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

Haji-Aghajany, Saeid; Voosoghi, Behzad; Yazdian, Amir

doi:10.1080/19475705.2017.1289248

Cited by 21 publications

(15 citation statements)

References 22 publications

(22 reference statements)

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“…Iran is generally a suitable target location for InSAR, given its relatively arid climate and sparse vegetation cover. InSAR time series methods have been applied to measure interseismic slip rates on a number of faults in the region, including the Ashkabad (5–12 mm/yr; Walters et al., 2013), Doruneh (2.5 ± 0.3 mm/yr; Mousavi et al., 2021), North Tabriz (6–10 mm/yr; Aghajany et al., 2017; Karimzadeh et al., 2013; Rizza et al., 2013; Su et al., 2016), Shahroud (4.75 ± 0.8 mm/yr; Mousavi et al., 2015), and the Minab‐Zendan‐Palami (10 mm/yr) and Sabzevaran‐Kahnuj‐Jiroft (7.4 mm/yr) fault systems (Peyret et al., 2009). However, InSAR has not previously been used to estimate the interseismic motion across the MRF, despite the potential of this technique to better constrain both the slip rate and locking depth of this important fault.…”

Section: Introductionmentioning

confidence: 99%

Interseismic Strain Accumulation Across the Main Recent Fault, SW Iran, From Sentinel‐1 InSAR Observations

2022

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The Main Recent Fault is a major right‐lateral strike‐slip fault in the western Zagros mountains of Iran. Recent geodetic and geological studies estimate a low slip rate of 1–6 mm/yr at an unknown depth which, when combined with a non‐ideal fault geometry, makes the Main Recent Fault a difficult but interesting target for InSAR analysis. This analysis would further cement the estimated slip rate and provide an opportunity of estimate the depth to the base of the locked seismogenic zone, both important constraints on the seismic hazard posed by the fault, as well as for understanding how oblique convergence is accommodated and partitioned across the Zagros. We use 200 Sentinel‐1 SAR images from the past 5 years, spanning two ascending and two descending tracks, to estimate the first InSAR‐derived slip rate and locking depth for a 300 km long section of the fault. We utilize two established processing systems, LiCSAR and LiCSBAS, to produce interferograms and perform time series analysis, respectively. We constrain north‐south motion using GNSS observations, decompose our InSAR line‐of‐sight velocities into fault‐parallel and vertical motion, and fit 1‐D screw dislocation models to three fault‐perpendicular profiles of fault‐parallel velocity, following a Bayesian approach to estimate the posterior probability distribution on the fault parameters. We estimate an interseismic slip velocity of 2.4 ± 1.2 mm/yr below a loosely constrained 14 km locking depth, the first such estimate for the fault, and discuss the challenges in constraining the locking depth for low magnitude interseismic signals.

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Section: Introductionmentioning

confidence: 99%

Interseismic Strain Accumulation Across the Main Recent Fault, SW Iran, From Sentinel‐1 InSAR Observations

2022

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“…where R d (287.05 J∕kg∕K) is the specific gas constant for dry air, R v (461.495 J∕kg∕K) is the specific gas constant for water vapor, Pðz 0 Þ is the surface pressure, e is the water vapor pressure, g m is the gravitational acceleration g averaged over the troposphere, and T is the temperature in K. 3,10 The LOS single path atmospheric delay, δL s LOS ðzÞ, neglecting refractive bending, varies with elevation for each acquisition date is 1,10 E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 4 ; 1 1 6 ; 4 3 1 δL s LOS ðzÞ ¼…”

Section: Integration Methodsmentioning

confidence: 99%

“…The values of the refractive index, which is necessary in this method, is derived from ERA-I data. 3 More details about this method algorithm and its application in tropospheric delay computation and reconstruction of signal path in tropospheric sensing could be found in previous research. 15,16 4 Numerical Results…”

