In this paper, parameters of north Tabriz fault are studied using 3D displacement field and artificial neural networks (ANNs). We provide the 3D surface displacement along the north Tabriz fault using an integration of tropospherically corrected InSAR, GPS and precise levelling data. To perform the InSAR analysis, we use the 17 ENVISAT radar acquisitions. The line of sight (LOS) displacement field was corrected using the ERA-Interim global meteorological reanalysis models. In order to calculate the 3D displacement, we use a Simultaneous and Integrated Strain Tensor Estimation from Geodetic and Satellite Deformation Measurements approach. ANNs used to estimate the parameters. 3D displacement field and ANN algorithm yields an average slip rate of 6.1 § 0.01 mm/year with a locking depth of 13.4 § 0.01 km. This rate is consistent with previous geodetic estimates based on recent global positioning system measurements and InSAR analysis. In addition, the length, width and dip angle of fault are about 101.2 § 0.01 km, 25.06 § 0.03 km and 63 § 0.05 deg. This study demonstrates that interseismic displacement with a sub-centimetre rate can be successfully detected by integration of multi temporal InSAR techniques, GPS and precise leveling data and shows that ANN algorithm is suitable for estimating the parameters of north Tabriz fault.
An important issue in deformation analysis is identification of (un)stable points in a monitoring network. This paper proposes a new method that identifies the unstable points of a network based on the generalized likelihood ratio (GLR) test. The method, which simultaneously uses the observations of two epochs, is called the simultaneous adjustment of two epochs (SATE) method. The existing methods apply individual least-squares adjustment to the observations of each epoch. SATE is applicable to one-, two-, or three-dimensional deformation networks with any type of observations, including distances, angles, global positioning system (GPS) baselines, and height difference. To investigate the performance of the proposed method, observations of a real GPS deformation-monitoring network were used. The results for unstable points identification are identical to those of the existing methods. Furthermore, a few simulation case studies were used to evaluate the efficacy of the proposed method. The simulated results for the deformation-monitoring networks, with different scenarios, confirm that the proposed method always performs the best. This method can thus be introduced as a reliable method that provides results that are superior to those of the two existing classical methods.
Maps of the total electron content (TEC) of the ionosphere can be reconstructed using data extracted from global positioning system (GPS) signals. For historic and other sparse data sets, the reconstruction of TEC images is often performed using multivariate interpolation techniques. In this paper, a quantitative comparison of the ability of artificial neural networks (ANN), polynomial fitting and kriging interpolation was carried out in order to model the spatial variations of TEC using GPS data over Iran. These methods are suitable for handling multi-scale phenomena and unevenly distributed data. The observations collected at 25 GPS stations from Iranian permanent GPS network (uniformly spread all over Iran with sampling rate of 30-seconds). Dual frequency carrier phase and code GPS observations were used. A smoothed TEC approach was used for absolute TEC recovery. Evaluation of the methods has been applied with single GPS station in Tehran equipped with ionosonde instrument. The minimum relative error for ANN, polynomial and kriging are 4.37, 6.35, 9.13 % and the maximum relative error are 8.61, 29.06, and 20.14 % respectively. Also root mean square error (RMSE) of 3.7 TECU is computed for ANN method which is less than RMSE of other mentioned methods. The results show that ANN method has higher accuracy and compiles speed than kriging and polynomial. As well as, it is found that polynomial and kriging methods required many computational points in adjustment step.
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