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Displacements recorded on continuous GPS stations following the 2014 M6 Cephalonia (Greece) earthquakes: dynamic characteristics and kinematic implications.


On Jan. 26 and Feb 3, 2014 Cephalonia island, Ionian Sea, Greece, was struck by two strong, shallow earthquakes (Table 1; Karastathis et al., 2014; Papadopoulos et al., 2014; Valkaniotis et al., 2014; NOA moment magnitudes 6.0 and 5.9, respectively). The earthquakes ruptured at least two, 10+ km long, strike-slip faults (Valkaniotis et al., 2014; Boncori et al., 2015). According to the NOA Institute of Geodynamics ( the epicentre of the first event (Fig. 1; NOA web report) was located near the town of Argostoli (the capital), while the second one was located in the north Paliki Peninsula, about 10 km to the NW of the former. During both earthquakes, ground-shaking phenomena such as liquefaction, road failures, rock falls, small/medium size landslides and stonewall failures were widespread all over the western part of the island (Valkaniotis et al., 2014). No primary, surface fault breaks were observed. Both earthquakes occurred in response to ENE-WSW horizontal strain in central Ionian Sea (Hollenstein et al., 2008a; Ganas et al., 2013).

Continuous GNSS (Global Navigation Satellite Systems) networks deployed over the last twenty years to study kinematic deformation of the crust have been recently used to estimate epoch-wise displacements during moderate (5<M<7) to large (7<M<9.3) earthquakes (e.g. Larson et al., 2003; Hollenstein et al., 2008b; Avallone et al., 2011; Wright et al., 2012). Thanks to their improvements in accuracy (now of the order of millimeters, see Elosegui et al., 2006) and their high sampling rate (up to 50 Hz, see Genrich and Bock, 2006) GnSS stations are increasingly becoming important tools, not only for geodesists but also for seismologists. The capability of GNSS permanent networks for seismological observations was proposed by Hirahara et al. (1994), Ge et al. (2000) and Larson (2009). There already exists a significant bibliography on this topic and a number of large earthquakes have been already recorded by High-Rate GPS (HRGPS) stations such as the 2002 M7.9 Denali earthquake (Kouba, 2003; Larson et al., 2003; Bock et al., 2004; Bilich et al., 2008), the M8.3 Tokachi-Oki and the M6.5 San Simon earthquakes in 2003 (Miyazaki et al., 2004; Ji et al., 2004; Langbein and Bock, 2004; Emore et al., 2007; Wang et al., 2007; Larson and Miyazaki, 2008; Melgar et al., 2011; Crowell et al., 2012), the M9.3 Sumatra earthquake in 2004 (Ohta et al., 2006), the 2008 M8.0 Sichuan earthquake (Shen et al., 2009; Shi et al., 2010), the 2009 M6.3 L'Aquila earthquake (Avallone et al., 2011), the M9.0 Tohoku-Oki earthquake in 2011 (Grapenthin and Freymueller, 2011; Sato et al., 2011; Guo et al., 2013) and the 2013 M6.6 Lushan earthquake (Lou et al., 2014).

The Cephalonia earthquake displacements were recorded by several continuous global positioning system (cGPS) stations of NOANET (; Ganas et al., 2011, 2014; Figure 1). All stations were operating with a sampling frequency of 1 Hz. Stations VLSM, SPAN and PONT are equipped with a LEICA GRX1200PRO receiver and AX1202 antenna and were functioning during both earthquakes. Station KIPO is equipped with a LEICA GMX902GG receiver and AX1203+GNSS antenna, but, during the period of seismic unrest it was not functioning after lightning damage. Station VLSM is located about 11 km to the east of the epicentre of the Jan 26, 2014 event and 18 km to the SE of the Feb 3 2014 event (Fig. 1). In addition, the sampling frequency of the station VLSM was operating at 5 Hz thus recording the coseismic displacements produced by the Jan 26, 2014, earthquake at higher frequency. In the present study, after the description of the high-rate GPS (HRGpS) data analysis, we will show and compare results for both earthquakes for the near-field station VLSM. Then, we will compare the displacements recorded by the HRGPS (1 Hz up to 5 Hz) at VLSM station and the strong-motion time histories (100 Hz) at NOA strong motion station SHMA, where the different instruments were approximately co-located (SHMA is located 9.7 km to the NE of VLSM; Figure 1). We also present results from 30-s GPS observations and use this data to test an Okada-type model of the January 26, 2014 dislocation.


