Insights into the Arabia–Anatolia plate collision from integrated SAR analysis and detailed modelling of the 2023 Türkiye–Syria earthquakes

We examine the 6 February 2023 Türkiye–Syria earthquakes using an extensive SAR dataset, addressing some limitations of previous studies. Large surface displacements caused significant loss of coherence in the Sentinel-1 Differential SAR Interferometry (DInSAR) results and prior analyses using Pixel Offset Tracking (POT) were limited by the poor azimuthal resolution of the available Sentinel-1 and ALOS-2 SAR images. For the first time, we present high-azimuth-resolution displacement measurements obtained thanks to the SAOCOM-1 sensors. Azimuth information is important considering that the main movements occurred were horizontal and, in some areas, with an important N-S component. By exploiting multi-frequency Sentinel-1, ALOS-2, and SAOCOM-1 SAR data and applying the DInSAR and POT techniques, where appropriate, we derived a detailed displacement field and retrieved an elaborated fault model comprising 22 segments; this model accurately characterizes the geometry and kinematics of the two main faults. Maximum slip reaches ∼15 m for both faults, and the total seismic moment corresponds to Mw 7.9. A finite-fault ShakeMap generated from this source model shows improved agreement with near-field ground motions relative to point-source formulations, while on-fault static stress changes identify low-slip areas that remained unbroken during rupture. The three-dimensional displacement field reveals a broad uplifted region and opposing horizontal motions between the two main ruptures, indicating distributed deformation within an interfault block that accommodates part of the Arabia–Anatolia convergence. This off-fault deformation has not been documented previously for the 2023 sequence and provides new constraints on strain partitioning and future seismic hazard along the East Anatolian Fault Zone.

1 Introduction

The Eastern Mediterranean region was struck by a major seismic sequence on 6 February 2023 in southeast Türkiye (Figure 1). The first and strongest event of magnitude Mw 7.8, may have ruptured within a previously identified seismic gap (Över et al., 2023) and was followed 9 hours later by an Mw 7.5 earthquake. These earthquakes rank among the most severe in the region in recent decades and caused catastrophic impacts, including more than 57,000 fatalities (Hussain et al., 2023) and widespread infrastructure damage such as building collapses and gas pipeline failures (Unal et al., 2024 and references therein). The sequence occurred along the East Anatolian Fault Zone, a major plate boundary approximately 600 km in length (Figure 1). Given that only a few continental strike-slip earthquakes exceeding Mw 7.5 have been documented globally in recent years (Mai et al., 2023), this sequence drew international scientific attention, resulting in a substantial number of studies (e.g., Barbot et al., 2023; Bai et al., 2025; Böse et al., 2024; Delouis et al., 2023; Ding et al., 2023; Goldberg et al., 2023; Hu et al., 2025; Trikhunkov et al., 2024; Karabulut et al., 2023; Kobayashi et al., 2024; Cetin et al., 2024; Convertito et al., 2024; Mai et al., 2023; Toker et al., 2023; Zhao et al., 2023; Okuwaki et al., 2023; Özkan et al., 2023; Provost et al., 2024; Bayraktar et al., 2024; Ren et al., 2024; Tung et al., 2024; Chen and Zhou, 2024; Magen et al., 2024; He et al., 2023; Wang et al., 2023; Wu et al., 2023; Liu et al., 2024; Kobayashi et al., 2024; Xu et al., 2023a; Xu et al., 2023b; Li et al., 2023; Liu et al., 2023; Ma et al., 2024; Sultan et al., 2025; Zhang et al., 2023; Zhou et al., 2025; Fotiou et al., 2023).

Figure 1

Figure 1. The February 2023 seismic sequence. Stars mark the two largest seismic events. Red focal mechanisms are from the United States Geological Survey - USGS (https://www.usgs.gov/). The purple focal mechanism corresponds to the geodetically derived focal mechanism of the 20 February 2023 event from the Istituto Nazionale di Geofisica e Vulcanologia–INGV, finite source database (https://terremoti.ingv.it/en/finitesource). In the bottom right inset map, stars indicate the largest events, red lines show fault traces from Reitman et al. (2023) and blue rectangles denote the fault segments derived from our study.

The earthquakes produced large-scale surface deformation, prompting extensive analysis of co-seismic data using Differential SAR Interferometry (DInSAR; Massonnet and Feigl, 1998; Rosen et al., 2000; Bürgmann et al., 2000). Initial studies relied mainly on Sentinel-1 (S-1) C-band data (5.6 cm wavelength), which are freely available under the COPERNICUS program open-access policy (Torres et al., 2012). The Interferometric Wide Swath (IWS) capability of TOPSAR provides spatial coverage of up to ∼250 km, advantageous for mapping the widespread deformation of the Türkiye–Syria sequence. However, the large displacements near the faults led to severe decorrelation and very high fringe rates, which limited phase unwrapping and restricted the ability of interferometric analysis to fully resolve near-field permanent displacements (e.g. Nofl et al., 2024).

Such limitations are quite clear in Supplementary Figure S1; these results, along with many other for earthquakes that have accrued globally, are generated through the EPOSAR tool (Monterroso et al., 2019; Monterroso et al. 2020; Monterroso et al. 2022), developed to automatically compute co-seismic DInSAR products, which are available via the EPOSAR TCS catalogue (https://www.ics-c.epos-eu.org). Moreover, the application of the Pixel Offset Tracking (POT) technique (e.g., Strozzi et al., 2002; Luckman et al., 2007) to the Sentinel-1 image amplitudes, has significant limitations. Indeed, although it can be beneficial for what concerns the retrieval of the large across-track (range) displacements, due to the relatively high ground range resolution (on average 5 m), it is extremely limited concerning the (along-track) azimuth component, due to the rather poor azimuth resolution (about 20 m), characterizing the Sentinel-1 TOPSAR acquisition mode. Assuming that the accuracy of the retrieved displacement, with the POT technique, is approximately 1/10 resolution (Strozzi et al., 2002; Casu et al., 2011), it turns out that only the horizontal displacements in range directions can be effectively mapped. Azimuth information is important considering that the main movements occurred were horizontal and, in some areas, with an important N-S component. For this reason, it was crucial to integrate the Sentinel-1 data with other SAR data, characterized by different wavelengths and better spatial resolutions.

