Radar Interferograms Research Articles

Phase unwrapping of SAR interferogram from modified U-net via training data simulation and network structure optimization

Phase unwrapping is the process of retrieving the true phase values from observed wrapped phases by adding the correct multiples of 2π. This process is crucial in synthetic aperture radar (SAR) interferometry, and numerous studies have aimed to enhance its performance. This study explored phase unwrapping using a modified U-Net regression model by optimizing both the network structure and training data. For network structure optimization, the study first compared model performance based on the size ratio of the lowest feature maps, determined by the number of pooling layers, to the size of convolutional kernels. A multi-kernel U-Net structure was developed to ensure robustness against variations in phase noise and gradient, compared to a standard single-kernel U-Net. Regarding the training data, data augmentation was implemented to address imbalances and better represent the local noise characteristics found in actual SAR interferograms. The training data was simulated to include local noise effects based on coherence measurements from real SAR data, as well as simple noise used for benchmarking the unwrapping performance with different training datasets. The results indicated that when the convolutional kernel size is smaller than the feature map size at the lowest layer, increasing the number of pooling layers leads to improvements in unwrapping performance. Conversely, performance decreased when the feature map size at the lowest layers was smaller than the convolutional kernel size. Specifically, the single-kernel U-Net with six pooling layers and the multi-kernel U-Net with five pooling layers exhibited the best unwrapping performance. Considering both simulated and real synthetic aperture radar interferogram data, the mean absolute errors for the single- and multi-kernel U-Net trained with simple noise were approximately 0.235 and 0.254, respectively. In contrast, for models trained with locally variable noise simulation data, the MAEs dropped to about 0.033 and 0.032, showing an improvement by approximately eightfold over models trained with simple noise. For real synthetic aperture radar interferograms, the mean absolute errors were 0.542 (single-kernel U-Net trained using simple noise), 0.592 (multi-kernel U-Net trained using simple noise), 0.542 (single-kernel U-Net trained using local noise), and 0.445 (proposed), respectively, underscoring the significant impact of training data on unwrapping performance. The study also evaluated the performance of the statistical-cost, network-flow algorithm for phase unwrapping (SNAPHU), obtaining mean absolute errors of about 0.043 for simulation data and 0.861 for real SAR data. Consequently, the multi-kernel model trained with locally different noise simulation data demonstrated roughly twice the performance compared to the traditional phase unwrapping method.

Toward long-term monitoring of regional permafrost thaw with satellite interferometric synthetic aperture radar

Abstract. Active-layer thickness (ALT) is estimated for a study area in northern Alaska's continuous-permafrost zone using satellite data from Sentinel-1 (radar) and ICESat-2 (lidar) for the period 2017 to 2022. Synthetic aperture radar (SAR) interferograms were generated using the Short Baseline Subset (SBAS) approach. Displacement time series over the thaw season (June–September) are fit well with a linear model (root mean square error (RMSE) scatter is less than 7 mm) and show maximum seasonal subsidence of 20–60 mm. ICESat-2 products were used to validate the interferometric synthetic aperture radar (InSAR) displacement time series. ALT was estimated from measured subsidence using a widely used model exploiting the volume difference between ice and water, reaching a maximum depth in our study area of 1.5 m. Estimated ALT is in good agreement with in situ and other remotely sensed data but is sensitive to assumed thaw season onset, indicating the need for reliable surface temperature data. Our results suggest the feasibility of long-term permafrost monitoring with satellite InSAR. However, the C-band (∼55 mm center wavelength) Sentinel radar is sensitive to vegetation cover and, in our studies, was not successful for similar monitoring in the heavily treed discontinuous-permafrost zone of central Alaska.

