gps horizontal kinematics pattern in the italian peninsula ... · gps horizontal kinematics pattern...

GPs horizonTAl kinemATics PATTern in The iTAliAn PeninsulA n. cenni1,2, e. mantovani3, P. baldi2, m. viti3, d. babbucci3, m. bacchetti2, A. vannucchi3

1Dipartimento Scienze Biologiche, Geologiche e dell’Ambiente, Università degli Studi di Bologna, Italy2Dipartimento di Fisica ed Astronomia, Università degli Studi di Bologna, Italy3Dipartimento Scienze Fisiche, della Terra e dell’Ambiente, Università degli Studi di Siena, Italy

Introduction. The present horizontal kinematic pattern in the Italian peninsula is deter-mined by using more than 400 continuous GPS stations operated in the 2001-2013 time span. The relatively high density of the network, in particular in central and northern Italy, can pro-vide a detailed spatial definition of horizontal movements. This short term kinematic pattern is compared with the long-term kinematics, inferred from post-early Pleistocene deformation, in order to gain insight into possible tectonic implications. Also the present horizontal deforma-tion pattern in the crust has been deduced by an weighted least-square procedure.

GPS data analysis. The daily observations of the considered GPS stations have been de-termined over the time interval 2001-2013. The data have been processed using the GAMIT software version 10.5 (Herring et al., 2010a), adopting the distributed processing procedure (Dong et al. 1998). The network is divided into 30 sub–networks (clusters), each including at least the following six common stations: BRAS, CAGL, GRAZ, MATE, WTZR and ZIMM. The IGS precise ephemerides are included in the processing with tight constraints as the Earth Orientation Parameter (EOP). The Phase Centre Variation (PCV) absolute corrections for both ground and satellite antennas are included. Loose constraints are assigned to the daily position coordinates of stations. The temporal evolution of diurnal, semidiurnal, and terdiurnal solid earth tides are reconstructed using the IERS/IGS 2003 models, also applying pole-tide correc-tions according to the IERS standards (Herring et al., 2010a). The ocean-loading tide effect is modeled using the FES2004 tide model produced at the Centre National d’Etudes Spatiales (Lyard et al., 2006). The dry and wet components of the earth atmosphere produce an ‘atmos-

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pheric delay’ in the travel time of the GPS signal from the satellite to the receiver, with respect to the vacuum model. This delay is modeled using the default model described by Saastamoi-nen (1972), adopting the global pressure and temperature distribution models developed to Boehm et al. (2006). The daily loosely constrained solutions of the 30 clusters obtained after GAMIT processing are combined into a unique solution by the GLOBK software (Herring et al., 2010b) and aligned into the ITRF2005 reference frame (Altamimi et al., 2007) by a weight-

ed six parameters transforma-tion (three translation and three rotation), using the ITRF2005 coordinates and velocities of the following 16 IGS stations: BUCU, CAGL, GRAZ, IENG, LAMP, MARS, MATE, PENC, SFER, SOFI, TLSE, VILL, WTZR,YEBE and ZIMM

At the end of this procedure, the daily time series of the north, east and vertical geographical position components of each site included in the analysis are estimated. These series are analyzed in order to assess the three velocity components for each site, with an observation period longer than 2 yr. The time series component yc(t), (c =1,2,3 for the north, east and vertical component) in the first step of the analysis are modelled by the following relation:

(1)

where Dc and vc are the intercept and constant rate, respectively. The gcj terms are the offset magnitudes for the N identified discontinuities, due to instrumental changes or seismic events eventually occurred at the Tj epochs, H is the Heaviside step function. These parameters are estimated using a weighted least square method. Successively, the data (outliers) whose deviation from the mean linear trend, estimated using the previous values, is three time larger than the average WRMS (weighted root mean square) of the position time-series residuals (have been identified and removed. The parameters of Eq. (1) are re-computed without outliers and the residual time series are estimated. These time series are then analyzed with the Lomb-Scargle method (Lomb, 1976; Scargle, 1982), in order to detect the principal periodic signal F with the maximum power value in the spectrum. This research is carried out in the period domain between T/2, where T is the observation time span of the site, and 1 month. The equation 1 has been modified in order to include the terms involving the principal periodic signal F so identified:

Fig. 1 – Residual horizontal GPS ve-locities with respect to Eurasia. The fixed Eurasian frame is modelled us-ing the Euler pole proposed to Altami-mi et al. (2007): 56.33 °N, 95.98 °W, ω = 0.261°/Myr. The coloured domains are based on the horizontal interpo-lated velocity field obtained using a D value of 50 Km, that is about three times the average spacing of the net-work (see text and Cenni et al. 2012 for explanations).

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(2)

The linear trend with other parameters of Eq. 1, the amplitude Ec ( ) and phase φ ( ) of the principal periodic signal F are estimated by a weight least square approach. As argued in several papers (e.g., Hackl et al., 2011; Bos et al., 2008; 2010; King and Williams, 2009; Santamaria-Gomez et al., 2011; Williams 2004, 2008), the noise εi(t) in the GPS position time series can be described as a power law process. Some different methods have been developed in order to estimate the characteristics of noise in the GPS time series and a more realistic values of the velocity uncertainties. In this note we have estimated the uncertainties associated to the velocities by the Allan Variance of the Rate (AVR) method, introduced by Hackl et al. (2011). This method, derived from the analysis of the frequency stability in atomic clocks or crystal oscillators (Allan, 1966), gives the possibility of dealing the time correlated noise in a time series. The estimate of velocity uncertainties is carried out in two independent steps: first the variance is computed and successively the choice of the error model is performed.