We therefore included L-band SAR data from the ALOS-2 satellite acquired by JAXA (Japan Aerospace Exploration Agency) and the SAOCOM-1 constellation operated by CONAE (Comisión Nacional de Actividades Espaciales, Argentina). ALOS-2 data were available in ScanSAR mode (15 m range, 50 m azimuth resolution), while SAOCOM-1 data were acquired in Stripmap mode (10 m range, 5 m azimuth resolution). The finer azimuth resolution of SAOCOM-1 allowed us to generate a highly detailed deformation map of the surface displacements induced by the February 2023 sequence.

The manuscript is organized as follows: We first describe the Earth Observation datasets used, the methodological framework, and the DInSAR and POT results for displacement mapping. We then detail the DInSAR analysis of ALOS-2 data and the POT analysis of SAOCOM-1 data, which together provide the basis for our displacement field. Presented here for the first time, SAOCOM-1 allowed us to obtain an unprecedented, detailed deformation map of the surface displacements, caused by the two earthquakes. Thanks to this dataset we were able in many cases to avoid making a priori assumptions about the parameters of the fault segments and to estimate a detailed slip distribution model. We also generate an updated finite-fault ShakeMap and assess off-fault deformation patterns to explore how the earthquakes relate to the broader Arabia–Anatolia plate boundary system.

2 Satellite earth observation

2.1 Satellite data and DInSAR, POT results

A central aim of this study is to jointly utilize the most reliable information derived from DInSAR and POT applied to the full set of available multi-frequency SAR data acquired by the Sentinel-1, ALOS-2, and SAOCOM-1 sensors. In Table 1, their main characteristics, and those of the derived products are shown. Moreover, the SAR sensors acquisition footprints are presented in Figure 2, where Sentinel-1, ALOS-2, and SAOCOM-1 tracks are highlighted in blue, orange, and red colour, respectively.

Table 1

Table 1. Main characteristics of the SAR images and data pairs used in the integrated DInSAR/POT analysis (Δ_rg denotes ground range resolution).

Figure 2

Figure 2. Footprints of the SAR acquisitions used for displacement retrieval. Blue rectangles indicate Sentinel-1 images, orange ALOS-2, and red SAOCOM-1. The pink polygon marks the area where the 3D displacement field was reconstructed.

For Sentinel-1, we used six C-band Single Look Complex (SLC) images from both ascending (Tracks 14 and 116) and descending (Track 21) orbits of Sentinel-1A. From these data, two ascending and one descending interferograms were generated (Table 1). As shown in Supplementary Figure S1, interferometric coherence is strongly degraded near the ruptured faults due to the metre-scale displacements caused by the high-magnitude earthquakes. In addition, dense fringe rates in several zones hampered conventional phase unwrapping (Fornaro et al., 1997a; Fornaro et al., 1997b). As a result, Sentinel-1 DInSAR maps provided only limited Line-of-Sight (LoS) displacement information. To overcome this, we applied the POT technique to the corresponding amplitude images, enabling the retrieval of displacement fields in both LoS and azimuth directions (Casu et al., 2010). In particular, to retrieve the POT results we exploited the AMPCOR routine (https://github.com/isce-framework/isce2/tree/main/contrib/PyCuAmpcor), which performs a cross-correlation amplitude analysis between coseismic SAR image pairs, allowing to generate two main products: across-track and along-track displacement maps, respectively. Moreover, we remark that for each deformation measurement we also compute the covariance value along both the azimuth and range directions, highlighting that a small value of this covariance corresponds to a high accuracy of the retrieved displacement. Accordingly, the covariance values represents a quality indicator of the amplitude based retrieved measurements, similarly to the interferometric coherence for what attains the phase difference based computed displacements (Casu et al., 2011).

It is also worth noting that the accuracy of the achieved POT results depends mainly on the resolution of the exploited SLC SAR images. Therefore, in the case of the used Sentinel-1 TOPS data, the ground range resolution is about 5 m, which permits us to achieve an accuracy level of the displacement maps on the order of 0.5 m (i.e., about 1/10 of the resolution, as mentioned above). Conversely, the azimuth resolution of the Sentinel-1 TOPSAR data is about 20 m, corresponding to an accuracy level of the along-track direction POT products on the order of 2 m, which is inadequate to accurately retrieve the deformation field. Accordingly, we focused on the LoS deformation component, retrieved by applying the POT technique to the Sentinel-1 TOPSAR data. Figure 3 shows the POT deformation maps along the corresponding radar LoS directions obtained through the data collected from both ascending (T14 and T116 tracks, Figure 3a) and descending (T21 track, Figure 3b) orbits. We also remark that only the pixels characterized by a covariance value less than a selected threshold (we typically assume 0.04) are considered reliable because they allow to achieve the expected accuracy of 1/10 of the (azimuth/range) spatial resolution (Casu et al., 2010; Casu et al., 2011).

Figure 3

Figure 3. Sentinel-1 POT displacement maps in the radar LoS direction from ascending (a) and descending (b) orbits. The ground range resolution (∼5 m) enables a displacement accuracy of ∼0.5 m.

For what concerns the Sentinel-1 POT results, relevant to the range displacements, they are characterized by a rather high level of accuracy (on the order of 0.5 m, as discussed above), although they clearly do not reach the one achievable through the DInSAR technique, that corresponds to a fraction of the radar signal wavelength (Rosen et al., 2000). Thus, to obtain additional information on the LoS displacement component, we benefited from the availability of the SAR data acquired by lower frequencies satellite systems operating at L-band, which helped to mitigate the issues related to the coherence loss and high fringe rate that, as previously discussed, strongly affect the Sentinel-1 C-band DInSAR products particularly near the ruptured faults. Specifically, we took advantage of the L-Band ALOS/PALSAR-2 SAR data. In particular, we exploited four SLC ALOS-2 images (as said, with a resolution of about 15 m and 50 m in the ground range and azimuth directions, respectively) acquired through the ScanSAR mode (similar to the Sentinel-1 IWS TOPSAR mode) from ascending and descending orbits, and covering an area of approximately 350 km × 350 km. These data enable the application of phase unwrapping procedures and the retrieval of displacement maps along the LoS direction with good spatial coverage, making them particularly suitable for analysing large-area deformation phenomena, as those characterizing the investigated earthquakes.

