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Estimating the slip rate on the north Tabriz fault (Iran) from InSAR measurements with tropospheric correction using 3D ray tracing technique
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ScienceDirect Advances in Space Research 64 (2019) 2199–2208 www.elsevier.com/locate/asr
Estimating the slip rate on the north Tabriz fault (Iran) from InSAR measurements with tropospheric correction using 3D ray tracing technique Saeid Haji-Aghajany, Behzad Voosoghi ⇑, Yazdan Amerian Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran Received 17 March 2019; received in revised form 27 June 2019; accepted 15 August 2019 Available online 23 August 2019
Abstract In this paper, interseismic deformation across the north Tabriz fault (NTF) using 17 ASAR/ENVISAT acquisitions on a single track for the period 2003–2010 have been investigated. One of the main limiting factors on the accuracy of interferometric synthetic aperture radar (InSAR) measurements comes from phase propagation delays through the troposphere. In order to retrieve millimeter velocities of interseismic deformations, it is necessary to improve the tropospheric corrections and correct interferograms. For this purpose, the 3D ray tracing technique based on eikonal equations has been used to estimate the tropospheric corrections. The corrected InSAR measurements are used to derive the interseismic displacement velocity field of the study area. The obtained velocity field has enabled us to accurately estimate the slip rate and locking depth for the NTF, using a simple elastic dislocation model. The numerical achievements show a slip rate of 5.6 ± 0.15 mm/yr below a locking depth of 14.5 ± 0.67 km for the NTF. Generally, the results of this paper are confirmed by the previous studies of the NTF parameters and some differences are due to this paper applied method for tropospheric corrections. Ó 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
Keywords: InSAR; Tropospheric delay; 3D ray tracing technique; Locking depth; Slip rate
1. Introduction The tectonics of Iran is mainly the result of the motion between the Arabian and Eurasia plates, which is converged in the Tabriz area with the rate of ~20 mm/year (Vernant et al., 2004). This convergence is distributed between the shortening across the Zagros Mountains, the internal deformation accommodated by the large strikeslip faults in central Iran, and the shortening of the Alborz Mountains. The Tabriz area is part of the complex tectonic system due to the interaction between Arabia, Anatolia and Eurasia and comprising the north Anatolian fault, the east ⇑ Corresponding author.
E-mail addresses: [email protected] (S. Haji-Aghajany), [email protected] (B. Voosoghi), [email protected] (Y. Amerian). https://doi.org/10.1016/j.asr.2019.08.021 0273-1177/Ó 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
Anatolian fault and Caucasus Mountains which is the bounds of the Zagros Mountains. Part of the northward motion of Arabia is transferred to Anatolia by this complex system of faults (Jackson, 1992) and, because of the oblique orientation of the motion relative to the mountain range, results in the partitioning of the motion between shortening in the Caucasus and right-lateral strike-slip motion along the Tabriz fault (Jackson, 1992). More locally, the NTF is a clear WNW–ESE trending strikeslip fault that runs for more than 100 km between the lake Urumieh and the Talesh system. It represents the southeastern termination of the Gailatu-Siah-Chesmeh-Khoy fault (Karakhanian et al., 2012), which merges at that place with the Maku and the Nakhichevan dextral strike-slip faults and continues farther east. To the west, the NTF joins the EW trending right-lateral strike-slip Tasuj faults and the north-dipping Sufian reverse fault, which are
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bounds of the lake Urumieh to the north. To the east, it merges with the north Bozghush fault and the south Bozghush north-dipping reverse fault located on both sides of the Bozghush Range. InSAR is a robust method for analyzing the interseismic deformation (Hooper, 2008). The amount of interseismic deformation is usually less than a few tens of millimeters per year. Increasing the accuracy of displacement field increases the accuracy of the fault parameters such as slip rate and locking depth. Therefore, eliminating various errors that are affecting the accuracy of the InSAR results is very important to determine the fault parameters accurately (Zebker et al., 1997). InSAR measurements of interseismic displacement rates of mm/yr require multiple interferograms due to the small signal-to-noise ratio (Wright et al., 2004; Fialko, 2006; Tymofyeyeva and Fialko, 2015). The main limitation of radar interferometry in measuring ground displacements comes from phase propagation in the troposphere (Zebker et al., 1997; Jolivet et al., 2014; Bekaert et al., 2015). Tropospheric propagation delays convey valuable information and could be interpreted as geophysical signals. These signals affect all of the pixels in radar acquisitions and computed displacement. Numerous studies have focused on the mitigation of tropospheric effects. Numerous studies have been addressed the indirect corrections of tropospheric delays empirically based on the stacking independent data (Peltzer et al., 2001; Schmidt et al., 2005), by characterizing the statistical properties of phase delay patterns (Emardson et al., 2003), or analyzing the delay and elevation correlations observed in interferograms of areas far from the main deformation (Remy et al., 2003; Cavalie´ et al., 2008; Elliott et al., 2008). This approaches are utilized to construct covariance matrices of observables and also to separate stochastic noise from ground motion signal (Emardson et al., 2003). Tropospheric delay estimation using external data is one of the most widely used methods for computing the required tropospheric corrections in InSAR displacement fields (Ferretti et al., 2001; Berardino et al., 2002; Li et al., 2006; Dee et al., 2011; Jolivet et al., 2014; Yu et al., 2017; Cong et al., 2018). In these methods, the tropospheric delay have been computed in zenith direction and the incidence angle of the signal have been used as a mapping function to convert the computed tropospheric delay along zenith to the line of sight (LOS) direction. The 3D ray tracing technique is one of the ways to compute the effect of the troposphere (Hobiger et al., 2010). This method does not require a mapping function because it reconstructs the actual path of a signal in 3D space. According to previous studies, this method can be considered as one of the strongest methods to calculate the tropospheric delay and to reconstruct the path of a ray. (Hobiger et al., 2010; Haji-Aghajany and Amerian, 2017; Haji-Aghajany and Amerian, 2018). The purpose of this paper is quantification and validation of the tropospheric effects on the fault