Section: D Ray Tracing Methodsmentioning

confidence: 99%

“…On the contrary, the relative humidity varies both vertically and laterally over short distances. 3 Numerous studies have been addressed the corrections of tropospheric delays empirically based on the correlation between the delay and the topographic elevation of areas far from the main deformation, [4][5][6] stacking independent InSAR data, 7,8 and characterizing the statistical properties of phase delay patterns. 9 These approaches are utilized to construct covariance matrices of observations and also to separate stochastic noise from ground motion signal.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of InSAR tropospheric signal correction methods

Haji-Aghajany

Amerian

2020

J. Appl. Rem. Sens.

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Tropospheric signals are considered as one of the most important performance limitations to compute the deformations caused by earthquake, subsidence, volcano, and so on using interferometric synthetic aperture radar (InSAR) technique. Various correction methods have been proposed to reduce the effect of these signals in displacement fields in previous research works. Different types of correction methods are used to estimate the tropospheric signal on InSAR observations. For this purpose, meteorological data derived from ERA-Interim (ERA-I) data, Weather Research and Forecasting (WRF) model, and Advanced Synthetic Aperture Radar/ENVISAT acquisitions are used. ERA-I reanalysis data and a locally run WRF model are also used to compute the tropospheric corrections with integral of the air refractivity method, which is called integration method. Also, the ability of ray tracing techniques to reduce the effect of the tropospheric signal in unwrapped interferogram is compared with integration method. To carry out a comprehensive study, the effects of correction methods are studied in two different areas. The results of the ray tracing methods have a significant difference with the results obtained from integration method and are more efficient when the weather condition between two satellite acquisitions is more different. The results show that the three-dimensional ray tracing method can reduce the root-mean-square error of the results up to 4.8 cm compared to the integration methods.

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“…The ERA5 contains most parameters available in its predecessor, ERA-Interim, and ERA5 has some In this research, the ERA5 reanalysis data published by the European Centre for Medium-Range Weather Forecasts (ECMWF) were used to perform the 3D ray tracing technique. Previous studies have shown that this data is very useful in a variety of fields, including geodynamics and geodesy [22,23]. The ERA5 is a climate reanalysis dataset, covering the period from 1950 to the present.…”

Section: Study Area and Data Setmentioning

confidence: 99%

An Optimal Troposphere Tomography Technique Using the WRF Model Outputs and Topography of the Area

et al. 2020

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The water vapor content in the atmosphere can be reconstructed using the all-weather condition troposphere tomography technique. In common troposphere tomography, the water vapor of each voxel is represented by an unknown parameter. This means that when the desired spatial resolution is high or study area is large, there will be a huge number of unknown parameters in the problem that need to be solved. This defect can reduce the accuracy of troposphere tomography results. In order to overcome this problem, an optimal voxel-based troposphere tomography using the Weather Research and Forecasting (WRF) model is proposed. The new approach reduces the number of unknown parameters, the number of empty voxels and the role of constraints required to enhance the spatial resolution of tomography results in required areas. Furthermore, the effect of considering the topography of the study area in the tomography model is examined. The obtained water vapor is validated by radiosonde observations and Global Positioning System (GPS) positioning results. Comparison of the results with the radiosonde observations shows that using the WRF model outputs and topography of the area can reduce the Root Mean Square Error (RMSE) by 0.803 gr/m3. Validation using positioning shows that in wet weather conditions, the WRF model outputs and topography reduce the RMSE of the east, north and up components by about 17.42, 10.46 and 20.03 mm, which are equivalent to 46.01%, 35.78% and 53.93%, respectively.

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

Cited by 21 publications

References 22 publications

Interseismic Strain Accumulation Across the Main Recent Fault, SW Iran, From Sentinel‐1 InSAR Observations

Interseismic Strain Accumulation Across the Main Recent Fault, SW Iran, From Sentinel‐1 InSAR Observations

Assessment of InSAR tropospheric signal correction methods

An Optimal Troposphere Tomography Technique Using the WRF Model Outputs and Topography of the Area

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