The May 22nd, 2021, Mw ${\mathrm{M}}_{\mathrm{w}}$ 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.
Plain Language Summary
We derive the co‐ and postseismic deformations and fault‐slip distributions of the 2021 Maduo earthquake from the space‐based geodetic data. We find that the pattern of eastward and upward postseismic deformations is similar to that of the coseismic left‐lateral motions, and the 60‐day early afterslip of 0.1∼0.3 m occurrs mostly toward the down‐dip of the main coseismic slip asperities. We propose a six‐segment geological structure model with varied dips and orientations to better recover the actual surface deformations. We find that at least five fault segments are activated from west to east. We examine the stress loading induced by the 1947 M7.7 Dari and the 2021 Maduo earthquakes. We find that nucleation of the 2021 event is partially facilitated by the 1947 event, and the 2021 event increases the potential seismic risk on the Tuosuo Lake and Maqin segments of the East Kunlun fault.
Key Points
Co‐ and post‐seismic deformations and fault‐slip distributions of the 2021 Maduo earthquake are derived from space‐based geodetic data
A six‐segment geological structure model with varied dips and orientations is proposed for interpretation of the Maduo event
The 1947 Dari earthquake increases stress in the western rupture zones and partially promotes the fault failure of the Maduo event
We derive the ALOS‐2 coseismic interferograms, pixel‐offsets and Sentinel‐2 sub‐pixel offsets of the 2023 Mw7.8 and Mw7.7 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 Mw7.8 event are basically consistent with the ∼8.4 m and ∼3.9 mm/yr of the Mw7.7 event. Most coseismic slips of the Mw7.8 and Mw7.7 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 Mw7.7 event is likely promoted by ∼2 bar stress increase from the Mw7.8 event on the central section of its main rupture.
Plain Language Summary
The middle and northern sections of the East Anatolian Fault (EAF) have experienced seven major earthquakes (M > 6.0) since the twentieth century, in accordance with the fast slip rate (∼10.5 mm/yr) and shallow locking depth (∼5 km) (Bletery et al., 2020, https://doi.org/10.1029/2020gl087775), leaving a well‐known seismic gap, the Pazarcık segment in the southern section of the EAF. Stress analysis by Nalbant et al. (2002, https://doi.org/10.1016/s0012-821x(01)00592-1) suggested that this seismic gap has potential to produce an Mw ≥ 7.3 earthquake. The 2023 Mw7.8 and Mw7.7 Kahramanmaras, Turkey earthquake sequence ruptured the Pazarcık segment. This earthquake sequence offers a valuable opportunity to explore the critical stage of the seismic cycle from interseismic strain accumulation to coseismic rupture. We extract the surface fault traces from the deformation maps derived from the ALOS‐2 interferometric synthetic aperture radar (InSAR), pixel offset and Sentinel‐2 sub‐pixel offset measurements, and then construct a seven‐segment fault geometric model according to the fault segmentation based on Duman and Emre (2013, https://doi.org/10.1144/SP372.14). By inverting the coseismic interferograms and pixel offsets and the interseismic LOS velocities from Weiss et al. (2020, https://doi.org/10.1029/2020GL087376), we determine the coseismic slip model of Mw7.8 and Mw7.7 earthquakes, and relate it to the interseismic kinematics.
Key Points
We drive a complete series of coseismic deformation maps and detailed slip distribution of the 2023 Kahramanmaras earthquakes
The Mw7.7 event produced normal dip‐slip (∼6 m) near the Goksun releasing bend and thrust dip‐slip (∼2 m) on the Dogansehir fault
The coseismic slip behaviors on the Cardak and Pazarcık faults correlate with the interseismic kinematics
SUMMARY
We study the 2016 January 21 (${{{M}}}_{\rm{w}}$ 5.9) and 2022 January 8 (${{{M}}}_{\rm{w}}$ 6.7) earthquake sequence that struck the Menyuan region in northwest China's Qinghai province. ...These two earthquakes are destructive events that occurred around/on the Lenglongling fault (LLLF). Here, we derive the line-of-sight displacement fields of the two earthquakes using Interferometric Synthetic Aperture Radar (InSAR) measurements of Sentinel-1 SAR data, and map the range and horizontal offset fields of the 2022 event using Sentinel-1 amplitude images and Planet-Lab optical images. Based on the offset maps, we determine the detailed surface rupture trace of the 2022 event. We perform slip inversions for the two earthquakes on triangle fault patches whose size increases with depth. Results show that the western branch segment of the 2022 event has a ∼0.5-m normal dip-slip motion. This result contradicts previous inferences on dip-slip sense of this branch segment. We identify a left-stepping fault structure with a ∼5-km step width in the transition zone between the Tuolaishan fault (TLSF) and LLLF, which may serve as a kinematic barrier to prevent further propagation of seismic rupture along the TLSF. Stress calculation shows that a stress drop of ∼0.4 bar produced by the 2016 event on a ∼5-km long LLLF segment may act as a negative stress barrier to suppress rupture propagation of the 2022 event toward the southeast of the LLLF.