Airborne Platform Three-Dimensional Positioning Method Based on Interferometric Synthetic Aperture Radar Interferogram Matching

As the demand for precise navigation of aircraft increases in modern society, researching high-precision, high-autonomy navigation systems is both theoretically valuable and practically significant. Because the inertial navigation system (INS) has systematic and random errors, its output information diverges. Therefore, it is necessary to combine them with other navigation systems for real-time compensation and correction of these errors. The SAR matching positioning and navigation system uses synthetic aperture radar (SAR) image matching for platform positioning and compensates for the drift caused by errors in the inertial measurement unit (IMU). Images obtained by SAR are matched with digital landmark data, and the platform’s position is calculated based on the SAR imaging geometry. However, SAR matching positioning faces challenges due to seasonal variations in SAR images, the need for typical landmarks for matching, and the lack of elevation information in two-dimensional SAR image matching. This paper proposes an airborne platform positioning method based on interferometric SAR (InSAR) interferogram matching. InSAR interferograms contain terrain elevation information, are less affected by seasonal changes, and provide higher positioning accuracy and robustness. By matching real-time InSAR-processed interferograms with simulated interferograms using a digital elevation model (DEM), three-dimensional position information about the matching points has been obtained. Subsequently, a three-dimensional positioning model for the platform has bene established using the unit line-of-sight vector decomposition method. In actual flight experiments using an FMCW Ku-band Interferometric SAR system, the proposed platform positioning framework demonstrated its ability to achieve precise positioning in the absence of signals from the global navigation satellite system (GNSS).

A method for correcting InSAR interferogram errors using GNSS data and the K-means algorithm

Correcting interferometric synthetic aperture radar (InSAR) interferograms using Global Navigation Satellite System (GNSS) data can effectively improve their accuracy. However, most of the existing correction methods utilize the difference between GNSS and InSAR data for surface fitting; these methods can effectively correct overall long-wavelength errors, but they are insufficient for multiple medium-wavelength errors in localized areas. Based on this, we propose a method for correcting InSAR interferograms using GNSS data and the K-means spatial clustering algorithm, which is capable of obtaining correction information with high accuracy, thus improving the overall and localized area error correction effects and contributing to obtaining high-precision InSAR deformation time series. In an application involving the Central Valley of Southern California (CVSC), the experimental results show that the proposed correction method can effectively compensate for the deficiency of surface fitting in capturing error details and suppress the effect of low-quality interferograms. At the nine GNSS validation sites that are not included in the modeling process, the errors in the ascending track 137A and descending track 144D are mostly less than 15 mm, and the average root mean square error values are 11.8 mm and 8.0 mm, respectively. Overall, the correction method not only realizes effective interferogram error correction, but also has the advantages of high accuracy, high efficiency, ease of promotion, and can effectively address large-scale and high-precision deformation monitoring scenarios.Graphical

LSC-GInSAR: a GNSS-enhanced InSAR approach by using least squares collocation

SUMMARY High quality Interferometric Synthetic Aperture Radar (InSAR) interferograms are essential for determining surface deformation from InSAR time-series. However, InSAR interferograms are usually polluted by spatially correlated errors (SCEs), especially the unmodelled atmospheric phase delays. To mitigate spatially correlated errors and improve the quality of InSAR interferograms, we propose a new approach to incorporate the Global Navigation Satellite System (GNSS) data from continuously operating reference stations for enhancing InSAR interferograms via modelling SCEs as signals and solving the signals together with the systematic parameters using least squares collocation (LSC), which is referred to as the LSC-GInSAR approach. Our improvement for the GInSAR method of Neely et al. can correct more SCEs. The Sentinel-1 data over the southern Central Valley of California, USA, are processed with our LSC-GInSAR approach, which is compared to the GInSAR approach. The performance of the LSC-GInSAR approach is evaluated by external GNSS displacements. The results show that the LSC-GInSAR approach can effectively mitigate medium-to-long-wavelength SCEs. The displacements resolved by LSC-GInSAR are more consistent with the cGNSS observations than those resolved by GInSAR, with an average root mean square improvement of 14.3 per cent. In addition, the LSC-GInSAR approach reduced the average standard deviations of all 276 InSAR interferograms from 14.2 to 11.0 mm compared to that of the GInSAR approach.

