We analyze more than 100 GPS time series of continuous and discontinuous GPS stations located in the Abruzzi region (Italy) surrounding the epicentres of the L’Aquila 2009 seismic sequence. The purpose of this work is to reconstruct the coseismic displacement field caused by the 6th April (Mw 6.3) main shock from a dense network of survey-mode stations surrounding the epicentral area and to characterize the early postseismic deformation field. In the months following the main shock, an extensive GPS survey was carried out on the existing Central Apennines Geodetic Network (CAGeoNet), with the intention of collecting a robust data set and to study the co- and postseismic deformation field of this Apenninic normal faulting earthquake. The analysis is carried out with two independent procedures and software (Bernese and Gamit) in order to provide reliable and validated geodetic solutions. The analysis of the postseismic transients and the knowledge of long-term inter-seismic velocities at all GPS stations, issued from permanent and CAGeoNet sites, allow us to derive a dense co- and postseismic displacement field for the L’Aquila Mw 6.3 main shock in a wide area around the epicentre. The highest deformation rate occurs during the first 4–5 months after the main shock and persists in the following at slightly slower rate throughout the whole monitoring period. Fast deformation rates imply that most of the observed deformation is due to a process different from a pure viscoelastic relaxation of the stress perturbation. Since the observed rates would imply a too low effective viscosity value (below 1017 Pa s), we rather suggest that most of the observed deformation in the first months after the earthquake is due to different processes, most likely frictional afterslip possibly modulated by the presence of fluids. The new coseismic displacement field is used to invert for the main shock fault geometry, analysing the consistency among the different geodetic solutions and the combined one, with the goal of validating the two data sets.

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