parameters estimation. Herein, the 3D ray tracing technique based on eikonal equations and meteorological data is used for tropospheric delays estimation. To investigate the importance of using the 3D ray tracing technique, the tropospheric effects are also estimated using linear phase/ elevation ratio. Finally, the obtained displacements from two tropospheric correction methods are used to directly estimate the slip rate and locking depth on the NTF using a simple elastic dislocation model. 2. InSAR analysis Recently developed InSAR processing approaches that use data from multiple acquisitions to retrieve deformation time series include Permanent Scatterer InSAR (PSI) and Small Baseline InSAR (SBAS) methods. PSI methods work by identifying the ground resolution elements that are dominated by a single scatterer (Hooper, 2008). A persistent scatterer (PS) exhibits reduced baseline and temporal decorrelation due to its stable, point-like scattering mechanism. In contrast, small baseline methods assume a distributed scattering mechanism and use common band spectral filtering and complex multi-looking of the interferograms in order to improve the signal-to-noise ratio (SNR) (Sousa et al., 2011). A relatively new and advanced technique, i.e., multi temporal InSAR technique has been developed. This technique uses multi temporal stacks of radar acquisitions to generate time series of ground deformations (Ferretti et al., 2001; Berardino et al., 2002). To investigate the surface motions in the region, the Stanford Method technique (StaMPS/MTI) have been used (Hooper, 2008). This method combine both sets of PSI and SBAS results to improve phase unwrapping and the spatial sampling of the signal of interest (Sousa et al., 2011). 3. Tropospheric effects and their mitigation Interferometric radar analysis will be done using commentary exact time delay and differential phase shifts. When the signals propagate through the troposphere, which has a slightly higher index of refraction, the velocity is lowered slightly and the observations contaminated due to spatially variable delays (Jolivet et al., 2014). The variations of delay can cause a phase gradient in InSAR measurements. These variations are due to the changes of pressure, temperature and water vapor in two acquisitions time. Due to the low temporal variations of pressure and temperature, the temporal variations of water vapor can be considered as the main reason of the tropospheric effects in an interferogram (Hanssen, 1998). In order to achieve an accurate displacement of an area, the tropospheric effect must be removed from interferograms. In previous studies, many methods have been proposed by various researchers in order to model and compute the effect of troposphere in a radar signal or in an interferogram (Peltzer et al., 2001; Remy et al., 2003; Emardson et al., 2003; Schmidt et al.,
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2005; Li et al., 2006; Cavalie´ et al., 2008; Elliott et al., 2008; Dee et al., 2011; Jolivet et al., 2014; Yu et al., 2017; Cong et al., 2018). In this paper the new and powerful 3D ray tracing technique is used to calculate the tropospheric delay for different radar signals in different times. The principles of this approach is based on the 3D reconstruction of the ray and the calculation of the actual distance traveled by it (Hofmeister, 2016; Haji-Aghajany and Amerian, 2017). The tropospheric delay is measurable by comparing the geometric distance and the actual distance traveled by the ray. Finally, the magnitude of the tropospheric effects in each interferogram can be computed by comparing the delays obtained for the two master and slave acquisitions. This method is based on the Eikonal equations. The Hamiltonian formalism of these equations is as follows: o a 1n a H ð! r ; rLÞ ðrL:rLÞ2 nð! rÞ ¼0 a
ð1Þ
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Fig. 2. GPS stations map of the study area (Rizza et al., 2013). Groundwater well are marked with blue circles. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 1. The geographical location of the study area and its digital elevation model (DEM).
Table 1 Specifications of the radar acquisitions. Mission
Sensor
Product
Track
Swath
Footprint longitude (deg.)
Footprint latitude (deg.)
Pass
Look Angle (deg.)
ENVISAT
ASAR
ASA_IM__0P
49
I2
45.93 46.89
38.59 37.61
D
23.5
47.16 45.68
38.39 37.40
Table 2 East, north and vertical GPS velocity components (EVel, NVel, VVel.) and 1runcertainties (rE, rN, rV). Data
Longitude (deg)
Latitude (deg)
E Vel (mm/yr)
N Vel (mm/yr)
rE (mm/yr)
rN (mm/yr)
GPS GPS GPS GPS GPS GPS GPS GPS GPS GPS GPS GPS GPS
46.225 46.257 46.285 46.389 46.453 46.470 46.724 46.848 46.852 46.870 46.885 46.973 47.116
37.822 37.985 37.896 38.091 38.137 38.231 38.401 37.873 37.734 37.790 37.950 38.026 38.156
0.55 0.72 0.02 0.88 2.86 4.16 4.89 1.02 0.18 0.23 2.29 1.17 3.18
13.36 12.89 13.45 12.59 11.64 10.82 10.69 12.31 14.41 14.13 14.36 8.88 12.30
0.61 0.63 0.57 0.69 0.59 0.63 0.58 0.59 0.74 0.57 0.60 0.95 0.55
0.63 0.65 0.59 0.72 0.61 0.65 0.60 0.61 0.78 0.59 0.58 1.00 0.57
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where ! r is the position vector, L is the path length, rL is the components of signal directions and nð! r Þ is the refractive index. Details of this method can be studied in previous researches (Hobiger et al., 2010; Hofmeister, 2016; Haji-Aghajany and Amerian, 2017). One of the most common and traditional ways to estimate the tropospheric effects is using linear phase/elevation ratio. This method is based on the linear relation between tropospheric phase and elevation. This relation is established by applying a constant value. More about this method can be founded in previous studies (Cavalie´ et al., 2008). In order to investigate the effects of using the 3D ray tracing technique, the obtained results from this approach
are compared to the obtained result from linear phase/elevation ratio. 4. Study area and data set The north west of Iran is considered as the study area. This area is located on the Turkish–Iranian plateau where ongoing Arabian–Eurasian convergence is partitioned between thrusts and strike slip faults in northwest of Iran and east of Turkey (Copley and Jackson, 2006). NTF and Sahand Volcano (SV) are the most important geodynamic phenomena in this region. Historical documents demonstrate that north west of Iran and east of Turkey have been struck by numerous destructive earthquakes.
Fig. 3. Perpendicular and temporal baselines of the interferograms.
