left plots show the names and epicentral distances of the five stations.


10

scenarios and may not be that effective since wesee no interference due to multipath in these exam-ples, consistent with the analysis of Genrich andBock [2006].

4.2. Seismogeodetic PPP-AR

[29] Here, we apply the tightly coupled Kalmanfilter (equations (3)–(6) to the 1–5 Hz GPS dataand 100–200 Hz accelerations to estimate 100–200 Hz seismogeodetic accelerations, velocitiesand displacements of the four events for the threecollocated GPS/accelerometer pairs, P506/WLA,P494/WES, and BOMG/BOM. The 108 wave-forms are shown in Figure 4 over an interval of200 s, spanning the entire period of seismic shak-ing. The enlarged view of the first 10 s of the seis-mogeodetic velocity in Figure 5 for stationBOMG/BOM shows that the P-wave can bepicked very accurately from the vertical compo-nent, and the S-wave from the horizontal compo-nents, since they are well aligned with SouthernCalifornia Earthquake Center (SCEC) P-wave and(where-available) S-wave picks. This is true for allfour events, including the Mw 4.6 event at 42 km dis-tance. This is primarily due to the high sensitivity ofthe strong motion accelerometers compared to theGPS. The GPS-only velocities, differentiated fromthe GPS-only displacements are too noisy to detectthe P-wave for this size event. Genrich and Bock[2006] examined GPS-only displacement waveformsduring periods of seismic quiescence and concludedthat high-frequency noise (greater than 0.5–0.3 Hz)of single-epoch GPS positions is dominated by sour-ces with a white noise spectrum. This is consistentwith our seismogeodetic results from the Brawleyswarm data during shaking. In this case, the higherfrequency components of the GPS/accelerometercombination are much more precise than theGPS-only displacements, which is a consequence ofthe greater sensitivity of the accelerometer data atthe higher frequencies and to the Kalman filter whichreduces systematic effects due to the accelerometers.

[30] As seen in Figure 6 for the Mw 5.4 event atstation P506, and for the Mw 5.5 event (notshown), the seismogeodetic displacements in thefirst 10 s contain a clear view of the progression ofthe long period near-source dynamic and staticground displacements, which has not previouslybeen available for earthquake studies. This hasbeen shown in previous studies for much largerearthquakes with collocated GPS and seismicinstruments [Bock et al., 2011; Crowell et al.,2012; Melgar et al., 2013], but this is the first timeit has been recovered for these size events. Signifi-

cant low-frequency displacements occur betweenP- and S-wave arrivals, which may be due toinstrument response due to tilt when using acceler-ometers alone rather than to real seismic deforma-tion. Even with a robust method of baselinecorrection [Boore and Bommer, 2005], integratedstrong motion records can have significantly dif-ferent displacements between the P- and S-wavearrivals as well as different final offsets from theGPS time series, as shown by Emore et al. [2007;Figure 6] and Melgar et al. [2013]. In real-timeoperations when the size of the event is not known,it is critical to have a consistent technique thatrecovers displacements reliably prior to S-waveshaking for both small (Figure 6) and large events.This sensitivity is important in the seismogeodeticvelocities for the purposes of EEW based on, forexample, the predominant period of the P-wave,Pd [Crowell, 2013].

[31] The displacements for the Brawley swarmearthquakes are relatively small, and except forthe near-field stations the permanent coseismicoffsets are barely detectable with the GPS-onlysolutions. However, the seismogeodetic displace-ments with significantly reduced noise show con-siderable more detail, in particular in the verticalcomponent compared to the GPS-only displace-ments seen in Figure 4. For combination P506/WLA, the seismogeodetic displacements are nois-ier than those for sites P494/WES and BOMG/BOM, because of the greater distance (betweenP506 and WLA), which is why we applied a rela-tively low precision to the accelerometer data atWLA.

[32] Although the seismogeodetic displacementsseen in Figure 4 have reduced high-frequencynoise, they exhibit some undesirable long periodvariations as the intensity of shaking diminishes.This is primarily due to GPS systematic errors(multipath). This illustrates the threshold of sensi-tivity and that the permanent displacements aresmall relative to the signal amplitude. The impor-tant point is that the seismogeodetic displacementsare accurate over the duration of dynamic motion,here for the first 20 s or so, which include P-waveand S-wave arrivals. This information is importantin developing scaling relationships to estimateearthquake magnitude from the first few secondsof shaking, in the context of EEW [Crowell,2013]. The sawtooth pattern seen in Figure 6,might be due to a possible misorientation of theaccelerometer data [e.g., Rios and White, 2002;Emore et al., 2007] and is an artifact of using onlya forward filter. It could also indicate a trade-off in


11

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12

motion relative to the estimated accelerometerbias at very low amplitude. In Bock et al. [2011]we eliminated the sawtooth pattern by 5 s back-ward smoothing. Although it could also be imple-mented in the tightly coupled approach, this is amore complex and time consuming operation andwould not change our basic conclusions.