A Multi-Scale Spatial Difference Approach to Estimating Topography Correlated Atmospheric Delay in Radar Interferograms

The Interferometric Synthetic Aperture Radar (InSAR) has been widely used as a powerful technique for monitoring land surface deformations over the last three decades. InSAR observations can be plagued by atmospheric phase delays; some have a roughly linear relationship with the ground elevation, which can be approximated using a linear model. However, the estimation results of this linear relationship are sometimes affected by phase ramps such as orbital errors, tidal loading, etc. In this study, we present a new approach to estimate the transfer function of vertical stratification phase delays and the transfer function of phase ramps. Our method uses the idea of multi-scale spatial differences to decompose the atmospheric phase delay into the vertical stratification component, phase ramp component, and other features. This decomposition makes the correlation between the vertical stratification phase delays and topography more significant and stable. This can establish the correlation between the different scales and phase ramps. We demonstrate our approach using a synthetic test and two real interferograms. In the synthetic test, the transfer functions estimated by our method were closer to the design values than those estimated by the full interferogram–topography correlation approach and the band-pass filtering approach. In the first real interferogram, out of the 9 sub-regions corrected by the proposed method, 7 sub-regions were outperformed the full interferogram–topography correlation approach, and 8 sub-regions were superior to the band-pass filtering method. Our technique offers a greater correction effect and robustness for coseismic deformation signals in the second real interferogram.

ВЛИЯНИЕ АТМОСФЕРЫ НА ИССЛЕДОВАНИЯ ПЕРЕСЕЧЕННОГО РЕЛЬЕФА МЕТОДАМИ РАДАРНОЙ ИНТЕРФЕРОМЕТРИИ

An influence of the atmosphere on the interferometric studies of the rugged terrain in the area of Tolbachik volcanic complex, Kamchatka, is discussed. Brief description of the radar interferometry technique and the components of the interferometric phase differences are presented. The two-component model of the troposphere is described, which assumes the decomposition of the refractive index into dry and wet components. An influence of the variations of the troposphere refractive index between radar observations on the phase measurements is simulated. The weather data of Klyuchi meteorological station were interpolated to the moments of the Canadian synthetic aperture radar «Radarsat-2» observations in summer-autumn 2013 and used as input data in the modeling. In the number of cases the calculated differential radar interferograms contain non-zero values of the phase difference that grow with the height of the relief. The non-zero phase differences observed on the interferograms are typically in the good agreement with simulation results. A noticeable discrepancy between interferometric measurements and simulation data occurs in the case of unstable weather with sharp alteration of relative humidity in the survey area, especially in the case the troposphere meteorological parameters vary in different directions during the radar observations. An assumption is made that the reason of the observed modeling errors is that the weather data of distant meteorological station do not match sometimes the actual weather conditions in the study area. The impact of variations of the refractive index between radar observations is less, the less the variations of the relief heights in the survey area.

An Influence of Snow Covers on the Radar Interferometry Observations of Industrial Infrastructure: Norilsk Thermal Power Plant Case

This manuscript presents the results of the study of snow covers’ influence on the interferometric measurements of the stability of industrial infrastructure in the vicinity of Norilsk city, Russia. Fuel tanks of the Norilsk thermal power plant (TPP) were selected as an object of study due to a well-known accident when about 20,000 tons of diesel fuel spilled from one of the tanks. Sentinel-1 synthetic aperture radar data acquired over the territory of Norilsk TPP were used in the DInSAR study of the possible displacements of the tanks that could be the cause of the tank’s damage. For twelve days, radar interferograms that were generated in the study covered the cold and warm seasons of 2018–2020, including the catastrophic event—the rupture of the tank with diesel fuel—in order to shed light on the possible impact of the area subsidence because of permafrost thaw under the tanks. As the tank walls and adjacent concrete base constituted the virtual dihedral corner reflector, the accumulation of snow on the surface near the tanks created a distorting effect on the results of monitoring the stability of the tank’s location. Three models of snow layer within the dihedral proposed could help explain the deviations in the signal amplitude and phase in the case of snowfalls occurring between radar observations. We propose three ways to minimize the influence of snow on interferometric measurements. One of them, the selection of the radar data acquired in proper observation conditions, made it possible to assess the stability of the mutual location of the tanks. Among the most important processing and analysis results in the paper is a conclusion about the high stability of the fuel tank’s location on the yearly time interval, including the troubleshooting tank.