Fig. 4. Standard deviation comparison of unwrapped interferograms corrected from the 3D ray tracing technique and corrected from the linear phase/ elevation ratio.
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Fig. 5. Example of interferogram and tropospheric correction across the NTF from radar acquisitions on 01-16-2004 and 04-08-2004.
Fig. 6. The distribution of PSs values of the selected interferogram.
One of the destructive events is earthquake in city of Van at October 23, 2011 (Mw 7.1) associated with reverse slip on a NE-SW trending fault. The documents suggest that the last two large earthquakes on the NTF took place within 60 years in the 18th century on its adjacent segments. The first event occurred in 1721 and had a magnitude of 7.3 Mw. Initiating at 37.90°N, 46.70°E (Berberian, 1994), it
ruptured to the southeast along the NTF more than 39 km. The second earthquake in 1780 (7.4) broke its northwestern section with an epicenter located at 38.12° N, 46.29°E and producing a surface rupture of 100 km (Berberian, 1994). Previous studies in this region, yields an average slip rate of 7.7 ± 2.5 mm/yr with a locking depth of 15.8 ± 10.8 km (Rizza et al., 2013; Karimzadeh et al., 2013; Haji-Aghajany et al., 2017). The location of the study area can be seen in Fig. 1. To perform the InSAR analysis, we use the 17 ENVISAT radar acquisitions between 2003 and 2010. The features of the acquisitions are visible in Table 1. In this research, the values determined for the continuous GPS in northwest of Iran are used to validate the obtained displacement rates. The velocities and 1r confidence uncertainties of these stations were estimated in ITRF2008 and then the Eurasian reference frame was defined by minimizing the horizontal velocities of IGS stations located in Europe and central Asia (Rizza et al., 2013). For this comparison, the GPS measurements should be project on to the LOS vector as follows (Hanssen, 2001):
Fig. 7. Mean LOS velocity field between 2003 and 2010 in the area plotted on aster shaded relief image. These displacement velocity fields have been computed from interferograms corrected by 3D ray tracing technique (right) and linear phase/elevation ration (left).
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Fig. 8. Time series of groundwater table measurements and time series analysis of InSAR data in the area.
GPSLOS ¼ ðGPSup cosðhÞÞ 3p GPSnorth cos c sinðhÞ 2 3p GPSeast sin c sinðhÞ 2
ð2Þ
GPSLOS depends only on the satellite’s orbit angle relative to true north (c), h is the incidence angle of the radar wave, GPSnorth, GPSeast and GPSup are the north, east and vertical components of displacement observed by the GPS station, respectively. The GPS velocities and their uncertainties are given in Table 2. Piezometric data of a groundwater well which collected by the regional water organization, are used to validate the time series of InSAR data. The location of the GPS stations and the groundwater well can be seen in Fig. 2. The 3D ray tracing technique need to meteorological parameters such as pressure, temperature and water vapor pressure. These parameter are accessible through different
methods, observations and meteorological models (Merrikhpour and Rahimzadegan, 2017a,b; Rahimzadegan and Mobasheri, 2011). The global meteorological models used for 3D ray tracing of radar signals. For each acquisition date, the ERA-I output which is the closest to the radar acquisition time is selected. The temperature, water vapor and dry air partial pressure provided at each pressure level has been interpolated using Kriging and spline.
5. InSAR analysis 30 interferograms have produced using 17 ASAR/ENVISAT acquisitions between 2003 and 2010 on track 49. Temporal and perpendicular baselines of considered interferograms is visible in Fig. 3. Before estimating the displacement velocity fields, the obtained interferograms have corrected using two mentioned tropospheric correction methods and corrected interferograms have
Fig. 9. Comparison between InSAR and GPS LOS. The displacement velocity is obtained in the coordinates of 8 GPS stations in the area using kriging interpolation (radius <100 m) for two methods.
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compared with each other (Fig. 4). As can be seen in Fig. 4, the standard deviation of corrected interferograms from 3D ray tracing technique is lower than that for corrected interferograms from linear phase/elevation ratio. Sample of the interferograms which obtained from two acquisitions on 01-16-2004 and 04-08-2004 and its tropospheric correction map from 3D ray tracing is shown in Fig. 5. According to the residual map it can be said that most displacements computed in this interferogram are related to the tropospheric effects. This result can be seen in more detail in Fig. 6. After applying the obtained tropospheric corrections by two methods on the interferograms, the displacement velocity filed of the area have been estimated (Fig. 7). The time series obtained from piezometric data collected by the regional water organization have been used to validate the obtained results (Fig. 8). According to the results in Fig. 8, it can be said that the main reason of the subsidence in this area is related to the seasonal high ground water fluctuations. The InSAR time series from 3D ray tracing are more consistent with the piezometric data than the InSAR time series from linear phase/elevation ratio. In order to more comprehensive study the displacement in the area; it is necessary to compare the InSAR velocity fields with the GPS data. The result of this comparison is shown in Fig. 9. These results demonstrates that GPS outputs is close to the InSAR velocity fields from 3d ray tracing technique. In order to better illustrate the effect of using 3D ray tracing technique, the three profiles (see Fig. 7) have been considered on corrected InSAR velocity filed. Two profiles have been picked up in the left block and one profile has been picked up in the right block because the density of the PSs on the right side of the displacement field is less than the left side. These profiles have been applied to better illustrate the displacement and to compare GPS velocities projected along the satellite LOS with the InSAR velocity fields (Fig. 10). In along of the profile P1, there is a quite agree between results from distances of 15–35 km. The InSAR and GPS indicate a gradual velocity variation of 2 ± 1 mm yr1, most of it, accommodated over a distance of 15 km. The profile P2 reveals a gradual LOS velocity similarly to P1 and the minimum difference between the results is about 2 mm yr1 (after 25 km). In the profile P2 (after 20 km) the difference between results is about 4 mm yr1. In the area of the profile P3 the density of PSs is less than other sides. Therefore, the reliability of the results of this profiles is less than the other profiles. In this profile the difference between obtained displacement velocities is about 2 mm yr1 (before 10 km). 6. Final inversion for slip rate After the calculation the occurred displacement the slip rate and locking depth are estimated using a screw dislocation model (Savage and Burford, 1973). To estimate the
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Fig. 10. Distribution of InSAR velocity fields (black circles) profiles (1, 2 and 3 from up to down) after tropospheric correction from 3D ray tracing technique. To better illustrate the difference between results, the data along with the standard deviation have been plotted on each measured point (green bar: correction from 3D ray tracing technique and red bar: correction from linear phase/elevation ratio). Topography along the profile shown with a brown line and blue stars indicate the GPS stations. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 11. Fault parameters of the interseismic deformation using a Monte-Carlo method. Frequency contours of slip rate and locking depth for LOS velocity after tropospheric correction from 3D ray tracing technique (left) and linear phase/elevation ratio (right). The frequencies are calculated by counting the number of the solutions on a regular grid (1 km by 1 mm). These are then smoothed and shown in color. The blue contour indicates frequencies of 2%, which approximately enclose 95% of all the solutions. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 12. Obtained results compared to the results of previous researches.