The corresponding LoS DInSAR displacement maps were created by exploiting the interferometric pairs listed in Table 1 and are shown in Figure 4 (wrapped interferograms can be found in the Supplementary Material). We retrieved the LoS displacement field of the overall frame with centimetric accuracy, except for a few areas located near the fault traces. Indeed, within these zones, the coherence losses due to the misregistration errors, due to the large magnitude of the earthquakes, were very significant, thus leading to masking the pixels of these zone.

Figure 4

Figure 4. ALOS-2 LoS displacement maps from ascending (left) and descending (right) interferometric pairs.

We further remark that in order to retrieve the full 3D displacement field related to the investigated events, it is necessary to also estimate the displacement component along the azimuth direction with high accuracy. Unfortunately, similar to the Sentinel-1 TOPS case, also the ALOS-2 ScanSAR data suffer from the poor azimuth resolution (50 m) characterizing their acquisition mode. Accordingly, such a component cannot be retrieved with a sufficient level of accuracy by applying the POT technique. To overcome this issue, we benefited from L-band SAR data obtained through different acquisition modes, as for the StripMap one that can guarantee good spatial resolution (in the order of few meters) in the azimuth direction.

In particular, we made large use of the SAR data acquired by the Argentinean SAOCOM-1 constellation, operated by CONAE, which consists of two L-band SAR twin satellites SAOCOM-1A and B, launched in 2018 and 2020, respectively. This system ensures a DInSAR-oriented acquisition plan in StripMap mode over the Italian Space Agency (ASI) exclusivity region that includes the area affected by the investigated seismic events. For our analysis we used the available SAR data characterized by very good coverage, which is relevant to two tracks (198 and 197) and five different swaths from ascending orbits, using a total of 13 SLC images paired in eight couples (Table 1). In particular, the azimuth resolution is of about 5 m, allowing the retrieval of ground displacements with high spatial density and, even more remarkable, avoiding the extreme interferometric fringes undersampling effects that may arise when the DInSAR technique is used in cases of very large magnitude events. This is evident in Figure 5a where the SAOCOM-1 DInSAR results are shown; indeed, in this case, a clear interferometric fringe pattern is visible in the coherent areas. However, the spatial extent of each SLC image is approximately 40 km in the range direction, so the phase unwrapping technique may not be straightforwardly applied, because the large displacement variation across the fault element causes drastic phase unwrapping errors. Nevertheless, as mentioned before, thanks to the good azimuth resolution (5 m) it is possible to retrieve the azimuth component of the displacement through the POT technique. The merged deformation map (Figure 5b), resulting from the exploitation of the SAR data pairs listed in Table 1, is achieved with an accuracy of approximately 0.5 m, which permits us to better retrieve a co-seismic ground deformation component that is unique with respect to the signals retrieved through the previously analysed Sentinel-1 (TOPS) and ALOS-2 (ScanSAR) data.

Figure 5

Figure 5. SAOCOM-1 maps were created by processing the ascending SLC images and obtained by mosaicking the deformation maps on the different tracks and swaths: (a) DInSAR results (b) POT displacements along the azimuth direction.

Starting from the retrieved results obtained by applying the DInSAR and POT techniques to the available Sentinel-1, ALOS-2, and SAOCOM-1 SAR images, it was then possible to compute the complete 3D co-seismic displacement field for the investigated earthquakes, as discussed in the following Section.

2.2 Joint exploitation of DInSAR and POT measurements for the 3D-displacement field retrieval

The measurements obtained through the DInSAR and POT techniques, applied to the phase and amplitude of the exploited SAR image pairs, respectively, represent the projections along the radar LOS and the along-track directions of the ground displacements associated with the 6 February 2023 Türkiye–Syria seismic events. In order to provide a clearer understanding of the occurred ground motions, a 3D deformation field can be retrieved by jointly exploiting these heterogeneous displacement measurements available in a common geographic grid. To obtain this result, different approaches can be considered. In particular, the Strain Model and Variance Component Estimation (SM-VCE) method permits to estimate the 3D deformations by properly exploiting the spatial correlation of adjacent points (Liu et al., 2025; Hu et al., 2021), while the previously developed solution described in De Luca et al. (2017) is based on the Least Squares (LS) technique. In the following, we exploit the latter approach that is rather effective and easy to implement.

Before presenting the obtained results, let us first detail the rationale of the 3D deformation field retrieval. In particular, in Figures 6a,b, the simplified acquisition geometries along the East/vertical plane and the East/North plane are reported, respectively. Specifically, the look angle θ is the angle between the nadir and the radar LoS, while the satellite heading angle α represents the angle between the North direction and the projection of the sensor path on the ground. Regarding the heading angle, it is important to remark that the available SAR satellites follow nearly polar orbits, implying a value of α that is of about 345° for Sentinel-1 data, as well as 347° and 353° for ALOS-2 and SAOCOM-1 images, respectively, for what concerns the ascending passes. Moreover, it is about 195° for Sentinel-1 data and 192° for ALOS-2, related to the descending passes.

Figure 6

Figure 6. Geometries for the retrieval of the 3D displacement components: (A) satellite SAR system geometry in the East-Up plane. (B) Satellite SAR system geometry in the East-North plane.

Furthermore, in Figure 6, $d$ represents the actual displacement of a specific pixel on the ground both in East-Up and East-North planes; $d_{rg}$ (the rationale is the same for both ascending $d_{{rg}_{asc}}$ and descending $d_{{rg}_{desc}}$ components) and represents the one-dimensional measurement due to the projection of the deformation vector, say $d=\left(d_{Up},d_{East},d_{North}\right)$ , along the radar LoS (range) (e.g., Elachi, 1988; Curlander and McDonough, 1992). Conversely, $d_{az}$ (the rationale is the same for both ascending $d_{{az}_{asc}}$ and descending $d_{{az}_{desc}}$ components) is a one-dimensional measurement that derives from the projection of the deformation vector, say $d=\left(0,d_{East},d_{North}\right)$ along the azimuth direction; it is important to highlight that the azimuth vector and, consequently, the deformation component $d_{az}$ , belong to the East-North plane, so it is not sensitive to vertical deformation component.