A Spectral Model for Multilook InSAR Phase Noise Due to Geometric Decorrelation

We find that the multilook phase noise associated with geometric decorrelation in a synthetic aperture radar interferogram does not follow the spectral shape of the signal in the range dimension because the phase noise arises from the uncorrelated ends of the frequency bands of the single-channel radar data contributing to the interferogram. The phase noise due to geometric decorrelation therefore diminishes much more quickly than the square root of the number of looks in range when the number of looks is large, and it is thus not well characterized by the common practice of computing the multilook phase noise from the Cramer-Rao bound as a function only of the total coherence and the total number of looks. Large discrepancies may result in cases for which the phase noise due to geometric decorrelation dominates, such as when simulating data with no receiver thermal noise. We present here a more accurate model of the multilook phase noise due to geometric decorrelation in addition to thermal noise. Our model depends on the spectra of the individual channels and on the spectrum of the multilook averaging window in range. Use of this model may have bearing on decisions of whether common-band filtering is necessary for particular applications.

Range Spectral Filtering in SAR Interferometry: Methods and Limitations.

A geometrical decorrelation constitutes one of the sources of noise present in Synthetic Aperture Radar (SAR) interferograms. It comes from the different incidence angles of the two images used to form the interferograms, which cause a spectral (frequency) shift between them. A geometrical decorrelation must be compensated by a specific filtering technique known as range filtering, the goal of which is to estimate this spectral displacement and retain only the common parts of the images' spectra, reducing the noise and improving the quality of the interferograms. Multiple range filters have been proposed in the literature. The most widely used methods are an adaptive filter approach, which estimates the spectral shift directly from the data; a method based on orbital information, which assumes a constant-slope (or flat) terrain; and slope-adaptive algorithms, which consider both orbital information and auxiliary topographic data. Their advantages and limitations are analyzed in this manuscript and, additionally, a new, more refined approach is proposed. Its goal is to enhance the filtering process by automatically adapting the filter to all types of surface variations using a multi-scale strategy. A pair of RADARSAT-2 images that mapped the mountainous area around the Etna volcano (Italy) are used for the study. The results show that filtering accuracy is improved with the new method including the steepest areas and vegetation-covered regions in which the performance of the original methods is limited.

Deep Learning for InSAR Phase Filtering: An Optimized Framework for Phase Unwrapping

Interferometric Synthetic Aperture Radar (InSAR) data processing applications, such as deformation monitoring and topographic mapping, require an interferometric phase filtering step. Indeed, the filtering quality significantly impacts the deformation and terrain height estimation accuracy. However, the existing classical and deep learning-based phase filtering methods provide artefacts in the filtered areas where a large amount of noise prevents retrieving the original signal. In this way, we can no longer distinguish the underlying informative signal for the next processing step. This paper proposes a deep convolutional neural network filtering method, developing a novel learning strategy to preserve the initial phase noise input into these crucial areas. Thanks to the encoder–decoder powerful phase feature extraction ability, the network can predict an accurate coherence and filtered interferometric phase, ensuring reliable final results. Furthermore, we also address a novel Synthetic Aperture Radar (SAR) interferograms simulation strategy that, using initial parameters estimated from real SAR images, considers physical behaviors typical of a real acquisition. According to the results achieved on simulated and real InSAR data, the proposed filtering method significantly outperforms the classical and deep learning-based ones.

