Proceedings of the International Symposium on Engineering Lessons Learned fromthe 2011 Great East Japan Earthquake, March 1-4, 2012, Tokyo, JapanREPLAY OF THE 2011 MW 9.0 GREAT EAST JAPAN
EARTHQUAKE IN SIMULATED REAL-TIME MODE
WITH INTEGRATED 1 HZ GPS AND 100 HZ
ACCELEROMETER DATA FOR RAPID SOURCE
CHARACTERIZATION, EARTHQUAKE RESPONSE
SPECTRA AND TSUNAMI PREDICTION
Yehuda BOCK1 , Diego MELGAR1, Brendan W. CROWELL1, Dominga SANCHEZ2 ,and José RESTREPO2
1

Research Geodesist, Institute of Geophysics and Planetary Physics, Scripps Institution of
Oceanography, La Jolla, CA, USA, ybock@ucsd.edu
2Professor, Department of Structural Engineering, University of California San Diego
La Jolla, CA, USA, jrestrepo@ucsd.edu

ABSTRACT: We replay real-time data from 190 1 Hz GEONET GPS and 100 Hz NIED
accelerometer stations that are within 1-2 km of each other in the region of grounddeformation during the 2011 Mw 9.0 Great East Japan earthquake. Using an on-the-fly
Kalman filter/smoother, we estimate three-dimensional 100 Hz broadband (static anddynamic) displacements with mm precision. Using the displacements, we derive a finite
fault slip model and earthquake response spectra, which are achievable within 3-4
minutes of earthquake initiation. We discuss implications for rapid tsunami warning.
Key Words: Great East Japan earthquake, GPS/seismic integration, earthquake
characterization, response spectra, tsunami warning

INTRODUCTION
Responses to recent great earthquakes and ensuing tsunamis in Sumatra, Chile and Japan with theresulting loss of life and damage to infrastructure demonstrate that our ability to evaluate themagnitude and source of catastrophic earthquakes and their tsunamigenic potential in the first seconds
to minutes after the initiation of rupture, even with modern seismic instrumentation, is problematic.
High-gain, broadband seismometers, which easily record long-period motion required for rapid
modeling, clip when shaking is too intense, i.e. when located close to the source of large earthquakes.
Secondly, low-gain, strong motion accelerometers, which are specifically designed to be capable ofrecording intense ground motions, cannot record long-period data. During strong shaking
accelerometers are rotated in addition to being translated and not capable of discerning between thesetwo different kinds of motions (Graizer, 1979; Iwan et al., 1985); hence, rotations are recorded astranslations. These spurious translations produce what is commonly known as baseline offsets (Booreet al., 2002), which make it problematic to extract all of the long-period information in the seismic580