uncertainties and covariance between slip rate and locking depth, the Monte-Carlo simulation method has been used (Wright et al., 2002). It should be noted that the results from the Monte-Carlo analysis are not dependent on the locking depth used in construction of the initial rate map. The slip rate obtained from linear phase/elevation ratio method is 7.5 ± 0.19 mm/yr with a locking depth of 16.1 ± 0.94 km at the 95% confidence level. The slip rate
obtained from 3D ray tracing technique is 5.6 ± 0.15 mm/yr with a locking depth of 14.5 ± 0.67 km at the 95% confidence level (Fig. 11). There is a strong trade-off between slip rate and locking depth (Wright et al., 2004). The rigidity contrast and fault offset in a 2D screw dislocation model are incorporate to describe asymmetric surface velocity and use a Monte-Carlo method as above to find the best-fitting parameters. There is a strong
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trade-off between these two parameters. An optimal factor of rigidity contrast is obtained if fault location is fixed from the mapped traces. Alternatively, the observations can be fit by shifting the deep fault location. Finally, the results of this study have compared with some of the recent studies of the NTF such as Hessami et al. (2003); Masson et al. (2006); Djamour et al. (2011); Rizza et al. (2013); Karimzadeh et al. (2013) and HajiAghajany et al. (2017) (Fig. 12). The comparison shows that the correction of tropospheric effects on interferograms cause significant changes in the estimation of the NTF parameters. These differences are due to the described method for tropospheric corrections. Accurate estimation of fault parameters are directly related to the accuracy of displacement field. As a result, removing the effect of the troposphere from the interferograms is necessary to achieve more accurate displacement field and fault parameters using InSAR. 7. Conclusions InSAR is widely used in earthquake studies due to its very high spatial resolution compared to GPS. Recently, analysis of interseismic deformation and determination of the fault parameters especially locking depth and slip rate has been subject of many researches due to improved accuracy of the InSAR displacement fields. Detecting the small amount of interseismic deformation, which is about few tens of millimeters, needs accurate elimination of intruder factors. This elimination is very important in order to calculate the fault parameters accurately. One of the main sources of errors of InSAR, for measuring fine ground displacements, is related to troposphere effect on passing signal. In this study, the benefits of using outputs of 3D ray tracing technique was presented to correct interferograms. These corrections reduce biases in estimations of mean velocity field and assist the unwrapping process. For this purpose, 17 ASAR/ENVISAT acquisitions between 2003 and 2010 on track 49 and produce 30 interferograms were used. The interferograms were corrected using 3D ray tracing technique and a common method that uses linear phase/elevation ratio. Then the ability of two correction methods were compared using GPS observations and piezometric data. After tropospheric correction of the interferograms from 3D ray tracing technique, mean displacement velocity of the InSAR data ranges is between 16 and 4 mm yr1. The obtained time series and velocity fields were compared to the piezometric data and GPS observations. This study demonstrates that accurate tropospheric corrections using 3D ray tracing technique have high importance in InSAR analysis. The InSAR data from linear phase/elevation ratio have demonstrated that the NTF has a slip rate of 7.5 ± 0.19 mm/yr with a locking depth of 15.7 ± 0.94 km and InSAR data from 3D ray tracing technique have demonstrated a slip rate of 5.1 ± 0.15 mm/yr with a locking depth of 12.8 ± 0.67 km.
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Comparing these results with the previous studies indicates some differences which are due to different method of applying tropospheric corrections. Acknowledgments Authors would like to appreciate the European Space Agency (ESA), the National Cartographic Center (NCC) of Iran and ECMWF for providing the ASAR/ENVISAT acquisitions, GPS and ERA-I data, respectively. References Bekaert, D.P.S., Hooper, A., Wright, T.J., 2015. A spatially variable power law tropospheric correction technique for InSAR data. J. Geophys. Res. Solid Earth 120. https://doi.org/10.1002/ 2014JB011558. Berardino, P., Fornaro, G., Lanari, R., Sansosti, E., 2002. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferometry. Berberian, M., 1994. Natural hazards and the first earthquake catalogue of Iran. Int. Inst. Earthquake Eng. Seismol. 1, 266–270. Cavalie´, O., Lasserre, C., Doin, M.P., Peltzer, G., Sun, J., Xu, X., Shen, Z. K., 2008. Measurement of interseismic strain across the Haiyuan fault (Gansu, China), by InSAR. Earth Planet. Sci. Lett. 275 (3–4), 246–257. https://doi.org/10.1016/j.epsl.2008.07.057. Copley, A., Jackson, J., 2006. Active tectonics of the Turkish-Iranian Plateau, Tectonics, 25, TC6006, doi: 10.1029/2005TC001906. Cong, X., Balss, U., Rodriguez Gonzalez, F., Eineder, M., 2018. Mitigation of tropospheric delay in SAR and InSAR using NWP data: its validation and application examples. Rem. Sens. 10, 1515. Dee, D.P., and 35 co-authors., 2011, The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. R. Meteorol. Soc. 137(656), 553–597, doi: 10.1002/qj.828. Djamour, Y., Vernant, P., Nankali, H.R., Tavakoli, F., 2011. NW Iraneastern Turkey present-day kinematics: results from the Iranian permanent GPS network. Earth Planet. Sci. Lett. 307, 27–34. https:// doi.org/10.1016/j.epsl.2011.04.029. Elliott, J.R., Biggs, J., Parsons, B., Wright, T.J., 2008. InSAR slip rate determination on the AltynTagh Fault, northern Tibet, in the presence of topographically correlated atmospheric delays. Geophys. Res. Lett. 35, L12309. Emardson, T.R., Simons, M., Webb, F.H., 2003. Neutral atmospheric delay in interferometric synthetic aperture radar applications: statistical description and mitigation. J. Geophys. Res. 108 (B5), 2231. https://doi.org/10.1029/2002JB001781. Ferretti, A., Prati, C., Rocca, F., 2001. Permanent scatters in SAR interferometry. IEEE Trans. Geosci. Remote Sens. 39 (1), 8–20. Fialko, Y., 2006. Interseismic strain accumulation and the earthquake potential on the southern San Andreas fault system. Nature 441, 968– 971. https://doi.org/10.1038/nature04797. Haji-Aghajany, S., Voosoghi, B., Yazdian, A., 2017. Estimation of north Tabriz fault parameters using neural networks and 3D tropospherically corrected surface displacement field. Geomatics, Nat. Hazards Risk. https://doi.org/10.1080/19475705.2017.1289248. Haji-Aghajany, S., Amerian, Y., 2017. Three dimensional ray tracing technique for tropospheric water vapor tomography using GPS measurements. J. Atmos. Sol.