GNSS study time series: linear and nonlinear components

  1. Belén Rosado 1
  2. Raúl Páez 13
  3. Alejandro Pérez-Peña 12
  4. Sonia Pérez-Plaza 3
  5. Gonçalo N. Prates 14
  6. Alberto Fernández-Ros 12
  7. Jorge Gárate 1
  8. Fernando Fernández-Palacín 3
  9. Manuel Berrocoso 12
  1. 1 Laboratorio de Astronomía, Geodesia y Cartografía. Campus de Puerto Real. Universidad de Cádiz. 11510 Puerto Real (Cádiz, Spain).
  2. 2 Departamento de Matemáticas. Facultad de Ciencias. Universidad de Cádiz. 11510 Puerto Real (Cádiz).
  3. 3 Departamento de Estadística e Investigación Operativa. Facultad de Ciencias. Universidad de Cádiz. 11510 Puerto Real (Cádiz).
  4. 4 Instituto Superior de Engenharia, Universidade do Algarve, Faro, Portugal.
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Actas:
International Work-Conference on Time Series (ITISE 2016)

Editorial: Godel Editorial

ISBN: 978-84-16478-93-4

Año de publicación: 2016

Páginas: 691-700

Tipo: Aportación congreso

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Resumen

Observation of lithospheric deformation and its analysis is important to investigate local tectonic movement in geodynamically active areas. Some processes can be identified from GPS data time series. For this reason, duringthe last decade a number of continuous processing services for observations taken by Global Positioning System (GPS) networks have been implemented worldwide. In this work, the analyzed GPS data correspond to 60 continuously recordingGPS (CGPS) stations located in the southern region of the Iberian Peninsula and Northern Africa, what we call SPINA network. The main goal is to present a detailed topocentric coordinates time series study for these sites distinguishingbetween linear and nonlinear components. The methodology applied on this paper for CGPS coordinate time series analysis is based on filtering processes, harmonic adjustments and wavelets analysis, once the coordinate time series are cleared of outliers and periods of bad data are identified and removed. Then, the wavelet transform was applied to the GPS time series to reduce scattering and separate noise and signal. Next, a mathematical model for the station movement is applied to determine sites’ velocities, in order to provide useful information for further geodynamic interpretations.From this processing, we present sites’ velocities determined by GPS, from which the surface deformation model is given. Finally, we compare the results obtained in our final solution with results provided by other geodetic and geophysical models.