In this framework, we can express the deformation components along the range and azimuth directions, for the ascending and descending orbits, as follows (Casu and Manconi, 2016):

$\begin{array}{ll}\hfill \mathit{d}=& \left(\begin{array}{c}\hfill d_{{rg}_{asc}}\hfill \\ \hfill d_{{rg}_{desc}}\hfill \\ \hfill d_{{az}_{asc}}\hfill \\ \hfill d_{{az}_{desc}}\hfill \end{array}\right)=\left(\begin{array}{ccc}\hfill -\sin {\theta }_{asc}\sin {\theta }_{asc}\hfill & \hfill -\sin {\theta }_{asc}\cos {\alpha }_{asc}\hfill & \hfill \cos {\theta }_{asc}\hfill \\ \hfill -\sin {\theta }_{desc}\sin {\alpha }_{desc}\hfill & \hfill \sin {\theta }_{desc}\cos {\alpha }_{desc}\hfill & \hfill \cos {\theta }_{desc}\hfill \\ \hfill \cos {\alpha }_{asc}\hfill & \hfill -\sin {\alpha }_{asc}\hfill & \hfill 0\hfill \\ \hfill -\cos {\alpha }_{desc}\hfill & \hfill -\sin {\alpha }_{desc}\hfill & \hfill 0\hfill \end{array}\right)\left(\begin{array}{c}\hfill d_{North}\hfill \\ \hfill d_{East}\hfill \\ \hfill d_{Up}\hfill \end{array}\right)\hfill \\ \hfill & =\mathbf{A}\left(\begin{array}{c}\hfill d_{North}\hfill \\ \hfill d_{East}\hfill \\ \hfill d_{Up}\hfill \end{array}\right)\hfill \end{array}(1)$

In the previous equations, the limitation of the LoS deformation measurements retrieved through the typical space-borne DInSAR technology is clear. Indeed, due to the quasi-polar orbits, the LoS displacement measurements are not very sensitive to the displacement component along the North direction (being $\mathit{sin}\alpha$ very close to zero), while they are highly sensitive to the vertical and horizontal (East-West) deformation components. On the contrary, the azimuth displacement measurements retrieved through the POT technique may regain sensitivity to the North component, thanks to the fact that the polar orbit followed by the satellite SAR system entails the azimuth direction that is close to the North one (as a consequence, $\mathit{cos}\alpha$ is very close to one).

Accordingly, it is evident that only for the pixels that are imaged by at least three non-coplanar measurements (specifically, one ascending range, one descending range, and one azimuth measurement) it is possible to perform a 3D deformation field retrieval. Due to the fact that in our study the azimuth displacement maps are available only for SAOCOM-1 acquisitions (see Table 1), it is clear that the area where the 3D displacement field can be achieved, corresponds to the overall SAOCOM-1 footprints represented, in Figure 2 by the area coloured in red.

We also remark that, for the investigated 3D displacement field retrieval, only a subset of “reliable” pixels is considered. In particular, with reference to the DInSAR products, the coherence threshold for the pixel selection is equal to 0.2 (Cheloni et al., 2017; Rosen et al., 2000), while the covariance threshold for the selection of the pixels exploited in the POT analysis is 0.04 (Casu et al., 2010; Casu et al., 2011).

We underline that for most of the investigated pixels of the common geographic grid the system described in Equation 1 is over-determined (more than three independent equations are available) and, consequently, it can be solved through a least square method. By applying this inversion method to the azimuth and range displacement measurements (say, $\mathit{d}$ see in Equation 1), we can easily demonstrate the efficacy of this data integration and how much this system is well constrained by evaluating the condition number (cond) of the matrix A (Demmel, 1997; Tarantola, 2005). The condition number is essentially a measure of the sensitivity to errors in the data relevant to a linear equations system, where cond(A) ≫ 1 is an indicator for an ill-conditioned linear system (Demmel, 1997; Tarantola, 2005). As shown in Equation 2, the condition number of the square matrix A in Equation 1 can be obtained as the ratio between the maximum and the minimum singular values vector, say [w]:

$cond\left(\mathit{A}\right)=\frac{\max \left(\left[w\right]\right)}{\min \left(\left[w\right]\right)}(2)$

In our case the obtained results have a mean value of 1.57 and a standard deviation value of 0.18 thus confirming that the system is well conditioned and, consequently, that the errors on the obtained solutions are comparable with those of the DInSAR and POT measurements. An image of the condition number map, retrieved on a pixel by pixel basis, is shown in Figure 7.

Figure 7

Figure 7. Map of the condition number values for the system reported in (1) computed for each pixel exploited for the 3D displacement field retrieval.

Concerning the expected errors of the retrieved 3D displacements, we remark that the uncertainties of the available DInSAR and POT measurements “propagate” into those of the retrieved 3D displacement components as follows:

${\sigma }_{NEU}=\sqrt{diag\left(C_{NEU}\right)}(3)$

where, for each investigated pixel, ${\sigma }_{NEU}=\left(\begin{array}{c}{\sigma }_{North}\\ {\sigma }_{East}\\ {\sigma }_{Up}\end{array}\right)$ represents the standard deviation vector relevant to the displacements along the North (N), East (E), and Up (U) directions, while $diag\left(·\right)$ represents the diagonal matrix operator and $C_{UEN}$ is the covariance matrix given by:

$C_{NEU}=A^{+}{C}_{disp}{\left(A^{+}\right)}^T(4)$

Note that the matrix A in (1) contains, for each exploited SAR dataset, the cosine projections of the displacements vector onto the North, East, and Up directions, and it is structured as reported in Equation 1; moreover, $A^{+}$ is the corresponding pseudoinverse matrix of A, and $C_{disp}$ represents a diagonal matrix containing the variance of the displacement measurements. Once $C_{NEU}$ is computed, the standard deviations of the three direction components can be easily obtained by extracting the square root of the diagonal elements, as shown in Equation 3. Thus, by considering the typical accuracies of the exploited measurements, which is of approximately 1/10 of the resolution for the POT results (corresponding to about 50 cm for both the Sentinel-1 and SAOCOM-1 products, see Table 1) and λ/4 for each single DInSAR displacement map (corresponding to about 6 cm for the ALOS-2 products) and applying the covariance propagation formula in Equation 4, the resulting uncertainties on the geodetic components can be estimated. It important to highlight that the resulting values differ from pixel to pixel, depending on the number of available displacements measurements (equations) and on the accuracy of the involved techniques (DInSAR or POT). The resulting mean standard deviation values are of about 20 cm for the retrieved vertical and east-west displacements, and of about 50 cm for what concerns the north-south deformation component. These findings are not surprising because the retrieved north-south displacements are predominantly constrained by the accuracy of the along-track information provided by the POT technique, while the vertical and east-west components significantly benefit from the exploited DInSAR measurements contributions in addition to those available through the POT. We also remark that the exploited 3D retrieval approach is rather effective and easy to implement although more advanced solutions, based on maximizing the positive impact of the DInSAR measurements higher accuracy, where available, are worth for future analyses.