Methods of joint processing of complex radar interferograms and multispectral optical images under temporal decorrelation

This article substantiates the relevance of the study, the characteristics of the joint use of optical multispectral survey, radar interferometry and partial polarimetry, identifies the scope of interpretation of the radar-optical composite built by optical and radar data, and provides a mathematical model for image processing of the water surface area. The quantitative assessment of these automated or semi-automated methods is not inferior to the accuracy of traditional methods for assessing the state of the offshore marine environment. It was shown that the most efficient approach is the direct use of the ResNet-10 deep learning algorithm on scenes when combined with complex (amplitude and phase) centimeter-range radar images and multispectral optical images of Sentinel platforms. This approach made it possible to detect 86.72% of all spots in the scenes and had an average accuracy of 75.35%. The approach has also showed a significantly reduced ability to detect patches when the local wind speed was below 2 m/s or above 12 m/s.

Interseismic deformation rate of the Haiyuan fault system based on the modified SBAS method

Radar interferograms are usually influenced by factors such as atmospheric artifacts, orbital errors, and terrain errors. It is difficult to reduce the influence by using the conventional small baseline subset (SBAS) method when determining the deformation rate. This study uses the adjustment model with systematic parameters to improve the conventional SBAS method and employs it to determine the interseismic deformation rate of the Haiyuan fault system, providing a data reference for exploring the locking depth, strain accumulation state, and potential seismic risk assessment of different segments of the Haiyuan fault system. The results are as follows: (1) the simulation experiment verifies the feasibility and robustness of the modified SBAS method. This method can effectively reduce the influence of residual signals such as atmospheric artifacts, orbital errors and terrain errors in the interferograms. The deformation rate map can be significantly improved; (2) the deformation rate field in the radar's Line of Sight (LOS) direction shows that there are obvious differences between the north and south sides of Haiyuan fault system, which is consistent with the characteristics of the left-lateral strike-slip movement of the Haiyuan fault system. The deformation rate field and profiles reflect the complex trends among different segments of Haiyuan fault system in detail. (3) the deformation rate of the Jingtai pull-apart basin is higher than that of the surrounding areas, possibly indicating strong regional activity, which provides a reference for studying the seismic risk of the Jingtai pull-apart basin; and (4) the interseismic deformation rate and profiles across the fault show that the middle section of the Lao Hu Shan (LHS) segment and the western and middle sections of the Haiyuan segment are locked.

Research on a GB-SAR interferogram filtering method for open-pit mines based on eight-neighbourhood outlier discrimination

Ground-based synthetic aperture radar (GB-SAR) interferograms contain a large amount of phase noise. The existing methods lose effective deformation information to varying degrees upon filtering, which seriously reduces the monitoring accuracy of GB-SAR. In this paper, a GB-SAR interferogram filtering method for open-pit mines is studied. First, permanent scatterer points (PSPs) are extracted through coarse filtering, and the distribution of the noise phase is analysed. Then, an improved Grubbs outlier discrimination criterion is introduced considering the uncertainty of the PSP distribution. A method based on the eight-neighbourhood outlier discrimination (EOD) criterion is proposed to identify noise points. High-coherence points (HCPs) are used to reconstruct the phase of the noise points to filter the GB-SAR interferogram. Finally, the filtering effect is quantitatively evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The superiority of the proposed filtering method in the image displacement region is verified based on simulated and real deformation data. The results show that the proposed method can accurately filter out the phase noise of interferograms and improve the accuracy of GB-SAR for slope monitoring.

A Lightweight Anchor-Free Subsidence Basin Detection Model With Adaptive Sample Assignment in Interferometric Synthetic Aperture Radar Interferogram