Horizontal velocity field. The horizontal velocity pattern obtained by the above analysis (Fig. 1) indicates that the outer part of the Northern Apennine belt moves significantly faster (3-4 mm/yr) and more easterly with respect to the surrounding zones, where velocities are mostly lower than 2 mm/yr. This evidence is fairly significant, being coherently indicated by many velocity vectors. One may note that the faster domain roughly corresponds to the Apennine sector that has been characterized by greater mobility since the middle Pleistocene (e.g., Mantovani et al., 2011, 2012, 2013; Cenni et al., 2012, 2013). This correspondence may suggest that the dynamic context that acted in the most recent tectonic evolution, causing the lateral escape of the above mentioned wedges, is still going on.

In order to minimize local eventually anomalies in a possible regional kinematic field, an interpolation approach has been applied. To this purpose, we have used a weighted least-

square procedure with a dis-tance-decaying parameter D which takes into account the distances between the grid node and GPS stations (Shen et al., 1996). This computation, starting from the GPS velocity

Fig. 2 – Horizontal strain rate field estimated using the weighted least square method described in text and in Cenni et al. (2012). This pattern has been obtained by using a distance decay factor of 50 Km, that is a val-ue about three times larger than the average spacing of the network. Con-verging and diverging black arrows indicate principal axes of shortening and lengthening, respectively. The 2D dilatation-rate field (red extensional and blue compressional) is also shown in the figure.

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data, solves for six parameters: the two horizontal velocity components and the four velocity gradients (Feigl et al., 1990). The weights are estimated by scaling the variance associated with the velocity data with an exponential scaling function e(djl/D), where djl is the distance between the jth node of the grid and the lth GPS station, and D is the distance decay factor. The GPS sites lying at a distance lower than D from the jth point of the grid give a significant contribution to the estimate of the interpolated velocity and of the strain rate (Shen et al., 1996). In order to improve the reliability of results, we have adopted two geometric criteria: the interpolated values are taken as acceptable only when at least three GPS sites are located at a distance lower than D from the point considered and the sites considered must be uniformly distributed in the surrounding region (one in each 120° angular sector). The resulting strain rate field obtained, by using a D value (50 km) about three times larger than the average spacing of the network (Fig. 2), points out the presence of fairly coherent regimes in the various sectors of the zone considered. A compressional regime, with SSE–NNW shortening axis, dominates in the East-ern Southern Alps, at the northern collisional border of the Adriatic plate with the Eurasian domain. A roughly N–S compression, associated with minor orthogonal lengthening occurs in the Po Valley area which overlies the outer buried thrusts of the Northern Apennine belt. This regime is consistent with the strain field that has been associated with the recent strong earthquakes that occurred in the Emilia zone since May 20, 2012 (Pondrelli et al., 2012). A dominant extensional regime, with SW–NE lengthening axis, coherently occurs in the North-ern Apennines, in agreement with the features of the extensional tectonics that has formed the main troughs running along the axial part of the chain (e.g., Boncio and Lavecchia, 2000). A transtensional regime mainly occurs in the central Apennines, where major sinistral fault systems are recognized (e.g., Piccardi et al., 2006).

Another parameter that can be derived from the GPS displacement field is the total strain rate (TSR), which is defined by the following relation where , and

are the horizontal components of the strain rate tensor (Kreemer et al., 2003). The pattern of TSR on a grid of 0.1° x 0.1° is shown in Fig. 3. The comparison of this pattern with the distribution of the earthquakes of M ≥ 4 occurred in the same area since 01/01/2001, shows that most seismicity is located where the TSR values are relatively high.

Fig. 3 – Second invariant of 2D strain rate tensor (Total strain rate = TSR) estimated from the velocity field (Fig. 1). Circles indicate the locations of the earthquakes occurred since 01/01/2001, with magnitude M ≥4; and depth greater than 40 km (ISIDe)

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Acknowledgements. We are grateful the following Institutions: ASI, ARPA Piemonte, FOGER (Fondazione dei Geometri e Geometri Laureati dell’Emilia Romagna), FREDNET (OGS), LABTOPO (University of Perugia), LEICA-Italpos, Regione Abruzzo, Regione Friuli Venezia Giulia, Regione Liguria, Regione Lombardia, Regione Piemonte, Regione Veneto, RING-INGV, Provincia Autonoma di Bolzano (STPOS), Provincia Autonoma di Trento (TPOS), STONEX, which have kindly made available GPS recordings. We are grateful to Settore - Coordinamento Regionale Prevenzione Sismica Dir. Gen. Politiche Territoriali e Ambientali Regione Toscana and Servizio Geologico, Sismico e dei Suoli Regione Emilia Romagna for the economic and logistic support. The figures have been carried out by the Generic Mapping Tools (Wessel and Smith, 1998).references Allan D. W., 1966. Statistics of atomic frequency standards. Proc. IEEE, 54(2), 221–230.Altamimi Z., Collilieux X., Legrand J., Garayt B. and Boucher C.; 2007: ITRF2005: a new release of the International

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gps horizontal kinematics pattern in the italian peninsula ... · gps horizontal kinematics pattern...

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