In this study high-rate data from three continuous GPS stations equipped with Leica dual-frequency receivers and geodetic antennas were processed in kinematic Precise Point Positioning (PPP) mode. The available GPS data were sampled at 1 Hz and for the day of the first earthquake (i.e. 26 January 2014), the VLSM station was also sampling at 5 Hz. Although, in the near field, and for a low to moderate earthquake, the HRGPS time series of displacements sampled at rates smaller than 5 Hz can be affected by aliasing effects (Smalley, 2009) we compared the results with strong motion data which are independent and not affected by aliasing due to their high sampling rates.

PPP GPS time series contain both systematic errors (e.g. multipath) and unmodeled stochastic noise, which can be the same order of magnitude of the seismic signal. We applied an ad-hoc strategy in order to minimize the noise level of the position time series. In order to reduce the uncertainties we combined the GPS PPP solutions originating from different processing software. In particular, we used GIPSY 6.2 (Zumberge et al., 1997; Webb and Zumberge, 1997) and CSRS-PPP ( to process the GPS data in kinematic mode. In all approaches we used the final IGS products (precise reference orbit and clock products), a 10[degrees] satellite elevation mask angle and the ITRF08 (Altamimi et al., 2011) as reference frame. The two kinematic time series from the different processing were filtered by using the modified sidereal filtering technique (Fig. 2; Choi et al., 2004), and aligned subtracting the mean values for the period prior to the event. An editing criterion has been adopted in order to eliminate high residuals by taking, for each couple of solutions the weighted mean value with the inverses of estimated variances as weights. This choice of weighting always assures an increasing in precision. Indeed, for two measurements [x.sub.1] and [x.sub.2] with standard deviations of [[sigma].sub.1] and [[sigma].sub.2] respectively, the quantity

y = [[sigma].sup.2.sub.2] x [x.sub.1] + [[sigma].sup.2.sub.1] x [x.sub.2]/[[sigma].sup.2.sub.1] + [[sigma].sup.2.sub.2] (1)

has a propagated error of

[[sigma].sub.y] = [[sigma].sub.1] x [[sigma].sub.2]/[square root of ([[sigma].sup.2.sub.1] + [[sigma].sup.2.sub.2])] which is always [less than or equal to] min ([[sigma].sub.1], [[sigma].sub.2]). We checked that for the considered periods all the solutions from the two processing were compatible.


We analyzed the processed displacements in the frequency domain to characterize the noise components that affect the GPS time series. As for many geophysical phenomena and following Mao et al. (1999), the power spectral density (PSD) of a GPS displacement time series can be well modeled as the sum of white noise (k=0) and colored noise (time-correlated) of the form:

PSD = [P.sub.0] ([f.sup.-k] + [f.sup.-k.sub.0]) (2)

where [f.sub.0] represents the crossover frequency of the PSD where the two processes have the same power levels. At frequencies higher than [f.sub.0] the signal components are indistinguishable from the white noise, thus losing any information. We adopted the Welch's method (Welch, 1967) to estimate the PSD by using 12 hours of data not including the earthquakes. We used the Levenberg-Marquardt algorithm (LMA) (Marquardt, 1963) to estimate the model parameters. Figure 3 shows that the analyzed GPS time series follow a Brownian/red noise behavior and that the crossover frequency is higher for 5 Hz data. This means an improvement in resolving and tracking the higher frequency ground shaking typical of earthquakes. For the first event of Jan 26, we had the possibility to process GPS data sampled at 5 Hz. We adopted the same processing scheme, obtaining the solutions in Figure 4 where the time series at 5 Hz is compared with the one obtained from the data at 1 Hz.

The different amplitudes in the ground motion between the two events can be also seen from Figure 5 where the time series at the same station are compared. In particular, we estimated the peak-to-peak motion obtaining for the 2014, Jan 26 event a maximum dynamic displacement in the North component of 3.0 cm and 1.5 cm in the East component, while for the 2014, Feb 3 event we estimated the maximum displacement of 6.1 cm and 3.4 cm in the North and East components respectively. Hence the second earthquake, even if with almost 22% less seismic moment in comparison to the first, produced a ground displacement double in amplitude. This could be explained by a difference in focal depth between the two events (Table 1; 15 vs. 5 km) despite the difference in epicentral distance (11 vs. 18 km), or to a difference in rupture dynamics or to a combination of both processes. Also, the azimuth from seismic event to GPS station is different in the two cases (Fig. 1).