-Terrestrial Phys. 164, 81–88. doi: 10.1016/j.jastp.2017.08.003. Haji-Aghajany, S., Amerian, Y., 2018. Hybrid regularized GPS tropospheric sensing using 3-D ray tracing technique. IEEE Geosci. Rem. Sens. Lett. 15, 1475–1479. https://doi.org/10.1109/LGRS.2018.2853183. Hanssen, R., 1998. Atmospheric heterogeneities in ERS tandem SAR interferometry. DEOS Report No.98.1, Delft University press: Delft, the Netherlands.
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ScienceDirect Advances in Space Research 64 (2019) 2199–2208 www.elsevier.com/locate/asr
Estimating the slip rate on the north Tabriz fault (Iran) from InSAR measurements with tropospheric correction using 3D ray tracing technique Saeid Haji-Aghajany, Behzad Voosoghi ⇑, Yazdan Amerian Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran Received 17 March 2019; received in revised form 27 June 2019; accepted 15 August 2019 Available online 23 August 2019
Abstract In this paper, interseismic deformation across the north Tabriz fault (NTF) using 17 ASAR/ENVISAT acquisitions on a single track for the period 2003–2010 have been investigated. One of the main limiting factors on the accuracy of interferometric synthetic aperture radar (InSAR) measurements comes from phase propagation delays through the troposphere. In order to retrieve millimeter velocities of interseismic deformations, it is necessary to improve the tropospheric corrections and correct interferograms. For this purpose, the 3D ray tracing technique based on eikonal equations has been used to estimate the tropospheric corrections. The corrected InSAR measurements are used to derive the interseismic displacement velocity field of the study area. The obtained velocity field has enabled us to accurately estimate the slip rate and locking depth for the NTF, using a simple elastic dislocation model. The numerical achievements show a slip rate of 5.6 ± 0.15 mm/yr below a locking depth of 14.5 ± 0.67 km for the NTF. Generally, the results of this paper are confirmed by the previous studies of the NTF parameters and some differences are due to this paper applied method for tropospheric corrections. Ó 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
Keywords: InSAR; Tropospheric delay; 3D ray tracing technique; Locking depth; Slip rate
1. Introduction The tectonics of Iran is mainly the result of the motion between the Arabian and Eurasia plates, which is converged in the Tabriz area with the rate of ~20 mm/year (Vernant et al., 2004). This convergence is distributed between the shortening across the Zagros Mountains, the internal deformation accommodated by the large strikeslip faults in central Iran, and the shortening of the Alborz Mountains. The Tabriz area is part of the complex tectonic system due to the interaction between Arabia, Anatolia and Eurasia and comprising the north Anatolian fault, the east ⇑ Corresponding author.
E-mail addresses: [email protected] (S. Haji-Aghajany), [email protected] (B. Voosoghi), [email protected] (Y. Amerian). https://doi.org/10.1016/j.asr.2019.08.021 0273-1177/Ó 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
Anatolian fault and Caucasus Mountains which is the bounds of the Zagros Mountains. Part of the northward motion of Arabia is transferred to Anatolia by this complex system of faults (Jackson, 1992) and, because of the oblique orientation of the motion relative to the mountain range, results in the partitioning of the motion between shortening in the Caucasus and right-lateral strike-slip motion along the Tabriz fault (Jackson, 1992). More locally, the NTF is a clear WNW–ESE trending strikeslip fault that runs for more than 100 km between the lake Urumieh and the Talesh system. It represents the southeastern termination of the Gailatu-Siah-Chesmeh-Khoy fault (Karakhanian et al., 2012), which merges at that place with the Maku and the Nakhichevan dextral strike-slip faults and continues farther east. To the west, the NTF joins the EW trending right-lateral strike-slip Tasuj faults and the north-dipping Sufian reverse fault, which are
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bounds of the lake Urumieh to the north. To the east, it merges with the north Bozghush fault and the south Bozghush north-dipping reverse fault located on both sides of the Bozghush Range. InSAR is a robust method for analyzing the interseismic deformation (Hooper, 2008). The amount of interseismic deformation is usually less than a few tens of millimeters per year. Increasing the accuracy of displacement field increases the accuracy of the fault parameters such as slip rate and locking depth. Therefore, eliminating various errors that are affecting the accuracy of the InSAR results is very important to determine the fault parameters accurately (Zebker et al., 1997). InSAR measurements of interseismic displacement rates of mm/yr require multiple interferograms due to the small signal-to-noise ratio (Wright et al., 2004; Fialko, 2006; Tymofyeyeva and Fialko, 2015). The main limitation of radar interferometry in measuring ground displacements comes from phase propagation in the troposphere (Zebker et al., 1997; Jolivet et al., 2014; Bekaert et al., 2015). Tropospheric propagation delays convey valuable information and could be interpreted as geophysical signals. These signals affect all of the pixels in radar acquisitions and computed