record. Post-processing schemes exist that can extract long-period information from strong motion
data (Boore and Bommer; 2005); however, these correction schemes are subjective and multiple
correction schemes yield seemingly plausible waveforms without an objective criterion to decide
which one is the correct one, certainly not in real time (Boore et al., 2002). Most importantly, the static(permanent) deformation is lost.
Earthquake early warning (EEW) systems, first developed in Japan, have now been implemented
or are under development in several countries (e.g., Gasparini et al., 2007, Allen et al., 2009a,b; Astiz,2009). However, they currently rely exclusively on seismic data and methods (including in Japan) withthe above mentioned limitations. By being insensitive to finite fault slip, seismic-based EEW systems
are unable to distinguish between a magnitude 6.5 event from a much larger event as was evident
during the 2011 Mw 9.0 Great East Japan earthquake. A good summary of the existing EEWalgorithms and their limitations are given by Allen et al. (2009b). The ability of P-wave-based
methods to rapidly and accurately detect large earthquakes (M > 7) is controversial (Allen et al.,2009b) and this is where broadband displacements from GPS or GPS/seismic integration as shown inthis paper can play a major role. We have shown for the 2003 Mw 8.3 Tokachi-oki earthquake in Japanand the 2010 Mw 7.2 El Mayor-Cucapah earthquake in northern Baja California, Mexico that a fast
centroid moment tensor (CMT) solution (Melgar et al., 2011) and a full finite fault slip model(Crowell et al., 2012) can be estimated within 2-3 minutes of those events using near-source GPS
networks. In addition, scaling relationships derived from displacement measurements from several
large earthquakes with near-field GPS/seismic network monitoring can be applied directly to EEW
algorithms (Crowell et al., 2009).
Structural monitoring is also currently limited to seismic instrumentation, in particular
accelerometers, and suffers the same limitations. Real time monitoring of buildings and infrastructure
is contemplated more and more as a desirable asset by the engineering community (Celebi et al., 2004;
Masri et al. 2004; Ko and Ni, 2005) for a number of reasons. The benefits of full-bandwidth
displacement waveforms during strong shaking as well as during quiescent periods are obvious.
Real-time monitoring serves an academic purpose in that it permits comparison between the actual andtheoretical responses of a structure and it allows engineers to explain and model the observed damage
product of a particular excitation. It also serves a very practical purpose in that it permits rapid
assessment of building conditions following an event and allows inspectors to rely on data and not justvisual inspections to tag a building green, yellow or red. This directly impacts the rapidity andaccuracy of inspections. Additionally, real-time monitoring serves engineering purposes on multiple
time scales. Immediately after an event it helps to assess the state of a structure, but in the mid tolong-term sense it allows structural engineers to assess performance-based design and analysis
procedures, and perform state-of-health monitoring and long-term damage detection.
Earthquake early warning, source imaging and structural monitoring, hence, are limited in termsof the size of the earthquake that can be evaluated in real time. Another example is regional waveform
matching techniques that invert for the moment tensor (MT, Dreger and Helmberger, 1990; Dreger2003). One notable exception is the W-phase algorithm. This represented an important advance incomputing centroid moment tensors (CMTs) as quickly as possible for large events. The W phasealgorithm was developed by Kanamori and Rivera (2008), who elaborated on Kanamori’s (1993)
observation of the W phase, a long-period phase arriving in between the direct P and S waves. They
showed that inversion for the MT using data as close as 15° from the source is viable. W phase inversion
algorithms currently run in real time at the USGS, Pacific Tsunami Warning Center (PTWC) and Institut
du Physique du Globe de Strasbourg (IPGP-EOST, Hayes et al. 2009). Since the W phase arrives wellbefore large amplitude surface waves and remains on-scale far longer such inversion algorithms haveshown to be a marked improvement in rapid computation of CMT solutions for large events overtraditional waveform matching techniques. W phase-based inversion schemes, while very robust,require long-period displacement records, e.g. 200–1000 s for the 2011 Great East Japan event (Duputel
et al. 2011). These are almost always unusable in real time close to the source for the reasons discussed
above; velocity instruments clip and it is difficult to extract long period motions from strong-motion
accelerometer data because of tilts and rotations of the instruments (Boore and Bommer, 2005).
For the Great East Japan event it took 20 min after origin time to arrive at the first CMT solutions by581