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.
Landslide-dammed lakes and their breaches can reactivate/accelerate landslides, causing potential damages. However, in the absence of displacement observations, the spatial-temporal deformation ...patterns of the landslide-dammed lakes and associated floods reactivated/accelerated landslides remain underexplored. In this article, we use the 2018 Baige landslide-dammed lake and associated floods reactivated/accelerated landslides to investigate the behaviors of such landslides. We first use an improved interferograms Stacking method to detect landslides, then utilize the multitemporal interferometric synthetic aperture radar to derive their deformation history. Through retrospective analysis of the deformation history and optical images, we find that the water level fluctuations and floods caused by the Baige landslide-dammed lake and its breaches reactivated/accelerated six landslides upstream and five landslides downstream. The area of the largest reactivated/accelerated landslide is about 5 km 2 . The maximum velocity change of these reactivated/accelerated landslides is about 20 cm/year. Landslide reactivation/acceleration occurs progressively from the toe to head, resulting in varying reactivation/acceleration times for different parts. The velocity change and acceleration area have a linear relationship, with larger landslides showing larger velocity changes and prolonged activity than smaller ones. Among these 11 reactivated/accelerated landslides, 4 are located in the river narrow sections and their volumes are all larger than that of the Baige landslide. Thus, their failure may cause larger damages than that caused by the Baige landslide. Our findings contribute to a better understanding of landslide-induced geological hazard chains and landslide behaviors.
•Obtain the long-term and regional-scale InSAR deformations of JSOAA.•Locate many subsidence areas in JSOAA, which are expanding.•Model the groundwater overpumping related settlement by an analytical ...model.•The aquifer in JSOAA is suffering permanent loss.
Groundwater is the main water source for agricultural irrigation in arid/semi-arid agricultural region. Overexploitation of groundwater inevitably leads to permanent loss of aquifer and ground subsidence. The oasis agricultural area in the southern Junggar basin (JSOAA) is one of the largest oasis agricultural areas in western China. In this study, we, for the first time, recover the regional-scale ground displacements time-series of JSOAA, using all ALOS-1/PALSAR (2007–2010) and Sentinel-1 (2015–2020) data. The results show that there are multiple subsidence areas related to groundwater overexploitation. From 2007 to 2010, the area with a subsidence rate greater than 10 mm/yr is about 5876.2 km2, accounting for 13.2 % of the total area of JSOAA. From 2015 to 2020, these values are about 16146.7 km2 and 36.3 %. In the areas with concentrated groundwater exploitation, the small separate subsidence areas grew larger and became connected, and finally developed into giant subsidence clusters. The maximum cumulative deformation of JSOAA exceeded 400 mm from 2007 to 2010, and 500 mm from 2015 to 2020. We modeled the surface subsidence caused by the permanent aquifer loss, and estimated the volume strain of aquifer in JSOAA. The total volume strain of the aquifer is 2.73 km3 between 2007 and 2020. Moreover, we estimated the aquifer storage coefficient and the net groundwater deficit of JSOAA. The results of this study will serve for JSOAA aquifer health detection, ecological environment stability assessment, and sustainable development of the agricultural economy.
The optical image sub-pixel correlation (SPC) technique is an important method for monitoring large-scale surface deformation. RapidEye images, distinguished by their short revisit period and high ...spatial resolution, are crucial data sources for monitoring surface deformation. However, few studies have comprehensively analyzed the error sources and correction methods of the deformation field obtained from RapidEye images. We used RapidEye images without surface deformation to analyze potential errors in the offset fields. We found that the errors in RapidEye offset fields primarily consist of decorrelation noise, orbit error, and attitude jitter distortions. To mitigate decorrelation noise, the careful selection of offset pairs coupled with spatial filtering is essential. Orbit error can be effectively mitigated by the polynomial fitting method. To address attitude jitter distortions, we introduced a linear fitting approach that incorporated the coherence of attitude jitter. To demonstrate the performance of the proposed methods, we utilized RapidEye images to extract the coseismic displacement field of the 2019 Ridgecrest earthquake sequence. The two-dimensional (2D) offset field contained deformation signals extracted from two earthquakes, with a maximum offset of 2.8 m in the E-W direction and 2.4 m in the N-S direction. A comparison with GNSS observations indicates that, after error correction, the mean relative precision of the offset field improved by 92% in the E-W direction and by 89% in the N-S direction. This robust enhancement underscores the effectiveness of the proposed error correction methods for RapidEye data. This study sheds light on large-scale surface deformation monitoring using RapidEye images.
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.