The results obtained through the above-mentioned procedure are reported in Figure 8. This analysis allows us to observe that the maximum deformation occurred along the East-West direction with a displacement reaching approximately 530 cm toward the West and about 350 cm along the East, respectively. About the North-South direction we identified that the maximum deformation was about 390 cm toward the South and 360 cm along the North. Finally, the Vertical component results indicate a maximum uplift of about 185 cm together with a maximum subsidence value of 200 cm.

Figure 8

Figure 8. Retrieved displacement components: (a) East–West, (b) North–South, and (c) Vertical.

3 Seismology

3.1 Fault segmentation and inversion strategy

Amongst the main goals of our study was the definition of a detailed fault model. To this end, the first step was to define the fault segmentation. As constraints we used focal mechanisms derived from seismic waveforms, relocated earthquake hypocenters (Lomax, 2023), mapped fault traces (AFEAD database, Reitman et al., 2023) and discontinuities observed in the SAR-derived displacement fields. For the modelling we used as input the DInSAR data from ALOS-2 and Pixel Offset Tracking (POT) of Sentinel-1, and SAOCOM-1 satellites.

The important contribution of SAOCOM-1 data can be seen in Figure 9, where there is the comparison of all the sensors, shown for different areas of the faults. Comparing the different SAR-based deformation results, SAOCOM-1 has the best overall coverage; based on that, it enabled us to define the trace of the segments (the red line in the map in Figure 9) in very high detail. SAOCOM-1 is the only sensor that provided clear high quality and high resolution of both on-fault and near-fault information, making it a unique dataset to study the displacement of the February 2023 Türkiye-Syria earthquakes.

Figure 9

Figure 9. Comparison of the quality of the ALOS-2, Sentinel-1 and SAOCOM-1 sensors, for different areas of the causative structures (green pixels are No Data).

Fault modelling was performed using the analytical elastic dislocation formulation of Okada (1985). The procedure consists of a two-step inversion: a non-linear inversion with uniform slip to determine the primary geometrical and kinematic parameters, minimizing a chi-square misfit function through the Levenberg–Marquardt algorithm (Levenberg, 1944; Marquardt, 1963; Atzori et al., 2009), followed by a linear inversion to estimate the slip distribution, imposing non-negativity to avoid unphysical back-slip (Atzori et al., 2019).

In the case of the Türkiye-Syria faults, characterized by a huge number of segments, we had to adopt a specific strategy, to retrieve by inversion the highest possible number of parameters, without inverting them simultaneously. This approach is aimed at suppressing tradeoffs that make the solution highly non-unique. We firstly defined the segments required to approximate the fault trace, then we focused on the modeling of every single segment separately after an ad hoc local subsampling (some examples are shown in Figure 10). Thus, we built up, step-by-step, the uniform slip model by exploiting in each step the previous step’s optimum solution; for few specific cases we had to a priori constrain some parameters to achieve the optimization convergence (Supplementary Table S1).

Figure 10

Figure 10. Examples of the different samplings performed for the creation of the inversions’ input.

In the same way and based on these uniform slip solutions we then proceeded to the linear inversions to estimate the slip distributions, after subdividing the fault into patches of 2 × 2 km. More details of the inversion procedure adopted can be found in the Supplementary Material.

Our preferred model includes 22 fault segments. The rupture associated with the first mainshock (Mw 7.8) extends over ∼310 km, while that of the second event (Mw 7.5) is ∼150 km long. For reference, Figure 11 labels the northern segments as “N” and the southern ones as “S”. The results show that many of the causative faults are not vertical (Supplementary Table S1; Figure 12). Along the northern segments, the dip varies systematically: starting at ∼50° in the east, steepening westward, and returning to ∼50° near the western termination. The dominant kinematics are left-lateral strike-slip with additional dip-slip components on some segments. The southern segments (that in some previous studies were assumed to be vertical), exhibit more variable dip values, ranging from ∼50° to vertical.

Figure 11

Figure 11. Slip distribution model for the February 2023 Türkiye-Syria earthquakes.

Figure 12

Figure 12. Graphs showing the dip and rake variations of the fault segments.

The maximum slip, 15.1 m (±1.03), is located on the N segments associated with the second mainshock (Mw 7.5), rather than the first event. This may reflect either afterslip captured within the SAR acquisition window or the sharper moment release of the second rupture (Karabulut et al., 2023). The latter interpretation suggests that the northern fault system may have been close to failure and was dynamically triggered by the rupture of the southern segments and its aftershocks (Meng et al., 2024).

On the southern faults, slip reaches up to 14.7 m (±0.8) at ∼5 km depth, with several patches extending to the surface. The slip distribution is more heterogeneous than in the north, with five to six principal asperities distributed across multiple segments. Segment S4 shows the highest slip values. In contrast, the northern system is dominated by three to four slip patches, with the largest on segment N2, which also hosts the second mainshock. Slip uncertainties and resolution values can be found in Supplementary Figure S10, 11.

The seismic moment estimated of the northern rupture is 3.23 × 10²⁰ N m (Mw 7.6), while the southern rupture released 5.21 × 10²⁰ N m (Mw 7.8). These results indicate that both systems contributed significantly to the overall energy release, but with contrasting rupture styles and slip heterogeneity. Table 2 summarizes the results for the northern and southern segments of the rupture.

Table 2

Table 2. Comparative source parameters for the northern (N) and southern (S) rupture systems of the 6 February 2023 earthquakes.