The excessive exploitation of coal resources has caused serious land subsidence, which seriously threatens the lives of the residents and the ecological environment in coal mining areas. Therefore, it is of great significance to precisely monitor and analyze the land subsidence in the mining area. To automatically detect the subsidence basins in the mining area from the interferometric synthetic aperture radar (InSAR) interferograms with wide swath, a lightweight model for detecting the subsidence basins with an anchor-free and adaptive sample assignment based on the YOLO V5 network, named Light YOLO-Basin model, is proposed in this paper. First, the depth and width scaling of the convolution layers and the depthwise separable convolution are used to make the model lightweight to reduce the memory consumption of the CSPDarknet53 backbone network. Furthermore, the anchor-free detection box encoding method is used to deal with the inapplicability of the anchor box parameters, and an optimal transport assignment (OTA) adaptive sample assignment method is introduced to solve the difficulty of optimizing the model caused by abandoning the anchor box. To verify the accuracy and reliability of the proposed model, we acquired 62 Sentinel-1A images over Jining and Huaibei coalfield (China) for the training model and experimental verification. In contrast with the original YOLO V5 model, the mean average precision (mAP) value of the Light YOLO-Basin model increases from 45.92 to 55.12%. The lightweight modules of the model sped up the calculation with the one billion floating-point operations (GFLOPs) from 32.81 to 10.07 and reduced the parameters from 207.10 to 40.39 MB. The Light YOLO-Basin model proposed in this paper can effectively recognize and detect the subsidence basins in the mining areas from the InSAR interferograms.

Signals of Surface Deformation Areas in Central Chile, Related to Seismic Activity—Using the Persistent Scatterer Method and GIS

Interferometric synthetic aperture radar is an effective means of measuring changes in the altitude of the Earth’s surface. In this research, the areas of surface deformation associated with low- and medium-intensity seismic events in Central Chile were analyzed using SENTINEL 1 satellite radar interferograms and geographical information system (GIS) tools. The persistent scatterer method was used to reduce noise from conventional InSAR methods. The results revealed that the coastal zone of Central Chile has a high density of daily earthquakes with a prevalence (93.03%) of low- and medium-intensity earthquakes. Monthly deformation maps were developed for the coast of the Biobio region in Central Chile. A clear deformation pattern is defined along the coast, being greater in the Arauco, Lota and Lebu areas. It was also shown that there was a slight upward trend in the north and northeast zone (i.e., δup ~3 mm/year), while there was an obvious accentuated upward trend (i.e., δup ~24 mm/year) in the southern part. This movement increases as latitude increases. This pattern is related to the daily seismic activity, the product of the movement between plates, and the geological faults located in the area. The deformation and trend maps provide certainty in terms of where hotspots are located, e.g., the most hazardous areas in the study zone, which can be applied to urban planning and/or safety assessment.

On the Phase Nonclosure of Multilook SAR Interferogram Triplets

This work explores the properties characterizing the phase non-closure of multi-look synthetic aperture radar (SAR) interferograms. Specifically, we study the implications of multi-look phase time incongruences on the generation of ground displacement time-series through small baseline (SB) multi-temporal InSAR (Mt-InSAR) methods. Our research clarifies how these phase inconsistencies can propagate through a time-redundant network of SB interferograms and contribute, along with phase unwrapping (PhU) errors, to the quality of the generated ground displacement products. Moreover, we analyze the effects of short-lived phase bias signals that could happen in sequences of short baseline (SB) interferograms and propose a strategy for their mitigation. The developed methods have been tested using both simulated and real SAR data. The latter were collected by the Sentinel-1A/B (C-band) sensors over the study areas of Nevada state, U.S., and Sicily Island, Italy.

An Area-Based Projection Algorithm for SAR Radiometric Terrain Correction and Geocoding

This article describes a projection algorithm between radar and map coordinates based on the representation of radar samples as area elements (AEs) rather than point elements. Each AE on the map grid (geographic grid) is associated with a number of radar grid samples that intersect completely or partially the AE. The association enables the geocoding (i.e., the map projection of radar imagery) with adaptive multilooking, accurately accounting for all radar samples contributing to the geocoded elements according to topography and radar geometry. By using averaging rather than interpolation, the proposed projection does not suffer from interpolation overfitting. The area-based geocoding also enables the generation of the geocoded polarimetric covariance matrix (GCOV) and geocoded synthetic aperture radar (SAR) interferograms with adaptive multilooking. Analogously, the slant-range projection of geocoded data is improved by projecting geographic grid pixels onto the radar grid according to their corresponding location based on the radar geometry without leaving gaps. This approach is used to reduce the computation time of previously published radiometric terrain correction (RTC) algorithms, performing 3.6–6.5 times faster over multilooked data and up to 26.3 times faster over single-look data. We demonstrate the strengths of the proposed area projection (AP) algorithm for RTC and geocoding using Uninhabited Aerial Vehicle SAR (UAVSAR), Sentinel-1B, and ALOS-2/PALSAR-2 data, and evaluate the results in the context of the upcoming NASA-ISRO SAR (NISAR) mission.