displacement. Numerous studies have focused on the mitigation of tropospheric effects. Numerous studies have been addressed the indirect corrections of tropospheric delays empirically based on the stacking independent data (Peltzer et al., 2001; Schmidt et al., 2005), by characterizing the statistical properties of phase delay patterns (Emardson et al., 2003), or analyzing the delay and elevation correlations observed in interferograms of areas far from the main deformation (Remy et al., 2003; Cavalie´ et al., 2008; Elliott et al., 2008). This approaches are utilized to construct covariance matrices of observables and also to separate stochastic noise from ground motion signal (Emardson et al., 2003). Tropospheric delay estimation using external data is one of the most widely used methods for computing the required tropospheric corrections in InSAR displacement fields (Ferretti et al., 2001; Berardino et al., 2002; Li et al., 2006; Dee et al., 2011; Jolivet et al., 2014; Yu et al., 2017; Cong et al., 2018). In these methods, the tropospheric delay have been computed in zenith direction and the incidence angle of the signal have been used as a mapping function to convert the computed tropospheric delay along zenith to the line of sight (LOS) direction. The 3D ray tracing technique is one of the ways to compute the effect of the troposphere (Hobiger et al., 2010). This method does not require a mapping function because it reconstructs the actual path of a signal in 3D space. According to previous studies, this method can be considered as one of the strongest methods to calculate the tropospheric delay and to reconstruct the path of a ray. (Hobiger et al., 2010; Haji-Aghajany and Amerian, 2017; Haji-Aghajany and Amerian, 2018). The purpose of this paper is quantification and validation of the tropospheric effects on the fault
parameters estimation. Herein, the 3D ray tracing technique based on eikonal equations and meteorological data is used for tropospheric delays estimation. To investigate the importance of using the 3D ray tracing technique, the tropospheric effects are also estimated using linear phase/ elevation ratio. Finally, the obtained displacements from two tropospheric correction methods are used to directly estimate the slip rate and locking depth on the NTF using a simple elastic dislocation model. 2. InSAR analysis Recently developed InSAR processing approaches that use data from multiple acquisitions to retrieve deformation time series include Permanent Scatterer InSAR (PSI) and Small Baseline InSAR (SBAS) methods. PSI methods work by identifying the ground resolution elements that are dominated by a single scatterer (Hooper, 2008). A persistent scatterer (PS) exhibits reduced baseline and temporal decorrelation due to its stable, point-like scattering mechanism. In contrast, small baseline methods assume a distributed scattering mechanism and use common band spectral filtering and complex multi-looking of the interferograms in order to improve the signal-to-noise ratio (SNR) (Sousa et al., 2011). A relatively new and advanced technique, i.e., multi temporal InSAR technique has been developed. This technique uses multi temporal stacks of radar acquisitions to generate time series of ground deformations (Ferretti et al., 2001; Berardino et al., 2002). To investigate the surface motions in the region, the Stanford Method technique (StaMPS/MTI) have been used (Hooper, 2008). This method combine both sets of PSI and SBAS results to improve phase unwrapping and the spatial sampling of the signal of interest (Sousa et al., 2011). 3. Tropospheric effects and their mitigation Interferometric radar analysis will be done using commentary exact time delay and differential phase shifts. When the signals propagate through the troposphere, which has a slightly higher index of refraction, the velocity is lowered slightly and the observations contaminated due to spatially variable delays (Jolivet et al., 2014). The variations of delay can cause a phase gradient in InSAR measurements. These variations are due to the changes of pressure, temperature and water vapor in two acquisitions time. Due to the low temporal variations of pressure and temperature, the temporal variations of water vapor can be considered as the main reason of the tropospheric effects in an interferogram (Hanssen, 1998). In order to achieve an accurate displacement of an area, the tropospheric effect must be removed from interferograms. In previous studies, many methods have been proposed by various researchers in order to model and compute the effect of troposphere in a radar signal or in an interferogram (Peltzer et al., 2001; Remy et al., 2003; Emardson et al., 2003; Schmidt et al.,
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2005; Li et al., 2006; Cavalie´ et al., 2008; Elliott et al., 2008; Dee et al., 2011; Jolivet et al., 2014; Yu et al., 2017; Cong et al., 2018). In this paper the new and powerful 3D ray tracing technique is used to calculate the tropospheric delay for different radar signals in different times. The principles of this approach is based on the 3D reconstruction of the ray and the calculation of the actual distance traveled by it (Hofmeister, 2016; Haji-Aghajany and Amerian, 2017). The tropospheric delay is measurable by comparing the geometric distance and the actual distance traveled by the ray. Finally, the magnitude of the tropospheric effects in each interferogram can be computed by comparing the delays obtained for the two master and slave acquisitions. This method is based on the Eikonal equations. The Hamiltonian formalism of these equations is as follows: o a 1n a H ð! r ; rLÞ ðrL:rLÞ2 nð! rÞ ¼0 a
ð1Þ
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Fig. 2. GPS stations map of the study area (Rizza et al., 2013). Groundwater well are marked with blue circles. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 1. The geographical location of the study area and its digital elevation model (DEM).
Table 1 Specifications of the radar acquisitions. Mission
Sensor
Product
Track
Swath
Footprint longitude (deg.)
Footprint latitude (deg.)
Pass
Look Angle (deg.)