[33] The initial directions of first-motion at P506/WLA, seen in the displacements in Figure 6,

exhibit a southwest orientation for Events 1–3, butare ambiguous for Event 4, which has predomi-nantly normal motion (Figure 1). The static offsetsfor the first three events are generally in the southto south-west direction, consistent with left-lateralstrike-slip mechanisms on vertical faults (Figure 1,Table 3). The coseismic seismogeodetic displace-ments in the horizontal components of near-faultcombination P506/WLA for the largest event(Event 3–Mw5.5, Figures 4 and 6), constitute a

Figure 5. Seismic velocities at the beginning of seismic shaking for Events 1–4 for combined GPS/acceler-ometer station BOMG/BOM, roughly 42 km northeast of the swarm as a function of origin time. The red ver-tical lines are the SCEC P- and (when available) S-wave picks.


13

large fraction of the total coseismic displacementin the 24 h solutions; a displacement of about 4mm in the East component and about 13–14 mmin the North component, which is in the samedirection and about 50% of the total displacementsestimated from the 24 h SOPAC/JPL solutions(Table 3).

[34] STS2 recordings clip at 13 mm/s, and thevery impulsive S-wave for P506 (Event 3), appa-rent in the seismogeodetic velocity (Figure 6), hasamplitudes in excess of 25 mm/s likely saturatinga broadband recording at 8 km from the source.The seismogeodetic velocity at P506, however,stays on scale. These reliable peak ground veloc-ities in the near field are potentially of value forground motion prediction relations for peakground velocity, once again without relying onunreliable real-time integration of the accelerationrecords.

[35] We compared 60 s of the seismogeodetic ve-locity estimates to those measured by the STS2broadband instruments at WES (Figure 7) andBOM (Figure 8) before they clipped by decon-volving the instrument response from the seis-mometer traces. To stabilize the frequency domaindeconvolution we first applied a 0.01–2 Hz band-pass filter to the data. We then compared the firstfew (unclipped) seconds of the seismometer re-cord, which contain the small amplitude P-waves,

with the velocity output by the seismogeodetic fil-ter. For comparison the seismogeodetic velocitiesderived from both tightly coupled and looselycoupled filters were low pass filtered at 2 Hz. Nohigh-pass filtering was necessary as was requiredto stabilize the deconvolution in the seismometer ;the low-pass filtering of the Kalman filtered datawas performed just to establish an objectivecomparison.

[36] After removing pre-event mean from allrecords, we can clearly see that the estimated seis-mic velocities are in excellent agreement with theobserved broadband velocities at frequencies from0.01 to 2 Hz with long-wavelength biases of up to1 mm/s for some events (e.g., Event 2 at P494/WES and Event 4 at BOMG/BOM). The agree-ment with Event 3 (the largest event— Mw 5.5) isquite striking, and is consistent with that seen inFigure 6 of Bock et al. [2011] for the P494/WEScollocation for the much larger magnitude (Mw

7.2) 2011 El Mayor-Cucapah earthquake. Table 4shows the RMS of the differences between theKalman filter recovered velocities and the meas-ured velocities by the two STS2 seismometers forthe entire 60 s interval. The RMS values rangefrom 0.1 to 1.6 mm/s. Despite the presence of longperiod noise, we can infer that the continuous esti-mates of seismic velocities for earthquakes withmagnitudes of Mw 4.6 or greater are, for the

Figure 6. The displacements and velocities at the beginning of seismic shaking for Event 3 and the com-bined GPS/accelerometer pair P506/WLA. The red vertical lines are the SCEC P- and S-wave picks.


14

Figure 8. Comparison in the vertical component of 60 s velocity waveforms measured by STS2 seismome-ter and Kalman filter output of GPS displacements and accelerometer data from BOMG/BOM. Note thatEvent 4 is preceded by a smaller seismic event, apparently unrelated to the Brawley seismic swarm so that thefirst seconds are obscured by the coda of the smaller seismogram (see also Figure 4). The event times arelisted in Table 1.

Figure 7. Comparison of the vertical component of 60 s seismic velocity waveforms measured by the STS2seismometer and loosely coupled and tightly coupled Kalman filter output from P494/WES. Note that onlythe first (Mw¼ 5.4) earthquake is shown for Event 2. Event times are given in Table 1.