We compared the time series of the displacement estimated by GPS at station VLSM and the displacement integrated from the accelerometer at SMHA (NOA) station which is located 9.7 km to the NE (location Sami; see Fig. 1). The raw accelerometer data have been a) corrected for the sensor CMG-5TD response and b) integrated, following the procedure described in Boore et al. (2002). Figure 6 shows the comparative behavior on the ground shaking (both amplitude and shape of waveforms) and the time delay (2-3 seconds) due to the different location with respect to the epicentre of the earthquake. The difference in S-wave (identified by the accelerometric signal) amplitude between the two earthquakes is confirmed also by the strong motion data. The calculated cross-correlation coefficients, also displayed in Figure 6, quantify the good agreement between the two observations especially for the second event.


We analysed daily 30-s observations from four (4) continuously operating stations within 100 km from the epicentres of the two strong events using GAMIT/GLOBK 10.40 (Herring et al., 2010). We used precise orbits from the International GNSS Service (IGS) and absolute calibration values from IGS tables, adopting a three step approach based on the <<quasi-observation>> theory (Feigl et al., 1993; Dong et al., 1998). For more details regarding the processing strategy as well as the reference frame realization, the reader is referred to Chousianitis et al. (2013). The final product of the analysis is the three-component time series of daily station positions with respect to the IGS realization of the ITRF2008 NNR frame (Altamimi et al., 2011). The North, East and Up detrended position time series of the VLSM GPS station affected by the Cephalonia seismic events are depicted in Figure 7 for time periods from January 1, 2013 until February 15, 2014. The static coseismic displacements were estimated by calculating the difference between the average position of 7 days before, and the average position of 7 days after the events on January 26, 2014 and February 3, 2014. The coseismic offsets are reported in Table 2 for all stations analysed (including station KEFA reported at the web site and station SVOR provided by the HEPOS network operators). The vector norms are plotted in Figure 8. The displacement of station KIPO was calculated from 30s data up to day 255 of 2013 (Sep 12, 2013) and after day 36 of 2014 (Feb 5, 2014). Therefore, its static offset includes both seismic events without knowing which event caused most of it or which was the azimuth of the static motion for each event.


Cephalonia has been repeatedly subjected to strong ground shaking due to the proximity of the island to the Cephalonia transform fault (CTF; Fig. 8; Scordilis et al., 1985; Tselentis et al., 1997; Louvari et al., 1999; Sachpazi et al., 2000; Qi Cheng et al., 2007). The 100-km long fault zone accommodates the relative motion of the Apulia (Africa) and Aegean (Eurasia) lithospheric plates with a 70-85 km cumulative dextral displacement (Pearce et al., 2012). Part of this relative motion is accommodated by a significant thrust component as it is evidenced by onshore geological data (Lekkas et al., 2001). Our 30-s GPS data provide important constraints to identify the seismic sources of the Jan-Feb 2014 earthquakes and to associate them with the CTF itself or not.

The 30-s GPS data analysis shows that station VLSM moved to the SW while station KIPO moved to the N (Fig. 8). This motion pattern is in agreement with the right-lateral kinematics for both ruptures as obtained from seismology (moment tensor solutions). Furthermore, the spatial distribution of the relocated earthquake sequence (Karastathis et al., 2014) indicates that the earthquake activity covers only the western part of Cephalonia Island trending from NNE to SSW at a length of about 35 km and maximum lateral width of c. 10 km. In that paper, the source of seismicity clearly differs from that of CTF. This combination of evidence (GPS and seismic) clearly suggests that the Jan 26 and Feb 3, 2014 earthquakes occurred on near-vertical, strike-slip faults to the east of station KIPO, i.e. the activated fault segments are located on-shore Cephalonia. The geodetic result (Fig. 8) also confirms, that a N-S seismic fault zone has been activated onshore Cephalonia, in agreement with the InSAR findings from Merryman-Boncori et al. (2015). The northward motion of station KIPO implies that the western peninsula of Cephalonia island (Paliki) belongs to a separate crustal block with respect to the rest of the island. We need more geodetic data to identify the borders of this block; however it is likely that it runs North-South (as it is traced in Figure 8; see dashed black line) because of the interseismic pattern of campaign GPS velocities published by Lagios et al. (2012).