agencies running W phase algorithms (Duputel et al. 2011), even though the rupture had a duration of 2
minutes (Simons et al. 2011). This delay was due to the reliance on teleseismic data. After several
iterations using progressively more data, the final CMT solution was obtained by the National
Earthquake Information Centre (NEIC) 90 minutes after origin time using data up to 90° from therupture. The first estimate of moment magnitude was obtained in about 3 min by the Japan
Meteorological Agency (JMA), but was grossly underestimated at Mw = 6.8. Duputel et al. (2011)
documented that in the numerous iterations between agencies the nodal planes were somewhat
consistent with only minor variations in strike dip and rake, the magnitudes oscillated between Mw 8.8
and 9.0 after the 20-minute mark, but the centroid locations varied by as much as 2° and 60 km in depth.
These solutions are robust enough and fast enough for ocean basin-wide tsunami early warning, and in
this regard the W phase algorithm is a resounding success. Numerous tsunami warnings for large
earthquakes (http://ptwc.weather.gov/) issued by the PTWC attest to this. Nonetheless, issuing a robust
early warning in the near source region remains elusive. Only Japan has such a system in place, there isno near-field early warning system anywhere else in the world. However, due to the Great East Japan
earthquake’s magnitude being severely underestimated the initial tsunami warnings issued by JMA werea poor assessment of the actual intensity of the tsunami (Ozaki, 2011) because the fundamental problem
of determining an earthquake’s dimension with local and regional data in real-time remains only
partially solved. In an attempt to extend the W phase algorithm and incorporate local data Rivera et al.
(2011) concluded that data closer than 3° to the source is unusable because the point source assumption
in the W phase algorithm can no longer be used to model the event.
INTEGRATION OF GPS AND SEISMIC DATAJapan has the world’s most advanced earthquake early warning system and the only early tsunami
warning capability in near-source coastal regions. However, the effectiveness of the Japanese responseto the 2011 Mw 9.0 Great East Japan earthquake was certainly diminished by strict reliance on
traditional seismic methods to estimate earthquake magnitude and ground shaking, while failing tobuild into their impressive earthquake early warning system the availability of real-time 1 Hz datafrom the 1200-station GPS network (GEONET – Miyazaki et al., 1998). If real-time displacementsfrom GPS and accelerometer data had been available an accurate estimate of moment magnitude could
have been obtained very soon after the rupture ended, an order of magnitude improvement in latency
compared to traditional seismic methods. This was critical for tsunami warning since the first waves
hit the coastline after only 30 minutes.
Permanent deformation is the best source of long-period information about an earthquake. GPS iswell equipped to measure permanent deformation and thus to characterize an earthquake as first
demonstrated for magnitude estimation of the 1992 Mw 7.3 Landers earthquake in southern California(Blewitt et al. 1993; Bock et al. 1993). There have been some attempts to address the use of GPS static
deformation estimates in rapid source models, notably by Blewitt et al. (2006) who showed that given an
epicentral location and assuming thrust faulting for the 2004 Mw 9.2 Sumatra–Andaman earthquake,one could have estimated an accurate magnitude within 15 min of the origin time using global GPS
stations at regional to teleseismic distances. In Bock et al. (2011), building on numerical simulations by
Smyth and Wu (2006) and applied to bridge monitoring by Kogan et al. (2008), we demonstrated thattraditional seismological time series can be combined with GPS time series to produce data productsof superior precision in real time.
In Figure 1a, we show the entire period of seismic shaking at collocated GPS and accelerometerdata at P494/WES in the vertical component during the 2010 Mw 7.2 El Mayor-Cucapah earthquake
in northern Baja California. This earthquake was recorded by real-time GPS and accelerometer
instruments throughout southern California, which was affected by significant crustal deformation.
The observed broadband velocities clipped even before the arrival of the S-wave at the station, while
the Kalman-filtered estimated velocity waveforms record the entire event at the 100 Hz rate of theaccelerometer. In Figure 1b, we show the first 12 s of the vertical broadband velocity record at WES
after the Southern California Earthquake Center’s P-wave pick and overlay the582Kalman-filter-estimated velocities. That the broadband seismic data and the waveform estimated by
Kalman smoothing are indistinguishable is striking, given also that the GPS resolution is poorest in thevertical direction. In Figure 1c, we compare displacements derived by (single) integration of the 12 s
seismometer record at WES, and the 100 Hz smoothed Kalman filter full spectrum displacements. It isclear that we can detect the small-amplitude P-wave recordings with a precision and accuracy of about
1 mm. This is a significant improvement over the 1 Hz GPS-only solutions where the verticalprecision is on the order of several cm, precluding P-wave detection. Figure 3c also shows the poor
results obtained by on-the-fly double integration of the accelerometer data. We obtained better resultsin post processing with baseline corrections and function fitting procedures (Boore at al., 2002).
Although the dynamic motions could be extracted, the static displacements were lost. With theaddition of GPS displacements, the double integration can be performed on-the-fly using only raw
accelerometer records. The Kalman filtered records capture both the shaking and the permanent
deformation, effectively recording the full bandwidth of seismic motion and allowing for the recordingof near-source deformation during large earthquakes.

Figure 1. Combining GPS and accelerometer data into a single stream creates a low-gain
broadband seismometer that never clips. Adapted from Bock et al. (2011).

BROADBAND DISPLACEMENTS FOR THE GREAT EAST JAPAN EARTHQUAKEFor the Great East Japan earthquake, we analyzed in simulated real-time mode 1 Hz GPS(RINEX) data from over 1000 GEONET stations (Miyzaki et al., 1998) in 30 prefectures on Honshu