15

purpose of estimating near-source ground motions,in agreement with those measured by much moreprecise broadband instruments in all three coordi-nate components.

[37] Figures 7 and 8 also compare the performanceof tightly coupled and loosely coupled filters inrecovering the seismic velocity waveforms. Over-all, for all events, both filters perform well in cap-turing the features in the frequency band 0.01–2Hz. However, compared to the measured veloc-ities, the velocity estimates recovered by theloosely coupled filter have relatively larger long-wavelength biases than those by the tightlycoupled filter. Specifically, in Table 4, all RMSvalues derived from the tightly coupled filter,except for Event 3 at BOMG/BOM, are smallerthan those from the loosely coupled filter by up to0.2 mm/s. The long-wavelength biases may berelated to the time-varying biases in the acceler-ometer data (perhaps due to temperature-depend-ent drift), which are estimated in the tightlycoupled filter, but ignored in the loosely coupledfilter.

[38] We note that Event 4 at BOMG/BOM (Figure4) is preceded by what appears to be local noisenear the station as there is no record of an earlierevent in the CISN earthquake catalog. This noiseobscures the P-wave arrival at this station.

5. Applicability to EEW

[39] We have demonstrated that broadband dis-placement waveforms estimated from a network ofcollocated GPS and strong motion instruments candetect P-wave arrivals near the source for earth-quakes as small as Mw 4.6 and at distances up to42 km. Therefore, seismogeodesy not only pro-vides the permanent deformation but also has the

potential to provide a significant improvement forEEW methods that are based on the maximum am-plitude of the first few seconds of the P-wave (Pd)or the predominant period (�max

p or �c). This thenallows prediction of the time of arrival and inten-sity of S-waves based on data from a single station[Heaton, 1985; Allen and Kanamori, 2003; Kana-mori, 2005; Böse et al., 2012]. The problem withsystems based solely on seismic instrumentation ismagnitude saturation for earthquakes greater thanabout M 7 [Wu and Zhao, 2006; Brown et al.,2009, 2011]. Seismogeodetic time series havebeen shown to be useful for large events [Bocket al., 2011; Crowell et al., 2012; Melgar et al.,2013]. This study of the 2012 Brawley seismicswarm demonstrates the lower magnitude boundfor the usefulness of the seismogeodetictechnique.

[40] Seismogeodetic waveforms are also importantas new input for better understanding of earth-quake physics by providing an unprecedentedview of the progression from the dynamic grounddisplacements to the final static offsets. Traditionalseismic methods rely primarily on broadband seis-mic and strong motion observations with their in-herent weaknesses during large earthquakes. Forexample, the existence of an earthquake nucleationphase has been hypothesized [Ellsworth and Ber-oza, 1995] and there is ongoing controversywhether earthquakes are deterministic or not [e.g.,Olsen and Allen, 2005; Rydelek and Horiuchi,2006; Olsen and Allen, 2006]. Scaling relation-ships based on seismogeodetic displacements dur-ing the first few seconds of P-wave arrival formedium to great earthquakes appear to lend cre-dence to the hypothesis that rapid estimation ofearthquake magnitude can be estimated before thetermination of seismic rupture [Crowell, 2013]. Ifso, that would have critical implications for EEWsystems as well as better understanding the physicsof seismic rupture.

[41] Broadband displacements from the GPS/ac-celerometer combination provide two advantages:(1) the ability to detect P-waves and subsequent Sand surface waves on-scale in the near-sourceregion, and (2) a precise measurement of long pe-riod and static offsets that accompany medium orgreater earthquakes. Generally, only the latteradvantage has been stressed in the literature [e.g.,Allen and Ziv, 2011]. We can conclude that anEEW system based solely on regional GPS net-works upgraded with strong motion accelerome-ters can effectively provide independent alerts for

Table 4. RMS of the Differences Between the Kalman FilterRecovered Seismic Velocities and STS2 Velocity Measurementsat Stations P494/WES and BOMG/BOM for the Four LargestEvents During the 26 August 2012 Brawley Swarm.a

P494/WES (mm/s) BOMG/BOM (mm/s)

EventsTightlyCoupled

LooselyCoupled

TightlyCoupled

LooselyCoupled

1 0.10 0.18 0.09 0.292 (first event) 1.37 1.58 0.14 0.333 0.91 0.96 0.44 0.274 0.23 0.26 0.47 0.65

aBoth tightly coupled and loosely coupled filters are presented.Pre-event biases have been removed.


16

any conceivable earthquake large enough torequire early warning, which would improve therobustness of alerts provided by a seismicnetwork.

