We derive the ALOS‐2 coseismic interferograms, pixel‐offsets and Sentinel‐2 sub‐pixel offsets of the 2023 M_w7.8 and M_w7.7 Kahramanmaras, Turkey earthquake sequence. Offset maps show that the sequence ruptured ∼300 km along the East Anatolian Fault (EAF) and ∼180 km along the secondary Cardak and Dogansehir faults. We infer the coseismic slip distribution and interseismic fault motion by inverting the co‐ and inter‐seismic observations. Inversion results show that the coseismic slip (∼8.0 m) and interseismic strike‐slip rate (∼4.6 mm/yr) on the main rupture of the M_w7.8 event are basically consistent with the ∼8.4 m and ∼3.9 mm/yr of the M_w7.7 event. Most coseismic slips of the M_w7.8 and M_w7.7 events occur within 10 and 12 km at depth, respectively, in keeping with the interseismic locking depth of 10.4 ± 3.3 km and 11.1 ± 3.1 km. This implies that the coseismic rupture kinematics correlate with the interseismic strain accumulation. Moreover, static stress changes show that the M_w7.7 event is likely promoted by ∼2 bar stress increase from the M_w7.8 event on the central section of its main rupture.

The May 22nd, 2021,7.4 Maduo earthquake occurred on an intraplate fault of Bayan‐Har block in the Tibet Plateau. Here, we derive the coseismic and early postseismic surface deformations from the Sentinel‐1 (S1) interferometric synthetic aperture radar (InSAR) data. We use the subpixel offsets of SAR and Sentinel‐2 (S2) optical images to determine the surface rupture traces. The fault geometry and coseismic fault slip distribution of multi‐segmented ruptures are estimated by inverting InSAR interferograms and SAR pixel offsets. We show that at least five fault segments with curved geometry are activated, with a peak coseismic slip of about 5 m. The geodetic data inversion suggests that an NW‐striking blind segment near the Eling Lake may have ruptured during the 2021 event. Postseismic slip inversion with the 60‐day cumulative line‐of‐sight deformations, shows that the early afterslip of 0.1 ∼ 0.3 m occurred mostly toward the downdip direction of the main coseismic slip asperities. The afterslip geodetic moment accounts for approximately 15.3% of the coseismic one. Coulomb stress analysis shows that the nucleation of the Maduo earthquake is partially facilitated by the 1947 M7.7 Dari earthquake.

On February 6, 2023 (local time), two earthquakes (Mw7.8 and Mw7.7) struck central and southern Turkey, causing extensive damage to several cities and claiming a toll of 40,000 lives. In this study, we propose a method for seismic building damage assessment and analysis by combining SAR amplitude and phase coherence change detection. We determined building damage in five severely impacted urban areas and calculated the damage ratio by measuring the urban area and the damaged area. The largest damage ratio of 18.93% is observed in Nurdagi, and the smallest ratio of 7.59% is found in Islahiye. We verified the results by comparing them with high-resolution optical images and AI recognition results from the Microsoft team. We also used pixel offset tracking (POT) technology and D-InSAR technology to obtain surface deformation using Sentinel-1A images and analyzed the relationship between surface deformation and post-earthquake urban building damage. The results show that Nurdagi has the largest urban average surface deformation of 0.48 m and Antakya has the smallest deformation of 0.09 m. We found that buildings in the areas with steeper slopes or closer to earthquake faults have higher risk of collapse. We also discussed the influence of SAR image parameters on building change recognition. Image resolution and observation geometry have a great influence on the change detection results, and the resolution can be improved by various means to raise the recognition accuracy. Our research findings can guide earthquake disaster assessment and analysis and identify influential factors of earthquake damage.