3.2 Shakemap

ShakeMap computations were performed for the Mw 7.8 event, consistent with its dominant contribution to recorded ground shaking. The finite-fault rupture of the Mw 7.8 event exhibits strong along-strike variability in segment length, dip and rake. To model the spatial decay of ground shaking, we implemented the multi-segment geometry in the OpenQuake engine (Silva et al., 2014). For comparison, a point-source representation with the Akkar et al. (2014) Ground Motion Model (GMM) is shown in Figure 13a, where several observations exceed the two-standard-deviation threshold. This reflects the limitations of point-source metrics in cases of extended rupture.

Figure 13

Figure 13. (a) Decay of Peak Ground Acceleration (PGA) as a function of distance from the hypocentre, based on strong motion recordings (colour-coded triangles), compared with predictions from the Ground Motion Model (GMM) of Akkar et al. (2014) using a point-source approximation. The solid line represents the median prediction, while the dashed lines indicate one and two standard deviations. The largest observed values (red triangles) exceed the two-standard-deviation threshold, highlighting the limitations of simplified point-source modelling for complex rupture geometries. (b) The same dataset as in (a), now including predictions from the GMM of Boore et al. (2014) with the rupture modelled as a multi-planar surface and both GMMs employing the RJB distance metric. This configuration provides a substantially improved fit for the largest observed ground motions, particularly in the near field, aligning observations more closely with the median prediction. Observed values were obtained from the USGS ShakeMap for this event (https://earthquake.usgs.gov/earthquakes/eventpage/us6000jllz/shakemap/intensity).

A more consistent representation is obtained when rupture geometry is incorporated explicitly. Using the Boore et al. (2014) GMM with the RJB distance metric improves the match with observations, particularly within the near field (Figure 13b). The BO14 model was therefore adopted for the ShakeMap generation, due to its suitability for large magnitudes and its extended distance range. The earthquake magnitude was fixed at Mw 7.8, and coefficients appropriate for strike-slip rupture were used. Basin depth was treated as unknown, and the corresponding basin adjustment in the GMM was disabled, as recommended by the authors. PGA values were calculated by combining model predictions on a dense grid with recorded values from strong-motion stations. For each grid point, RJB was computed from the rupture surface; given the local Vs30 value, PGA was then evaluated using BO14.

Observed ground motions, originally reported as peaks of the two horizontal components, were converted to the rotationally independent GMRotD50 metric (Boore, 2010) using the relationships of Boore and Kishida (2017). Predicted PGA values were calculated on a grid based on the USGS Vs30 dataset (Heath et al., 2020; https://earthquake.usgs.gov/data/vs30/), considering all points within ∼400 km of the rupture, consistent with the upper applicability limit of BO14. To reduce computational cost, the grid was downsampled by a factor of 10, lowering the resolution from 0.0083° to 0.0833° (∼9 km) without loss of fidelity. Observed data (262 stations) and synthetic predictions (11,817 points) were merged, yielding a total of 12,079 points.

Figure 14 shows the distribution of PGA and the associated residuals. The finite-fault configuration captures the main patterns of strong shaking, with only a few stations located ∼20 km from the rupture exceeding model predictions. Some overestimation at larger distances (>50 km) is evident, consistent with known variability in long-period attenuation.

Figure 14

Figure 14. (a) Spatial distribution of Peak Ground Acceleration (PGA) values, expressed as rotationally independent mean (GMRotD50, Boore, 2010), derived from the combination of observed (colour-coded triangles) and calculated values, using the Ground Motion Model (GMM) of Boore et al. (2014). Calculations were performed over a grid with a resolution of 10 km (∼12,000 grid points), incorporating local site conditions. (b) Distribution of station residuals (colour-coded triangles) relative to the predictions of the Boore et al. (2014) GMM. The solid grey line is the median prediction; dashed lines denote one and two standard deviations (∼ 68% and 95% confidence intervals).

Stress redistribution along the fault planes (Supplementary Material), computed following Ripperger and Mai (2004), reveals zones of coseismic stress release that coincide with areas of maximum slip, particularly in the southeastern part of segment N2 during the second event. In contrast, extensive unruptured or low-slip portions of the faults retain elevated shear stress and may represent potential locations for future seismic activity.

4 Discussion

The model derived from combined DInSAR and Pixel Offset Tracking (POT) analysis of Sentinel-1, ALOS-2 and SAOCOM-1 datasets indicates slip of approximately 15 m for both southern and northern fault structures. Compared with other published slip-distribution models, Chen and Zhou (2024) report peak slips of 6 m for the first event and 8 m for the second. Typical peak slip values in the literature are ∼8 m for the Mw 7.8 earthquake and ∼10–12 m for the Mw 7.6 earthquake. Ergintav et al., 2024, estimate slip exceeding 7 m for one event and 9 m for the other. Barbot et al. (2023) report maximum slips of 8 m and 12 m for the first and second events, respectively, while Li et al. (2023) present a peak slip of approximately 10 m. Liu et al. (2023) found maximum slip values of 8.1 m for the first event and 11 m for the second, with slip uncertainties spanning 0–2 m. Among previous studies, we could say that our results are closer to those of Magen et al. (2024), who propose a maximum slip of about 15 m for the Mw 7.6 event. It is possible that the larger values retrieved here can be explained by the improved on-fault displacement data, provided by SAOCOM-1.

The joint seismic moment of the events is 8.44 × 10²⁰ N m, corresponding to a moment magnitude (Mw) of 7.9. Along-strike variability in slip distribution is pronounced, particularly in southern segments (segments S3, S7–S9), where reduced slip occurs at fault bends. This variability illustrates the influence of fault geometry on rupture propagation, consistent with the notion that geometrical complexities act as barriers or asperities, affecting the distribution and intensity of ground shaking (Liu et al., 2024). The rupture width remains shallower than 20 km, consistent with rupture-width saturation in large interplate strike-slip earthquakes and with the local Moho depth (∼32–38 km; Artemieva and Shulgin, 2019), supporting empirical scaling relationships (Leonard, 2010). The maximum slip in the S segments (in S4) occurred close to a segment junction. It’s possible that this high slip at the exact location could be the result of a strong focusing of rupture energy due to the junction.