[42] A further benefit of our approach is related tothe issue of false alarms (predicting a false eventas well as neglecting to predict a significantevent), which is a recognized problem for bothseismic-only and geodetic-only methods, in partic-ular when the station spacing in a network issparse. False triggers in the geodetic-only time se-ries can be minimized using the seismogeodeticapproach described in this paper. An EEW alarmwould not be issued if there is no significantchange in the estimated seismic velocities (i.e., nosignificant seismic shaking), which are continu-ously estimated without clipping, as is the case forstrong motion networks. However, the seismogeo-detic data could independently confirm or deny thedetection of a large event by a seismic network,depending on the retrieved coseismic offsets.

[43] We have shown in this paper that PPP-AR isan efficient and viable method to analyze high-rateGPS data compared to relative network position-ing methods, and with the same precision but with-out the requirement to choose a reference site. Ifthe client processing is implemented at the site, itenables additional advantages for EEW based onobservations at single stations with on-site proc-essing capabilities [e.g., Kanamori, 2005].

[44] There are currently hundreds of real-timeGPS stations in the Western U.S., only a fractionof which are collocated with seismic instruments.One approach to achieving a seismogeodetic capa-bility is to upgrade the existing GPS stations withlow-cost MEMS accelerometers, which have beenshown by extensive large outdoor shake table teststo be adequate for this purpose [e.g., Bock et al.,2011]. Other shake table tests have shown that cer-tain low-cost MEMS accelerometers are sensitiveenough to detect a M> 3 earthquake at near-fielddistances (10 km) and M> 5 at regional distances(100 km) (Robert Clayton, personal communica-tion). We can infer that these types of accelerome-ters directly collocated at the GPS stations wouldhave been adequate to monitor the larger events ofthe 2012 Brawley swarms.

6. Conclusions

[45] We have established that single-epoch GPS-only PPP-AR is a highly efficient and viable

method to analyze high-rate GPS data, with thesame precision as relative network positioningmethods but without the requirement to choose alocal reference station. Rather, positions are inde-pendently estimated with respect to an externalreference frame realized through the true-of-dateglobal coordinates of the reference network sta-tions and estimated satellite orbits and clocks.FCBs estimated from the reference network allowfor ambiguity resolution at each station within thezone of deformation. This methodology is particu-larly useful for continuous GPS networks of hun-dreds to thousands of stations deployed withinzones of tectonic deformation and seismic riskbecause the computation time scales linearly asthe number of stations increases. However, likerelative network positioning, it is limited in preci-sion to about 20 mm for detection of real-timecoseismic displacements and is unable to detect P-wave arrivals.

[46] We have shown the lower bound of sensitivityfor GPS-only and combined GPS/accelerometerseismogeodetic waveforms using data collectedfrom four near-source events of the 26 August2012 Brawley seismic swarm, ranging in magni-tude from Mw 4.6 to 5.5. During periods of seismicshaking the tightly coupled seismogeodetic PPP-AR analysis provides continuous unclipped esti-mates of broadband displacements in all threecomponents that clearly discern the arrival of P-waves. Seismogeodetic velocities are estimatedthroughout the period of seismic shaking with aprecision of about 0.1–1 mm/s. Together, seismo-geodetic broadband displacements and velocitiesfrom near-source stations can form the basis forEEW systems for any magnitude earthquake ofconsequence, which is more robust against falsealarms than seismic-only or GPS-only approaches.Furthermore, seismogeodesy can be used to betterunderstand the physics of the earthquake ruptureprocess.

Acknowledgments

[47] We thank the two reviewers and the Associate Editor forproviding useful comments. Melinda Squibb and Anne Sulli-van handled the real-time GPS data. Peng Fang was responsi-ble for the 24 h GPS analysis at SOPAC, and Angelyn Moorefor the analysis at JPL. GPS 5 Hz data for the 2012 Brawleyswarm were provided by the Plate Boundary Observatory(PBO) operated by UNAVCO for EarthScope (http://www.earthscope.org) and supported by NSF grant EAR-0323309. Real-time 1 Hz GPS data over North America werecollected from the International GNSS Service and PBO.


17

http://www.earthscope.org

http://www.earthscope.org

Accelerometer data were obtained from the California Inte-grated Seismic Network, and University of California SantaBarbara (courtesy of Jamie Steidl). Seismic event informationis from the Southern California Earthquake Center (SCEC)data archive. This paper was funded by NASA grants AIST-11 NNX09AI67G, ROSES NNX12AK24G, 06-MEaSUREs-06-0085, and SCEC award 12083.

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