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The postseismic effects of the 2006 Yogyakarta earthquake was caused by the Opak Fault activity as the possible source still continues. Secular velocity analysis who referred to the velocity which is free from the other deformations than interseismic deformation needs to be done to represent local deformation of the fault. This study was conducted to determine the significance of the difference between the secular velocity without and with postseismic corrections. The secular velocity is determined by the linear least square method. Furthermore, the velocity is calculated its postseismic correction with logarithmic method. This research data includes CORS BIG and Opak Fault station observation data from 2013 to 2018 which is processed using GAMIT/GLRED. Furthermore, the time series data for each station is plotted and analysed, then it is visualized its velocity. The result of this study shows the value of secular velocity each station ranges from 21.676 to 30.997 mm/year and −14.116 to. 2.573 mm/year in the East (E) and North (N) components respectively, and the resultant value of the horizontal velocities range from 22.507 to 32.711 mm/year. The secular velocity resulted with postseismic correction range from 20.735 to 29.864 mm/year and −22.255 to −6.439 mm/year in E and N components, and the resultant value of the horizontal velocities range from 36.963 to 23.281 mm/year. The velocities difference value in the E and N components range from −4.876 to 1.915 mm/year and −1.543 to 14.175 mm/year, and the horizontal velocities values range from −11.035 to 1.260 mm/year. The statistical significance of the two-parameter differences of the whole station, it is concluded that there was no significant velocities difference between the secular velocity values without and with postseismic corrections.

The Opak Fault is an active fault that can potentially cause earthquakes in Yogyakarta. Periodic monitoring of the Opak Fault activity was previously used more GNSS observation data from the measurement campaign by the Geodesi Geometri dan Geodesi Fisis (GGGF) Laboratory Team, Geodetic Engineering Department, Faculty of Engineering, Universitas Gadjah Mada. However, there are several CORS BIG stations located in Yogyakarta. The CORS BIG data is used to increase the precision of the Opak Fault monitoring station. Therefore, the addition of the CORS is evaluated to obtain a displacement in the monitoring station. The computation of the displacement velocity value of the Opak Fault monitoring station has been done before using the Linear Least Square Collocation and grid search methods. The other method, namely the kriging method, needs to be evaluated for producing a more precise displacement velocity value. The research data includes GNSS campaign and CORS BIG data for six years, 2013 to 2020. The CORS stations around DIY are JOGS and CBTL. The GNNS data were processed to determine the solution for the daily coordinate, displacement, and standard deviation values for each Opak Fault monitoring station. The displacement velocity value is generated by the Linear Least Square method then reduced from the influence of the Sunda Block. The velocity value is used in the strain value estimation around the Opak Fault area at each station using the kriging method combined with the gaussian sequential simulation technique. The estimated displacement velocities are examined for statistical significance compared to the research of Adam (2019) and Pinasti (2019). This research generates the value of the displacement velocity in the east and north components of 12.39 to 30.99 mm/year and 1.96 to -14.11 mm/year, respectively. The displacement direction of all monitoring stations is dominant to the southeast. The Sunda Block reduced the displacement velocity. The east and north components are -2.32 to 2.28 mm/year and -0.52 to 4.2 mm/year, respectively. The displacement direction is towards the northwest. The strain estimation using the kriging method combined with the gaussian sequential simulation technique obtained an average strain value of 0.05 microstrain/year. The result of the data processing at each station has different arrow lengths, meaning that each location has a different strain value.

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