ENVISAT
ASAR
ASA_IM__0P
49
I2
45.93 46.89
38.59 37.61
D
23.5
47.16 45.68
38.39 37.40
Table 2 East, north and vertical GPS velocity components (EVel, NVel, VVel.) and 1runcertainties (rE, rN, rV). Data
Longitude (deg)
Latitude (deg)
E Vel (mm/yr)
N Vel (mm/yr)
rE (mm/yr)
rN (mm/yr)
GPS GPS GPS GPS GPS GPS GPS GPS GPS GPS GPS GPS GPS
46.225 46.257 46.285 46.389 46.453 46.470 46.724 46.848 46.852 46.870 46.885 46.973 47.116
37.822 37.985 37.896 38.091 38.137 38.231 38.401 37.873 37.734 37.790 37.950 38.026 38.156
0.55 0.72 0.02 0.88 2.86 4.16 4.89 1.02 0.18 0.23 2.29 1.17 3.18
13.36 12.89 13.45 12.59 11.64 10.82 10.69 12.31 14.41 14.13 14.36 8.88 12.30
0.61 0.63 0.57 0.69 0.59 0.63 0.58 0.59 0.74 0.57 0.60 0.95 0.55
0.63 0.65 0.59 0.72 0.61 0.65 0.60 0.61 0.78 0.59 0.58 1.00 0.57
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where ! r is the position vector, L is the path length, rL is the components of signal directions and nð! r Þ is the refractive index. Details of this method can be studied in previous researches (Hobiger et al., 2010; Hofmeister, 2016; Haji-Aghajany and Amerian, 2017). One of the most common and traditional ways to estimate the tropospheric effects is using linear phase/elevation ratio. This method is based on the linear relation between tropospheric phase and elevation. This relation is established by applying a constant value. More about this method can be founded in previous studies (Cavalie´ et al., 2008). In order to investigate the effects of using the 3D ray tracing technique, the obtained results from this approach
are compared to the obtained result from linear phase/elevation ratio. 4. Study area and data set The north west of Iran is considered as the study area. This area is located on the Turkish–Iranian plateau where ongoing Arabian–Eurasian convergence is partitioned between thrusts and strike slip faults in northwest of Iran and east of Turkey (Copley and Jackson, 2006). NTF and Sahand Volcano (SV) are the most important geodynamic phenomena in this region. Historical documents demonstrate that north west of Iran and east of Turkey have been struck by numerous destructive earthquakes.
Fig. 3. Perpendicular and temporal baselines of the interferograms.
Fig. 4. Standard deviation comparison of unwrapped interferograms corrected from the 3D ray tracing technique and corrected from the linear phase/ elevation ratio.
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Fig. 5. Example of interferogram and tropospheric correction across the NTF from radar acquisitions on 01-16-2004 and 04-08-2004.
Fig. 6. The distribution of PSs values of the selected interferogram.
One of the destructive events is earthquake in city of Van at October 23, 2011 (Mw 7.1) associated with reverse slip on a NE-SW trending fault. The documents suggest that the last two large earthquakes on the NTF took place within 60 years in the 18th century on its adjacent segments. The first event occurred in 1721 and had a magnitude of 7.3 Mw. Initiating at 37.90°N, 46.70°E (Berberian, 1994), it
ruptured to the southeast along the NTF more than 39 km. The second earthquake in 1780 (7.4) broke its northwestern section with an epicenter located at 38.12° N, 46.29°E and producing a surface rupture of 100 km (Berberian, 1994). Previous studies in this region, yields an average slip rate of 7.7 ± 2.5 mm/yr with a locking depth of 15.8 ± 10.8 km (Rizza et al., 2013; Karimzadeh et al., 2013; Haji-Aghajany et al., 2017). The location of the study area can be seen in Fig. 1. To perform the InSAR analysis, we use the 17 ENVISAT radar acquisitions between 2003 and 2010. The features of the acquisitions are visible in Table 1. In this research, the values determined for the continuous GPS in northwest of Iran are used to validate the obtained displacement rates. The velocities and 1r confidence uncertainties of these stations were estimated in ITRF2008 and then the Eurasian reference frame was defined by minimizing the horizontal velocities of IGS stations located in Europe and central Asia (Rizza et al., 2013). For this comparison, the GPS measurements should be project on to the LOS vector as follows (Hanssen, 2001):
Fig. 7. Mean LOS velocity field between 2003 and 2010 in the area plotted on aster shaded relief image. These displacement velocity fields have been computed from interferograms corrected by 3D ray tracing technique (right) and linear phase/elevation ration (left).
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Fig. 8. Time series of groundwater table measurements and time series analysis of InSAR data in the area.
GPSLOS ¼ ðGPSup cosðhÞÞ 3p GPSnorth cos c sinðhÞ 2 3p GPSeast sin c sinðhÞ 2
ð2Þ
GPSLOS depends only on the satellite’s orbit angle relative to true north (c), h is the incidence angle of the radar wave, GPSnorth, GPSeast and GPSup are the north, east and vertical components of displacement observed by the GPS station, respectively. The GPS velocities and their uncertainties are given in Table 2. Piezometric data of a groundwater well which collected by the regional water organization, are used to validate the time series of InSAR data. The location of the GPS stations and the groundwater well can be seen in Fig. 2. The 3D ray tracing technique need to meteorological parameters such as pressure, temperature and water vapor pressure. These parameter are accessible through different
methods, observations and meteorological models (Merrikhpour and Rahimzadegan, 2017a,b; Rahimzadegan and Mobasheri, 2011). The global meteorological models used for 3D ray tracing of radar signals. For each acquisition date, the ERA-I output which is the closest to the radar acquisition time is selected. The temperature, water vapor and dry air partial pressure provided at each pressure level has been interpolated using Kriging and spline.
5. InSAR analysis 30 interferograms have produced using 17 ASAR/ENVISAT acquisitions between 2003 and 2010 on track 49. Temporal and perpendicular baselines of considered interferograms is visible in Fig. 3. Before estimating the displacement velocity fields, the obtained interferograms have corrected using two mentioned tropospheric correction methods and corrected interferograms have
Fig. 9. Comparison between InSAR and GPS LOS. The displacement velocity is obtained in the coordinates of 8 GPS stations in the area using kriging interpolation (radius <100 m) for two methods.