Atmospheric Phase Screen Compensation on Wrapped Ground-Based SAR Interferograms

This work proposes a method for estimating and compensating the atmospheric phase screen (APS) in sets of synthetic aperture radar (SAR) interferograms generated with a ground-based SAR (GB-SAR) instrument. We address the presented approach’s physical, statistical, and mathematical framework by discussing its potential and limitations. In contrast with other existing algorithms that estimate the APS from the unwrapped phase signals, our methodology is based on the straightforward analysis of the wrapped phases, directly. Therefore, the method is not affected by any potential phase unwrapping mistake, and it is suitable for multitemporal interferometric SAR (InSAR) applications. The effects of the local topography, the decorrelation noise, and the ground deformation on the APS estimates are deeply studied. Experiments performed on simulated and real GB-SAR InSAR data corroborate the validity of the theory. In particular, the simulated results show that the method is beneficial in zones with medium-to-high topographic slopes (e.g., for Alpine and mountainous regions).

New insight into the 24 January 2020, Mw 6.8 Elazığ earthquake (Turkey): An evidence for rupture-parallel pull-apart basin activation along the East Anatolian Fault Zone constrained by Geodetic and Seismological data

In this study, we investigated the main features of the causative fault of the 24 January 2020, Mw 6.8 Elazığ earthquake (Turkey) using seismological and geodetic data sets to provide new insight into the East Anatolian Fault Zone (EAFZ). We first constrained the co-seismic surface deformation and the rupture geometry of the causative fault segment using Interferometric Synthetic Aperture Radar (InSAR) interferograms (Sentinel-1A/B satellites) and teleseismic waveform inversion, respectively. Also, we determined the centroid moment tensor (CMT) solutions of focal mechanisms of the 27 aftershocks using the regional waveform inversion method. Finally, we evaluated the co-seismic slip distribution and the CMT solutions of the causative fault as well as of adjacent segments using the 27 focal solutions of the aftershocks, superimposed on the surface deformation pattern. The CMT solution of the 24 January 2020Elazığ earthquake reveals a pure strike-slip focal mechanism, consistent with the structural pattern and left-lateral motion of the EAFZ. The rupture process of the Elazığ event indicated that the rupture is started at 12 km around the hypocenter, and then propagated bilaterally along the NE-SW but mainly toward the southwest. The rupture slip has initially propagated toward the southwest (first 10 s) and northeast (4 s), and again toward the southwest (9 s). Maximum displacement is calculated as 1.3 m about 20 km southwest of the hypocenter at 6 km depth (centroid depth). The rupture stopped to down-dip around 20 km depth toward the southwest. The distribution of the slip vectors indicates that the rupture continued mostly through a normal oblique movement. Most of the moment release was released SW of the hypocenter and the rupture reached up to around 50 km. The focal mechanisms of analyzed 27 aftershocks show strike-slip, but mostly normal and normal oblique-slip faulting with an orientation of the tensional axes (NNE-SSW), indicating a normal oblique-slip, “transtensional” stress regime, parallel-subparallel to the strike of the EAFZ, consistent with SW-rupture directivity and co- seismic deformation pattern. Finally, based on the co-seismic surface deformation compatible with the distributional pattern of normal focal solutions, normal and normal oblique-slip focals of the aftershocks evidence the rupture-parallel pull-apart basin activation as a segment boundary of the left-lateral strike-slip movement of the EAFZ.