To test this N-S boundary scenario we computed a forward, Okada-type model (Okada, 1992; see Table 3 for parameters) for a buried dislocation in elastic half-space, using the NOA moment tensor (MT) solution for the 2014, Jan 26, 2013 event ( The dislocation strikes N18[degrees]E, dips 67[degrees] to the east and it is found at depths from 9.2 km (top fault) to 16.5 km (bottom fault). The hypocentre is placed at the depth of 16.5 km according to Karastathis et al. (2014). Our 2014, Jan 26 surface deformation model compares well with the continuous GPS observations (see Fig. 9) for comparison with observed displacements), although all GPS data are located at the hanging wall of the 2014, Jan 26 fault. Our continuous GPS stations data (observed offsets) are reported in Table 2 including NOANET stations VLSM and PONT, one private-company station (KEFA) and one station from the HEPOS network (; their station 040A renamed SVOR in this paper), in total four (4) stations in operation during the first event. Irrespective of the fault location used in modeling (Fig. 9) it is evident that a general N-S pattern of surface displacement vectors has appeared on either side of the hypocentre (yellow star in Fig. 9).

In addition, a fault source towards the southwest of the hypocentre (Fig. 9a) fits better the observed surface displacements, as motion vectors of VLSM & SVOR fit very well the modeled ones (Figs. 9b and 9c show rupture locations to the north and in the middle of the hypocentre, respectively). Station KEFA shows a large residual, probably due to local site effects as this station is located inside the town of Lixouri (Fig. 1) where liquefaction phenomena were observed (Valkaniotis et al., 2014; Papadopoulos et al., 2014). Our rupture model considers a single fault slip patch (with slip partitioning parameters as in Table 3). In Figure 9 we model the first earthquake source as one-and two-directional, meaning that the location of the hypocentre (start of the rupture) is at the ends or in the middle of the rupture. This affects the Okada calculations in 3-D space. It has to be clarified that when considering different rupture models, the position of the fault is not the same, as it can be seen in Figures 9a, 9b, and 9c. In the case of Figure 9a the position of the fault is more southerly than in the case of Figure 9c (a two-directional rupture). Both the position of the fault and the one- and two-directional slip patterns are taken up for the seismic moment calculations producing the three different theoretical displacement fields seen in Figure 9. This modeling could not be repeated for the 2nd event as there are several MT solutions proposing an east-dipping strike slip fault, however, we cannot exclude a west-dipping fault plane on the basis a) of the GFZ MT solution published on the EMSC site, b) the seismicity relocation by Karastathis et al., (2014) and c) the InSAR motion pattern (Merryman Boncori et al., 2015).

The Jan-Feb 2014 earthquakes highlighted the need for more seismological and geophysical data both onshore and offshore Cephalonia, to locate and map active faults including CTF fault structure (e.g. is it one offshore segment or several?) and its relation to the large offshore scarp about 10 km to the west of Cephalonia (see Fig. 1 for its location). This data is also necessary to characterize the seismic potential of this plate boundary fault by estimating the area and percentage of seismic coupling along it trace. Previous work from seismic profiles (Sachpazi et al., 2000; Kokkinou et al., 2005) locate the CTF within 10-km to the west of Cephalonia, however, the 2014 seismicity is offset eastwards with respect to the CTF trace. The northward motion of NOA station KIPO implies that CTF did not rupture during the 2014 events, because KIPO is located at the hanging wall of CTF and its expected co-seismic motion is to the south (CTF is a dextral strike-slip fault dipping to the southeast; Louvari et al., 1999). Therefore, it follows that CTF is accumulating elastic strain since 1983 (Mw=6.8, Global CMT solution; Scordilis et al., 1985) with an average slip rate of 2 cm/yr (Serpelloni et al., 2005; Vernant et al., 2014). Considering the scaling relations of Mai and Beroza (2000; relation mean slip to seismic moment) the amount of accumulated strain along CTF since 1983 can be released by a seismic event of M6.5-6.7, at any time.

DOI: 10.13168/AGG.2015.0005

Article info

Article history:

Received 12 September 2014

Accepted 5 January 2015

Available online 3 March 2015


F.C and K.C processed the Cephalonia GPS data. I.K did the Okada-type model. A.G wrote the manuscript with contributions from all co-authors.