As mentioned in Barbot et al. (2023), no discrete throughgoing faulting appears to have been activated in the intervening region between the N and S segments. This is indicating that rupture transfer likely occurred through dynamic or static stress interactions rather than along a continuous fault plane. We can see that in Figure 15 we have both horizontal and vertical movements. A distinctive feature of the 3D displacement field is the presence of opposing horizontal motions and significant uplift within the region between the southern (Mw 7.8) and northern (Mw 7.5) ruptures (Figures 15–17). North–south displacements show northward motion south of the southern rupture and alternating zones of southward and northward motion within the interfault region. East–west displacements reveal the expected sinistral pattern, with westward motion north of the southern rupture and a narrow zone of eastward motion in the interfault region (Figure 16).

Figure 15

Figure 15. North-South displacement of the February 2023 earthquakes as derived from the SAR analysis. Dashed black line indicates an area of northward movement within the interfault area. Light grey arrows are showing northward movement, darkgrey arrows are showing southward movement (arrows are indicative of the movement and not in scale). Blue lines are faults (from AFEAD database, Zelenin et al., 2022).

Figure 16

Figure 16. Upper map: East-West displacements as derived from the SAR analysis. Dashed black line indicates the eastern termination of eastward movement within the interfault area. Light grey arrows are showing eastward movement, darkgrey arrows are showing westward movement (arrows are indicative of the movement and not in scale). Bottom map: Geology of the area. Legend can be found in Figure 17. Blue lines are faults (from AFEAD database, Zelenin et al., 2022).

Figure 17

Figure 17. Upper map: East-West displacements as derived from the SAR analysis. The black line is the eastern termination of the uplifting pattern. White lines are velocity profiles shown in Figures 18, 19. Bottom map: Geology of the area. Blue lines are faults (from AFEAD database, Zelenin et al., 2022).

Except the expected movement change defined by the causative strike-slip faults, possibly the local geology and secondary faults also play a role in the expression of the horizontal components in the East-West direction (see the areas within the orange rectangle in Figure 16). We should note here that, in the interfault area, the forefront of the N-S opposing movements and the forefront of the E-W opposing movements (dashed black lines in Figure N–S and Figure E–W), coincides.

Vertical displacement shows a broad uplifted zone between the two ruptures, bounded by the mapped fault traces but not clearly associated with specific surface geological discontinuities (Figures 17–19). Profile AA′ displays a relatively smooth uplift of more than 1.5 m across a distance of ∼60 km, whereas profile BB′ exhibits sharper gradients consistent with fault-controlled deformation. These observations might suggest that both local fault structure and broader regional tectonics influence the uplift pattern.

Figure 18

Figure 18. Top profile: Elevation allong the AA’ profile (Figure 17). Bottom Profile: vertical dispalcement along allong the AA’ profile (Figure 17). Blue dots show subsidence and red uplift.

Figure 19

Figure 19. Top profile: Elevation allong the BB’ profile (Figure 17). Bottom Profile: vertical dispalcement along allong the BB’ profile (Figure 17). Blue dots show subsidence and red uplift.

This region lies at the boundary between the Arabian and Anatolian plates, where convergence is transferred to the EAFZ. The patterns of uplift and opposing horizontal motions are consistent with deformation within a restraining block situated between the two major ruptures (Figure 20). This block appears to rotate and uplift during the coseismic phase, accommodating shortening in a transpressional setting. Field surveys have documented localized thrust-like features and localized transtension and transpression, on different sites mainly on the locations of the causative structures (e.g., Pucci et al., 2025). Other InSAR studies have presented 3D displacement patterns (e.g., An et al., 2023) here, we focus on and analyse this large off-fault area inbetween the two faults. Our analysis indicates that plate convergence in this part of the EAF, expressed by the two major earthquake ruptures, is accommodated through the rotation and uplift of a compressional micro-block.

Figure 20

Figure 20. Sketch showing how convergence between the Arabian and Anatolian plates is accommodated in this part of the EAFZ: uplift and rotation of an interfault compressional micro-block between the southern (S) and northern (N) sinistral strike-slip fault systems.

Regarding the style of the previously analyzed activity in the area but also in a broader framework, the overall collisional mechanism is complex. The activity might be related with pre-existing structures and on how the stress acts in the brittle upper crust and by the geology of the area. Also, elevated pore pressure changes along pre-existing structures might have produced alteration in the frictional behavior contributing to this expression. We note that, it has been reported that shallow but also deep processes have played a role in the postseismic displacement expression of the 2023 February Türkiye-Syria events (Liu et al., 2025). At the same time, the fault zone creation could have also been caused by variations in strength in the lithosphere (Delph et al., 2024). At the study area, the S-wave velocities are relatively slow in the Anatolian plate, the crust has a thickness of 40–45 km and the lithosphere is thin, about 60 km; on the contrary the Arabian plate is having a thinner crust (30 km) and a thicker lithosphere (60–70 km) (Abgarmi et al., 2017; Delph et al., 2017; Hua et al., 2020; Ogden and Bastow 2022). Delph et al. (2024) reported that at the area, the Anatolian plate is weaker (there is ductile deformation related to its thinner lithosphere) than the Arabian plate (it has a strong lithospheric mantle). The Arabian plate underthrusts beneath the Anatolian (Figure 21), with the specific rheological differences. This process might be a factor that contributes to the concentration of stress, transmitted to the surface, to the seismicity expression and the faulting style (Delph et al., 2024). Part of the area in-between the S and N segments that hosts this rotational micro-block, lies within a broad deformation zone with unstable tectonics, since it is exactly at the collisional front. It’s possible that together with the shallow processes, there are also lithospheric controls that might had influenced the expression and deformation style of the 2023 Türkiye-Syria seismic events. The available data do not allow us to distinguish whether the uplift originates primarily from shallow fault geometry or deeper lithospheric structure; both interpretations remain plausible within the resolution of the current dataset.

Figure 21

Figure 21. Conceptual sketch illustrating convergence and rheological contrasts in the study area. A mechanically stronger Arabian plate underthrusts a weaker Anatolian plate, influencing stress transmission, faulting, and seismicity expression (after Delph et al., 2024).

Overall, the 2023 Türkiye–Syria earthquake sequence demonstrates how distributed deformation, geometric complexities and variable fault kinematics combine to shape rupture propagation and off-fault deformation. The uplifted and rotating block between the two ruptures represents a significant expression of strain partitioning at the Arabia–Anatolia boundary and may influence future patterns of seismic activity along this part of the East Anatolian Fault Zone.