583

and Hokkaido islands (Figure 2) using the method of instantaneous positioning (Bock et al., 2000;
Bock et al., 2011). Three-dimensional 1 Hz relative displacements were estimated in triangularsubnetworks derived from a Delaunay triangulation of all stations. We used the International
Reference Frame (ITRF) 2005 coordinates obtained from the ARIA group at Caltech/JPL (Simons etal., 2011) as a priori values for analysis of the triangular subnetworks. Using the coordinates of GPS
station 0848 on the northern tip of Hokkaido island as fixed, we computed three-dimensional
displacements of all other stations using a network adjustment procedure described in Crowell et al.
(2009). We then searched for NIED KiK-net and K-Net accelerometer stations (Aoi et al., 2004) thatwere within 1-2 km of a GEONET GPS station. We identified 190 “collocated” stations in the regionof deformation, all with 100 Hz accelerometer data which had been triggered by the earthquake. The
two data types, 1 Hz GPS displacements and 100 Hz accelerometer data, were analyzed in real-timemode using a Kalman smoother as described in Bock et al. (2011), resulting in 100 Hz displacements
for all 190 collocated stations. As is typical with GPS data analysis, the 1 Hz GPS-only displacements
have a precision of about 1 cm in the horizontal directions and 5 cm in the vertical direction.
Combining the GPS displacements with the much more precise accelerometer data, results in 100 Hz
broadband displacements with a precision of at least 1 mm in all three components (see also Figure 1)
and an accurate representation of the dynamic shaking and the evolution of the permanent deformation
throughout the network.

Figure 2. Coseismic displacements in the epicentral region computed from Japan’s 1200+

station CGPS Network (GEONET) by ARIA group at Caltech/JPL (courtesy of Susan
Owen, JPL). Maximum surface displacement on land was 5.24 meters at station 0550 oncoast.
In Figure 3, we show the 100 Hz displacement record for the instruments (collocated 1 Hz GPS
receiver 0172 and 100 Hz strong-motion accelerometer MYG001) on the coast across from theearthquake’s epicenter (Figure 2) estimated in simulated real-time mode with the Kalman filter584

algorithm of Bock et al. (2011). The 100 Hz displacement records are superimposed on the 1 Hz
GPS-only displacements. Shown are the three coordinate components in the top three panels.
Displacements were largest in the east component. Note that the vertical component of the
GPS/accelerometer combination is about 50 times more precise than the GPS-only record. In thebottom panel we zoom in on the first few seconds of the event in the vertical component, showing that
we can detect the P wave arrival with the GPS/accelerometer record, which is critical for earthquake
early warning since it allows one to predict the arrival and intensity of the destructive S wave. Notethat the vertical axis goes from -3 mm to +3 mm so that the resulting displacements appear to beprecise at the sub-millimeter level. The station dropped by nearly 1.5 m for this event a factor that
contributed to the ensuing tsunami, which arrived within about 30 minutes.

Figure 3. (Top three panels) Three-dimensional displacements for the 2011 Great East
Asia earthquake computed for GPS station 0172 and accelerometer station MYG001.
Overlaid are the 1 Hz GPS-only displacements on top of the 100 Hz GPS/accelerometer
combination. (Bottom Panel) Zoom in on the vertical component of the 100 Hz
GPS/accelerometer combination clearly showing the P-wave arrival and the first 7
seconds of shaking.

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RESPONSE SPECTRA FROM BROADBAND DISPLACEMENTS
Traditionally earthquake response spectra have been computed from measured ground accelerations(e.g., Celebi et al. 2004; Celebi 2006) for two reasons: (1) strong ground motions are recorded only on
acceleration sensors because velocity sensors clip and displacement measurements have been thus far
unavailable, and (2) accelerations are forces per unit mass and thus can be directly related to loadingof a structure and hence are a suitable description of an single degree of freedom (SDF) oscillator.
Using the displacements waveforms estimated at the 190 collocated GPS/accelerometer stations for
the Great East Japan earthquake, we computed response spectra for each station by solving theordinary differential equation for a SDF oscillator using a finite difference scheme for three different
forcing functions: (1) the raw 100 Hz accelerometer data, (2) the raw 1 Hz GPS displacements, and (3)the 100 Hz Kalman-filtered GPS/accelerometer combination.

Figure 4. Response spectra displacements for collocated GPS station 0172 andaccelerometer station MYG001 in north and east directions obtained by solving theordinary differential equation for a SDF oscillator with a finite difference scheme.
Shown are spectra for three forcing functions: (1) the raw 100 Hz accelerometer
data, (2) the raw 1 Hz GPS displacements, and (3) the 100 Hz Kalman-filtered
GPS/accelerometer combination.

