GPS time series modeling by autoregressive moving average method: Application to the crustal deformation in central Japan

Autoregressive moving average (ARMA) method is applied to modeling the time series of position changes of GPS sires, obtained by the Geographical Surv ey Institute (GSI) of Japan during the period from April 1996 to March 1998 . The present application is focused on denoising of the GPS time series da ta where only white noise is considered, and detection of data discontinuit ies and outliers in order to obtain time-averaged velocity and strain field s in central Japan. The data discontinuities are detected by a typical Kalm an filter algorithm. The outliers are eliminated by using robust estimation techniques during the ARMA process. The averaged strain field, calculated by the least-squares collocation method from the improved two-year time ser ies data, distinguishes clearly between the tectonically active and inactiv e regions. Higher maximum shear strain rates were detected in the southern area of the Kanto district. In the areas with very high seismicities, howev er, the difference between the maximum shear strain rates, that were estima ted from the raw time series data and the ARMA-analyzed data, amounted to a bout 0.2 microstrain/yr. This indicates that the existence of noise and dis continuities can lead to an over-prediction of the strain field.