5 Conclusion

The February 2023 Kahramanmaraş events demonstrated a rather unexpected rupture propagation across multiple fault segments. This study provides high-resolution constraints on fault behaviour, coseismic deformation, and stress redistribution for one of the most significant recent earthquake sequences in the Eastern Mediterranean. The combination of SAR-derived displacement fields, detailed slip modelling, stress analysis, and rupture-aware ground motion simulations, enhances the understanding of the mechanics of large interplate strike-slip earthquakes, identifies fault segments and structural complexities that may govern future seismic hazard and sheds light into the collisional processes between Arabian and Anatolian plate.

We presented and exploited for the first time SAOCOM-1 results for the Türkiye-Syria February 2023 seismic events, overcoming the limitations of the use of Sentinel-1 satellite whose azimuth POT products were of low quality. SAOCOM-1 results provided a better spatial coverage, important input for a detailed definition of the fault trace and on-fault/near-fault displacement measurements. Based on DInSAR and Pixel Offset Tracking dataset of ALOS-2, Sentinel-1 and SAOCOM-1 we were able to create a detailed slip model. The slip distribution shows marked along-strike variability, with reduced slip at major bends and concentrated patches near segment junctions. The rupture width remains less than 20 km, consistent with the regional crustal structure.

The finite-fault configuration improves the representation of near-field ground shaking compared with point-source formulations. The updated ShakeMap, based on the Boore et al. (2014) Ground Motion Model, captures the observed spatial pattern of strong shaking and illustrates the importance of incorporating rupture geometry when modelling ground motions from large strike-slip earthquakes. Static stress changes derived from the slip model, presented in the Supplementary Material, highlight low-slip areas that remained unbroken and may influence future seismic activity.

The three-dimensional displacement field reveals a broad uplifted region between the two ruptures, accompanied by opposing horizontal motions. These observations indicate that deformation is distributed within an interfault block situated at the Arabia–Anatolia plate boundary. The uplifted and rotating character of this block suggests a transpressional environment where shortening is accommodated during major ruptures. This feature has not been documented previously for the 2023 sequence and provides new constraints on strain transfer along the East Anatolian Fault Zone.

The kinematics of this compressional block, together with previously documented rheological contrasts between the Arabian and Anatolian lithosphere, may influence how strain is partitioned across this part of the plate boundary (as proposed by Delph et al., 2024). Improved understanding of this structure is important for assessing long-term deformation patterns and future seismic hazard. Further work, including continuous geodetic monitoring, detailed field surveys and paleoseismological investigations, is needed to evaluate its persistence and role in accommodating collision-related deformation.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author contributions

NS: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Resources, Validation, Visualization, Writing – original draft, Writing – review and editing. PS: Data curation, Formal Analysis, Methodology, Resources, Visualization, Writing – original draft, Writing – review and editing. SA: Conceptualization, Data curation, Formal Analysis, Methodology, Software, Validation, Writing – original draft, Writing – review and editing. MB: Formal Analysis, Writing – review and editing. NV: Data curation, Formal Analysis, Methodology, Visualization, Writing – original draft, Writing – review and editing. CT: Data curation, Formal Analysis, Writing – review and editing. AK: Conceptualization, Funding acquisition, Writing – original draft, Writing – review and editing. FC: Formal Analysis, Methodology, Software, Writing – review and editing. CB: Writing – review and editing. CD: Writing – review and editing. MP: Investigation, Writing – review and editing. MF: Formal Analysis, Writing – review and editing. AA: Writing – review and editing. MM: Writing – review and editing. FM: Resources, Writing – review and editing. YB: Writing – review and editing. RL: Conceptualization, Funding acquisition, Writing – review and editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was supported in part by the Italian Civil Protection Department (DPC) within the framework of the IREA-DPC (2022–2024) agreement; the contents do not necessarily reflect the official opinions or policies of the DPC. Additional support was provided by the European Union–Next Generation EU (PNRR-M4C2) under Project CN-MOST (CN00000023), Project ICSC–CN-HPC (CN00000013), Project MEET IR (IR0000025), and Project GeoSciences IR (IR0000037), as well as by the National Operational Program Infrastructures and Networks 2014–2020 of the Italian Ministry of Infrastructure and Transport under Project GRINT (PIR01_00013) and Project IBiSCo (PIR01_00011). Support from the Geo-INQUIRE project (GA 101058518) is also acknowledged. AK acknowledges support from EPOS-ON project (GA 101131592).

Acknowledgements

Strong motion recordings were retrieved from AFAD (Disaster and Emergency Management Presidency, https://deprem.afad.gov.tr) and KOERI (Kandilli Observatory and Earthquake Research Institute, http://www.koeri.boun.edu.tr). Seismic Data can be also accessed via the EPOS Platform https://www.epos-eu.org/dataportal. Geological map is from 1:500,000 scale Geology Map of Türkiye, prepared and published by the Institute of Mineral Research and Exploration (MTA), Ankara - Turkey, 1961. Downloaded from https://www.dotaltai.org/documents/geology-map-of-turkiye. Active fault mapping used in this study incorporates the Active Faults of Eurasia Database (AFEAD; Zelenin et al., 2022), which is openly available at https://doi.org/10.5194/essd-14-4489-2022. The authors thank the Italian Space Agency (ASI) for providing SAOCOM-1 data under the ASI-CONAE SAOCOM-1 License to Use Agreement. This article includes modified Copernicus Sentinel data 2024; the contributions of the European Commission, Copernicus Program, and ESA in supporting systematic Sentinel-1 acquisitions and open data policy are gratefully acknowledged. ALOS-2 data were provided by JAXA under the framework of the third Research Announcement on Earth Observations Collaborative Research Agreement (PI nr. ER3A2N543). The DEM of the study area was obtained from the NASA SRTM archive. Some figures were produced using GMT (https://www.generic-mapping-tools.org/) and Python (Python Software Foundation, https://www.python.org/). The authors thank all institutions whose data and processing infrastructures supported this work. The slip distribution models created in this study are available in the supplementary material of this paper and the finite source database of the Istituto Nazionale di Geofisica e Vulcanologia – INGV site, https://terremoti.ingv.it/en/finitesource.

Conflict of interest

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The authors CB, AK declared that they were an editorial board member of Frontiers at the time of submission. This had no impact on the peer review process and the final decision.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feart.2025.1717560/full#supplementary-material

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