586

Figure 4 shows a typical example for the integration of displacement data (Figure 3) from GPS station
0172 and accelerometer station MYG001 (Figure 2). The noise levels in the displacement time series
are significantly reduced with the GPS/accelerometer combination and we capture both
high-frequency shaking as well as the final coseismic (static) offsets. The spectra display someinteresting characteristics. Because of limited sampling frequency, the GPS spectra are limited tofrequencies smaller than 0.5 Hz. Additionally, we routinely observe that in the frequency range
between 0.1 and 0.5 Hz the GPS data lead to a small over-estimation of the spectral displacement
when compared to the accelerometer spectra, which may be explained by aliasing of signals outside
the Nyquist frequency. Conversely, at lower frequencies the accelerometer spectra are significantly
larger (1 m in the east component and 2.5 m in the north component) than the ones estimated from theGPS data alone. As we expect, the GPS spectra seem to better capture the spectral response at theselong periods while the accelerometer spectra do a better job at the high frequencies. However, thespectral estimates from the GPS/accelerometer combination coincide with the GPS spectra at low
frequencies and with the accelerometer spectra at high frequencies. Thus, with a single data productone can obtain the full spectrum of information.
As Celebi and Sanli (2002) point out, in addition to monitoring free-field displacements, thecapacity to directly measure displacements on structures directly impacts real-time monitoring
capacity. For example, the analysis techniques discussed for the 1994 Mw 6.7 Northridge earthquake
by Darragh et al. (1994) involve bandpass filtering of the strong-motion records, usually in the 0.2-25
Hz range. This might be unsuitable for monitoring when long-period information is desired; even so,monitoring of large structures is still performed with accelerometer-only instrument packages (e.g.,
Ventura et al., 2003) mainly because alternate methodologies have not yet been proven to be practical.
Examples of GPS-only based monitoring exist (Lovse et al. 1995; Stein et al. 1997; Hudnut and Behr,
1998) but the technology has stagnated in this regard. Deployment of GPS for monitoring following
the proposal of Celebi and Sanli (2002) has been rather limited. However, the optimal combination of
GPS and accelerometer data described here along with lower costs for both types of sensors should
expand the use of this technology for monitoring structures.
RAPID SOURCE CHARACTERIZATION AND TSUNAMI WARNING
From the broadband displacement waveforms, we leveraged the improved accuracy (especially in thevertical) to develop a coseismic model of the earthquake 200 seconds after initiation (Figure 5). We
followed the methods of Crowell et al. [2012] to rapidly model the event, whereby we utilized Green’s
functions from Okada [1985], a first-order Tikhonov regularization with a generalized smoothness
parameter, and a moving average window of 50 seconds on the displacement waveforms. We used the
Slab 1.0 model [Hayes and Wald, 2009; Hayes et al., 2009] of the Kuril-Japan trench as the faultinterface. Results from our model show that there is a large displacement from the trench towards
the hypocenter, with some smaller-scale features towards the south. The solution computed here is
obtainable within less than 4 minutes of the event, which would have proved invaluable to tsunami
warning and emergency first responders.
Given the finite fault slip model (Figure 5) the next step is to incorporate numerical modelingalgorithms to rapidly predict expected tsunami intensity parameters such as maximum amplitude,
inundation distance and run-up height in the near-source region. Ohta et al. (2012) have taken asimilar approach but their algorithm is limited in one important way. It assumes that the fault is arectangle that slips coherently, that is, slip is the same everywhere on that fault plane. As we know
from Simons et al. (2011) and from our rapid modeling, slip on the Great East Japan earthquake fault
is highly heterogeneous; it has considerable amounts of slip trenchward, possibly explaining theanomalous tsunami intensity. Thus, assuming homogenous slip could lead to severe mis-modeling of
the expected tsunami.

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Figure 5. Finite fault-slip model for the March 11, 2011 Mw 9.0 Great East Japan
earthquake estimated in simulated real-time mode based on the 100 Hz displacements forthe 190 GPS/accelerometer stations. The slip (in meters) denoted by the color scale isvalid for 200 seconds after earthquake and denotes primarily the coseismic motion. The
moment magnitude is estimated to be 8.95. The gray arrows denote the observed(horizontal) displacements and the black arrows are the model displacements. The rms of
fit is 152 mm.

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CONCLUSIONSExisting seismic methods are limited in the near-field for rapid response after large earthquakes. For
the Great East Japan earthquake, it took about 20 minutes to determine that a great earthquake wasoccurring. We have shown that using the existing GPS and accelerometer networks in Japan,
characterization of the earthquake, including magnitude and finite fault slip, could have completed
within less than 4 minutes of initiation. These results could have had important implications for rapid
structural damage assessments and early warning for the ensuing devastating tsunami.
ACKNOWLEDGMENTS
We thank Japan’s GSI for GEONET 1 Hz data and NIED for 100 Hz KiK-net and K-Net
accelerometer data for the 2011 Great East Japan earthquake. This paper was funded by NASA/AIST
Grant No. NNX09AI67G.
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