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compared with each other (Fig. 4). As can be seen in Fig. 4, the standard deviation of corrected interferograms from 3D ray tracing technique is lower than that for corrected interferograms from linear phase/elevation ratio. Sample of the interferograms which obtained from two acquisitions on 01-16-2004 and 04-08-2004 and its tropospheric correction map from 3D ray tracing is shown in Fig. 5. According to the residual map it can be said that most displacements computed in this interferogram are related to the tropospheric effects. This result can be seen in more detail in Fig. 6. After applying the obtained tropospheric corrections by two methods on the interferograms, the displacement velocity filed of the area have been estimated (Fig. 7). The time series obtained from piezometric data collected by the regional water organization have been used to validate the obtained results (Fig. 8). According to the results in Fig. 8, it can be said that the main reason of the subsidence in this area is related to the seasonal high ground water fluctuations. The InSAR time series from 3D ray tracing are more consistent with the piezometric data than the InSAR time series from linear phase/elevation ratio. In order to more comprehensive study the displacement in the area; it is necessary to compare the InSAR velocity fields with the GPS data. The result of this comparison is shown in Fig. 9. These results demonstrates that GPS outputs is close to the InSAR velocity fields from 3d ray tracing technique. In order to better illustrate the effect of using 3D ray tracing technique, the three profiles (see Fig. 7) have been considered on corrected InSAR velocity filed. Two profiles have been picked up in the left block and one profile has been picked up in the right block because the density of the PSs on the right side of the displacement field is less than the left side. These profiles have been applied to better illustrate the displacement and to compare GPS velocities projected along the satellite LOS with the InSAR velocity fields (Fig. 10). In along of the profile P1, there is a quite agree between results from distances of 15–35 km. The InSAR and GPS indicate a gradual velocity variation of 2 ± 1 mm yr1, most of it, accommodated over a distance of 15 km. The profile P2 reveals a gradual LOS velocity similarly to P1 and the minimum difference between the results is about 2 mm yr1 (after 25 km). In the profile P2 (after 20 km) the difference between results is about 4 mm yr1. In the area of the profile P3 the density of PSs is less than other sides. Therefore, the reliability of the results of this profiles is less than the other profiles. In this profile the difference between obtained displacement velocities is about 2 mm yr1 (before 10 km). 6. Final inversion for slip rate After the calculation the occurred displacement the slip rate and locking depth are estimated using a screw dislocation model (Savage and Burford, 1973). To estimate the
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Fig. 10. Distribution of InSAR velocity fields (black circles) profiles (1, 2 and 3 from up to down) after tropospheric correction from 3D ray tracing technique. To better illustrate the difference between results, the data along with the standard deviation have been plotted on each measured point (green bar: correction from 3D ray tracing technique and red bar: correction from linear phase/elevation ratio). Topography along the profile shown with a brown line and blue stars indicate the GPS stations. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 11. Fault parameters of the interseismic deformation using a Monte-Carlo method. Frequency contours of slip rate and locking depth for LOS velocity after tropospheric correction from 3D ray tracing technique (left) and linear phase/elevation ratio (right). The frequencies are calculated by counting the number of the solutions on a regular grid (1 km by 1 mm). These are then smoothed and shown in color. The blue contour indicates frequencies of 2%, which approximately enclose 95% of all the solutions. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 12. Obtained results compared to the results of previous researches.
uncertainties and covariance between slip rate and locking depth, the Monte-Carlo simulation method has been used (Wright et al., 2002). It should be noted that the results from the Monte-Carlo analysis are not dependent on the locking depth used in construction of the initial rate map. The slip rate obtained from linear phase/elevation ratio method is 7.5 ± 0.19 mm/yr with a locking depth of 16.1 ± 0.94 km at the 95% confidence level. The slip rate
obtained from 3D ray tracing technique is 5.6 ± 0.15 mm/yr with a locking depth of 14.5 ± 0.67 km at the 95% confidence level (Fig. 11). There is a strong trade-off between slip rate and locking depth (Wright et al., 2004). The rigidity contrast and fault offset in a 2D screw dislocation model are incorporate to describe asymmetric surface velocity and use a Monte-Carlo method as above to find the best-fitting parameters. There is a strong
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trade-off between these two parameters. An optimal factor of rigidity contrast is obtained if fault location is fixed from the mapped traces. Alternatively, the observations can be fit by shifting the deep fault location. Finally, the results of this study have compared with some of the recent studies of the NTF such as Hessami et al. (2003); Masson et al. (2006); Djamour et al. (2011); Rizza et al. (2013); Karimzadeh et al. (2013) and HajiAghajany et al. (2017) (Fig. 12). The comparison shows that the correction of tropospheric effects on interferograms cause significant changes in the estimation of the NTF parameters. These differences are due to the described method for tropospheric corrections. Accurate estimation of fault parameters are directly related to the accuracy of displacement field. As a result, removing the effect of the troposphere from the interferograms is necessary to achieve more accurate displacement field and fault parameters using InSAR. 7. Conclusions InSAR is widely used in earthquake studies due to its very high spatial resolution compared to GPS. Recently, analysis of interseismic deformation and determination of the fault parameters especially locking depth and slip rate has been subject of many researches due to improved accuracy of the InSAR displacement fields. Detecting the small amount of interseismic deformation, which is about few tens of millimeters, needs accurate elimination of intruder factors. This elimination is very important in order to calculate the fault parameters accurately. One of the main sources of errors of InSAR, for measuring fine ground displacements, is related to troposphere effect on passing signal. In this study, the benefits of using outputs of 3D ray tracing technique was presented to correct interferograms. These corrections reduce biases in estimations of mean velocity field and assist the unwrapping process. For this purpose, 17 ASAR/ENVISAT acquisitions between 2003 and 2010 on track 49 and produce 30 interferograms were used. The interferograms were corrected using 3D ray tracing technique and a common method that uses linear phase/elevation ratio. Then the ability of two correction methods were compared using GPS observations and piezometric data. After tropospheric correction of the interferograms from 3D ray tracing technique, mean displacement velocity of the InSAR data ranges is between 16 and 4 mm yr1. The obtained time series and velocity fields were compared to the piezometric data and GPS observations. This study demonstrates that accurate tropospheric corrections using 3D ray tracing technique have high importance in InSAR analysis. The InSAR data from linear phase/elevation ratio have demonstrated that the NTF has a slip rate of 7.5 ± 0.19 mm/yr with a locking depth of 15.7 ± 0.94 km and InSAR data from 3D ray tracing technique have demonstrated a slip rate of 5.1 ± 0.15 mm/yr with a locking depth of 12.8 ± 0.67 km.
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