Estimation of north Tabriz fault parameters using neural networks and 3D tropospherically corrected surface displacement field

Abstract

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.