In February 2023, Mw 7.8 and Mw 7.7 earthquakes struck southeastern Turkey. Generating a coseismic 3D deformation field that can directly reflect the characteristics of surface deformation is important for revealing the movement mode of a seismogenic fault and analyzing the focal mechanism. Optical image sub-pixel correlation (SPC) only captures deformation in the horizontal direction, and SAR image pixel offset tracking (POT) obtains range deformation that is not sensitive to north–south deformation signals. Thus, neither of them can capture the complete 3D deformation alone. Combining them may be able to allow the monitoring of 3D deformation. In this study, we used Sentinel-2 optical images to obtain the horizontal deformation (east–west and north–south) and Sentinel-1 and ALOS-2 data to extract the range and azimuth offsets. The least-squares method was used to fuse the optical and SAR offsets to obtain the 3D deformation field of the 2023 Turkey earthquake sequence, which indicates that the two events were both left-lateral strike-slip earthquakes. The surface deformation caused by the two large earthquakes is mainly in the east–west direction. In the vertical direction, the two earthquakes caused a small-magnitude uplift and subsidence. The findings in this paper can be used as a reference for the study of coseismic 3D deformation.

The joint action of human activities and environmental changes contributes to the frequent occurrence of landslide, causing major hazards. Using Interferometric Synthetic Aperture Radar (InSAR) technique enables the detailed detection of surface deformation, facilitating early landslide detection. The growing availability of SAR data and the development of artificial intelligence have spurred the integration of deep learning methods with InSAR for intelligent geological identification. However, existing studies using deep learning methods to detect landslides in InSAR deformation often rely on single InSAR data, which leads to the presence of other types of geological hazards in the identification results and limits the accuracy of landslide identification. Landslides are affected by many factors, especially topographic features. To enhance the accuracy of landslide identification, this study improves the existing geological hazard detection model and proposes a multi-source data fusion network termed MSFD-Net. MSFD-Net employs a pseudo-Siamese network without weight sharing, enabling the extraction of texture features from the wrapped deformation data and topographic features from topographic data, which are then fused in higher-level feature layers. We conducted comparative experiments on different networks and ablation experiments, and the results show that the proposed method achieved the best performance. We applied our method to the middle and upper reaches of the Yellow River in eastern Qinghai Province, China, and obtained deformation rates using Sentinel-1 SAR data from 2018 to 2020 in the region, ultimately identifying 254 landslides. Quantitative evaluations reveal that most detected landslides in the study area occurred at an elevation of 2500–3700 m with slope angles of 10–30°. The proposed landslide detection algorithm holds significant promise for quickly and accurately detecting wide-area landslides, facilitating timely preventive and control measures.

Since the occurrence of the 2019 M_w 6.4 and M_w 7.1 Ridgecrest earthquake sequence in the Eastern California Shear Zone, coseismic deformation following the two earthquakes has been intensively studied, and source modeling with interferometric synthetic aperture radar (InSAR), Global Positioning System (GPS) and seismological datasets has been favored. However, we recently found that the coseismic modeling of the two earthquakes constrained by the dense near‐field Planet‐Lab optical measurements can be more detailed than the previously estimated, which requires an accurate Planet‐Lab displacement. In this study, we improve the long wavelength orbital ramps correction method for obtaining a more accurate Planet‐Lab horizontal displacement field. The corrected dense near‐field Planet‐Lab data are firstly used together with the intermediate‐field GPS data to invert and distinguish the fault slip distribution for the two earthquakes. The same scheme is used to simultaneously invert the InSAR, SAR, GPS, and optical datasets, to improve the constraints on seismic source parameters. Our inversion results show that the joint‐event slip model is rougher than the two single‐event slip models, but it has a more concentrated slip pattern and larger slip amplitude in some zones. We show that adding a near‐field constraint of the Planet‐Lab data in the combined‐data inversion can reduce the slip parameter uncertainty and enhance the model resolution. The Coulomb failure stress changes on the southeastern Blackwater, the southern Owens Valley, and the central Panamint Valley faults are enhanced by about 0.4–0.8 bar by the 2019 Ridgecrest earthquake sequence.