We thank two anonymous reviewers for constructive comments. We also thank Vassilios Karastathis, Stefano Salvi, Sotiris Valkaniotis, Efthimios Lekkas, Christodoulos Kyriakopoulos, Gerassimos Papadopoulos, Ioannis Papoutsis, Haris Kontoes, Michail Gianniou, Panagiotis Argyrakis, Marios Papanikolaou, Christina Tsimi, Alexandra Moshou and Maria Sachpazi for comments and discussions on various aspects of this work. We thank Ioannis Kalogeras for providing the accelerometric data for NOA station Sami and Michail Gianniou for co-seismic offsets from station SVOR. Figures 7 and 8 were made by use of GMT software; Figure 9 by use of Coulomb v3.3. Figure 1 was prepared by S. Valkaniotis. NOANET was funded by several EU FP6 & FP7 projects, ESA and the Greek government (GSRT and project ASPIDA). NOANET data is available from and the NOA GSAC


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Athanassios GANAS (1) *, Flavio CANNAVO (2), Konstantinos CHOU SIANITIS (1), Ioannis KASSARAS (3) and George DRAKATOS (1)

(1) Institute of Geodynamics, National Observatory of Athens, 11810 Athens, Greece,,,

(2) INGV, Catania, Italy,

(3) Department of Geology, University of Athens,

* Corresponding author's e-mail:

Table 1 Focal parameters of the two strong earthquakes of the
January/February 2014 Cephalonia seismic sequence as calculated by
Karastathis et al. (2014). Moment magnitude is from NO A http:// .

Date           Time       Latitude    Longitude   Depth   Magnitude
(DD.MM.YYYY)   (UTC)      (degrees)   (degrees)   (km)    Mw

26.01.2014     13:55:42   38.2102     20.4614     16      6.0
03.02.2014     03:08:44   38.2734     20.4310     5       5.9

Table 2 Offset results from 30-s data for horizontal components of
NOANET (VLSM, KIPO, PONT) and collaborative stations (AGRI; belongs
to the private company METRICA SA; SVOR to HEPOS network). Station
KEFA was provided by NTUA and belongs
to the private company Tree Company SA. The norm displacement refers
to the norm of the horizontal motion vector. For station location see
map in Figures 1, 8, 9.

Station     LAT       LON     1st Event   1st Event   2nd Event
                                N cm        E cm        N cm

KIPO      38.2030   20.3480      n/a         n/a         n/a
VLSM      38.1768   20.5886     -0.52       -1.05       -0.90
PONT      38.6189   20.5851     -0.05       -0.24       -0.06
AGRI      38.6240   21.4090     -0.09       -0.07       -0.06
KEFA      38.1960   20.4380     -4.70       2.50       -20.00
SVOR      38.1149   20.5228     -1.20        0.5         n/a

Station   2nd Event   1st Event   2nd Event    TOTAL
            E cm       NORM cm     NORM cm    NORM cm

KIPO         n/a         n/a         n/a       7.41
VLSM        -1.55      1 .1 7       1.80       2.97
PONT        -0.26       0.24        0.38       0.60
AGRI        -0.11       0.11        0.13       0.24
KEFA          0         5.30        14.00      19.30
SVOR         n/a        1.30         n/a        n/a

Table 3 Source parameters used for horizontal displacement modeling
at Earth's surface by use of software Coulomb v3.3 (Toda et al.,

Lon               Lat       Fault length    Fault top    Depth (km)
([degrees])   ([degrees])       (km)          (km)

                            Fault width    Bottom (km)

20.461          38.210       13.2 / 7.9    9.2 / 16.5       16.5

Lon               Lat                     Source NOA
([degrees])   ([degrees])

                             [M.sub.w]      Strike          Dip
                            ([degrees])   ([degrees])   ([degrees])

20.461          38.210          6.0           18            67

Lon               Lat       Source NOA    Slip RL (m)
([degrees])   ([degrees])

                               Rake       Slip Rev (m)

20.461          38.210          164       0.381/0.109

Lon               Lat       Poisson   E (bar)
([degrees])   ([degrees])

20.461          38.210       0.25      8E+05
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Title Annotation:Original paper
Author:Ganas, Athanassios; Cannavo, Flavio; Chousianitis, Konstantinos; Kassaras, Ioannis; Drakatos, George
Publication:Acta Geodynamica et Geromaterialia
Article Type:Report
Geographic Code:4EUGR
Date:Jan 1, 2015
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