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[Geophysical Research Letter]
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[Interseismic deformation of the Shahroud fault system (NE Iran) from Space-
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borne Radar Interferometry measurements]
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[Z. Mousavi1, 2, E. Pathier1, R. T. Walker3, A.Walpersdorf1, F. Tavakoli4, H. Nankali4, M. Sedighi4,
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M-P Doin1]
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[1) ISTerre, Université Joseph Fourier, CNRS UMR 5275, Grenoble, France (zahra.mousavi@ujf-
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grenoble.fr)
Supporting Information for
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2) Department of Earth Sciences, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
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3) Department of Earth Sciences, University of Oxford, South Parks Road, Oxford, OX1 3AN, UK.
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4) National Cartographic Center, Geodetic Department, Tehran, Iran]
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Contents of this file
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Text S1. Removing the tropospheric delay, orbit error and residual DEM
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The tropospheric delays can be split up into turbulent and stratified components. The
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effect of the turbulent component on SAR acquisitions can be considered as random in
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space and time and is mostly topography independent. Therefore it can be reduced by
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stacking or time-series analysis [Schmidt and Burgmann, 2003]. The vertically stratified
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component, correlated with topography, is not random in space [Doin et al., 2009] and it
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should be removed before tectonic interpretation. Here we use the global atmospheric
Text S1. Removing the tropospheric delay, orbit error and residual DEM
Figures S1 to S5
Tables S1
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model ERA-Interim provided by the European Center for Medium‐Range Weather
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Forecast (ECMWF) to estimate the tropospheric delay map. ERA‐I is a global
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atmospheric model on a ∼75 km grid, generated four times per day and on 37 pressure
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levels. The used global atmospheric model like ERA-I have been shown reducing biases
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in INSAR multi-years strain rate estimate [Jolivet et al., 2011]. We did a spatial bilinear
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interpolation for the delay function of ERA data and then a spline interpolation for
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altitude to predict the delay map for each single image [Jolivet et al., 2011]. Differential
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delay maps corresponding to each interferogram can be estimated by comparing these
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delay maps. The residual orbital components due to orbital errors can still be significant,
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particularly when studying low rates of deformation. Such errors are reduced from the
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wrapped interferometric phase by searching for a best fitting ramp in range, followed by
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a best fitting ramp in azimuth. We do not try to correct for more complex orbital error at
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that stage to avoid removing the tectonic signal. Indeed the interseismic signal related to
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the SFS that we want to estimate should be mainly localized within a few tens of
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kilometers from the fault zone as shown on other strike-slip fault studies (e.g. Wright et
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al. 2001, Wang et al 2009, Jolivet et al 2014). In this case, using a simple phase ramp
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orbital error model estimated over 300km-long track minimize the risk of removing
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tectonics signal. Comparison with independent GPS data will be done to check a-
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posteriori the validity of this assumption. The interferometric phase component due to
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DEM errors varies with the perpendicular baseline between the two acquisitions and is
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estimated from wrapped interferograms following the methodology from Ducret et al.,
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[2013]. The main interest of this correction is to reduce the phase variance for helping the
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unwrapping process. Some residual DEM errors may remain after this step; they are re-
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estimated during the time-series analyses step described hereafter. Then, interferograms
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are filtered using Goldstein's filter [Goldstein and Werner, 1998] and unwrapped using a
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branch-cut algorithm [Goldstein et al., 1988]. A careful visual inspection of the corrected
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interferograms indicates that there is no evidence for any sharp creep deformation signal
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located along the faults over the 2003 to 2010 period. To investigate the long wavelength
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tectonic signal due to interseismic strain accumulation, a time series analysis of the
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selected images has been performed on a pixel basis in order to enhance the signal to
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noise ratio.
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Reference:
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Doin, M. P., C. Lasserre, G. Peltzer, O. Cavalié, and C. Doubre (2009), Corrections of stratified
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tropospheric delays in SAR interferometry: Validation with global atmospheric models, J. Appl.
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Geophys., 69, 35–50.
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Ducret, G., M.-P. Doin, R. Grandin, C. Lasserre, S. Guillaso (2013), DEM Corrections Before Unwrapping
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in a Small Baseline Strategy for InSAR Time Series Analysis, Geoscience and Remote Sensing
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Letters, IEEE, Volume: PP , Issue: 99, 1 – 5.
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Goldstein, M. R., A. H. Zebker, and C. L. Werner (1988), Satellite radar interferometry: Two-dimensional
phase unwrapping, Radio Sci., 23(4), 713–720.
Goldstein, M. R., and L. C. Werner (1998), Radar interferogram filtering for geophysical applications,
Geophys. Res. Lett., 25(21), 4035–4038.
Jolivet, R., R. Grandin, C. Lasserre, M.-P.Doin, and G. Peltzer (2011), Systematic InSAR tropospheric
phase delay corrections from globa l meteorological reanalysis data, Geophys. Res. Lett., 38, L17311.
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Jolivet, R., P. S. Agram, N. Y. Lin, M. Simons, M. P. Doin, G. Peltzer, and Z. Li (2014), Improving InSAR
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geodesy using global atmospheric models. Journal of Geophysical Research: Solid Earth, 119(3),
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2324-2341.
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Schmidt, D. A., and R. Bürgmann (2003), Time-dependent land uplift and subsidence in the Santa Clara
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valley, California, from a large Interferometric synthetic aperture radar data set, J. Geophys. Res.,
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108(B9), 2416
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Wang, H., T. J. Wright, and J. Biggs (2009), Interseismic slip rate of the northwestern Xianshuihe fault
from InSAR data, Geophys. Res. Lett. 36, no. 3).
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Wright, T., B. Parsons, and E. Fielding (2001), Measurement of interseismic strain accumulation across the
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North Anatolian Fault by satellite radar interferometry, Geophys. Res. Lett. 28, no. 10, 2117-2120.
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Figures:
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Figure S1. Diagrams of all processed interferograms selected using a maximum
perpendicular baseline criterion (500m). Perpendicular baselines with respect to the July
2003 orbit for descending track D020 (left plot), and January 2004 orbit for ascending
track A156 (right plot), are plotted as function of acquisition dates. Track D020 uses 22
images that are combined into 63 interferograms and track A156 uses 23 images that
are combined into 51 interferograms.
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Figure S2. Velocity versus altitude for the area covered by track D020 from which it is
clear that there is no correlation between altitude and deformation.
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Figure S3. Solution space plot for modeling the location of fault and slip rate for the two
tracks A156 (left) and D020 (right). The red stars are the best-solution of modeling the
location and slip rate of fault based on fix locking depth at 12km and the green stars are
the location of fault that we choose for the 2d modeling of slip rate and locking depth.
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Figure S4. One example of a perturbed dataset: a) the original interferogram, b) the
perturbation image c) perturbed data d) the RMS misfit for perturbed data.
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Figure S5. Solution space plot for modeling the location of fault and slip rate for the two
tracks A156 (left) and D020 (right). The red stars are the best-solution of modeling the
location and slip rate of fault based on _x locking depth at 12km and the green stars are
the location of fault that we choose for the 2d modeling of slip rate and locking depth.
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Tables:
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Table S1. Correlation coefficients between LOS mean velocity map and topography,
estimated separately on the northern and southern part (with respect to the fault zone) of
each InSAR tracks. These values are all low, indicating that there is no significant
correlation between the mean velocity and topography.
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Northern part
Southern part
0.1643
0.0733
-0.4874
-0.129
correlation coefficient track
D020
correlation coefficient track
A156
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