Improving strain rate estimation from velocity data of non-permanent GPS stations: the Central Apennine study case (Italy)

An efficient procedure is proposed to define realistic lower limits of velocity errors of a non-permanent GPS station (NPS), i.e., a station where the antenna is installed and operates for short time periods, typically 10–20 days per year. Moreover, the proposed method is aimed at being independent of standard GPS data processing. The key is to subsample appropriately the coordinate time series of several continuous GPS stations situated nearby or inside the considered NPS network, in order to simulate the NPS behavior and to estimate the velocity errors associated with the subsampling procedure. The obtained data are used as lower limits to accept or correct the error estimates provided by standard data processing. The proposed approach is applied to data from the dense, non-permanent network in the Central Apennine of Italy based on a sequence of solutions for the overlapping time spans 1999–2003, 1999–2004, 1999–2005 and 1999–2007. Both the original and error-corrected velocity patterns are used to compute the strain rate fields. The comparison between the corresponding results reveals large differences that could lead to divergent interpretations about the kinematics of the study area.

[1]  T. Dixon,et al.  Noise in GPS coordinate time series , 1999 .

[2]  O. Francis,et al.  Modelling the global ocean tides: modern insights from FES2004 , 2006 .

[3]  Robert W. King,et al.  Estimating regional deformation from a combination of space and terrestrial geodetic data , 1998 .

[4]  Y. Bock,et al.  Anatomy of apparent seasonal variations from GPS‐derived site position time series , 2001 .

[5]  Paolo Baldi,et al.  Global Positioning Systems and digital photogrammetry for the monitoring of mass movements: application to the Ca' di Malta landslide (northern Apennines, Italy) , 2003 .

[6]  A. R. Pisani,et al.  Data analysis of Permanent GPS networks in Italy and surrounding region: application of a distributed processing approach , 2006 .

[7]  Paolo Baldi,et al.  Kinematics of a landslide derived from archival photogrammetry and GPS data , 2008 .

[8]  Marco Dubbini,et al.  Modeling environmental bias and computing velocity field from data of Terra Nova Bay GPS network in Antarctica by means of a quasi-observation processing approach , 2007 .

[9]  A. Pesci,et al.  Strain rate analysis over the Central Apennines from GPS velocities: the development of a new free software , 2006 .

[10]  David D. Jackson,et al.  Crustal deformation across and beyond the Los Angeles basin from geodetic measurements , 1996 .

[11]  Z. Altamimi,et al.  ITRF2005 : A new release of the International Terrestrial Reference Frame based on time series of station positions and Earth Orientation Parameters , 2007 .

[12]  J. Ray,et al.  Anomalous harmonics in the spectra of GPS position estimates , 2008 .

[13]  Paolo Baldi,et al.  Short term (geodetic) and long term (geological) velocity fields in the Northern Apennines , 2008 .

[14]  Yehuda Bock,et al.  Error analysis of continuous GPS position time series , 2004 .

[15]  Stephane Mazzotti,et al.  Current deformation in the northern Canadian Cordillera inferred from GPS measurements , 2007 .

[16]  Michael R. Craymer,et al.  Current tectonics of northern Cascadia from a decade of GPS measurements , 2003 .

[17]  S. Williams.  The effect of coloured noise on the uncertainties of rates estimated from geodetic time series , 2003 .

[18]  Antonio Galgaro,et al.  Grid_strain and grid_strain3: Software packages for strain field computation in 2D and 3D environments , 2008, Comput. Geosci..