On 6 February 2023, a devastating doublet of earthquakes with magnitudes of Mw 7.8 and Mw 7.6 successively struck southeastern Turkey near the border of Syria. The earthquake sequence represents the strongest earthquakes in Turkey during the past 80 years and caused an extensive loss of life and property. In this study, we processed Sentinel-1 and GPS data to derive the complete surface displacement caused by the earthquake sequence. The surface displacements were adopted to invert for the fault geometry and coseismic slip distribution on the seismogenic faults of the earthquake sequence. The results indicate that the coseismic rupture of the Turkey earthquake sequence was dominated by left-lateral strike slips with a maximum slip of ~10 m on the East Anatolian Fault Zone (EAFZ) and the Sürgü fault (SF). Significant surface ruptures are recognized based on the geodetic inversion, which is consistent with the analysis of post-earthquake satellite images. The cumulative released moment of the two earthquakes reached 9.62 × 10²⁰ Nm, which corresponds to an event of Mw 7.95. Additionally, the interseismic fault slip rates and locking depths along the central and western segments of the EAFZ were estimated using the high-resolution long-term velocity field. The results reveal significant lateral variations of fault slip rates and locking depths along the central and western segments of the EAFZ. Generally, the estimated fault locking zone showed good spatial consistency with the coseismic fault rupture of the Mw 7.8 shock on the EAFZ. The static coulomb failure stress (CFS) change due to the Mw 7.8 earthquakes suggests that the subsequent Mw 7.6 event was certainly promoted by the Mw 7.8 shock. The stress transfers from the fault EAFZ to the fault SF were realized by unclamping the interface of the fault SF, which significantly reduces the effective normal stress on the fault plane. Large CFS increases in the western Puturge segment of the EAFZ, which was not ruptured in the 2020 Mw 6.8 and the 2023 Mw 7.8 earthquakes, highlight the future earthquake risk in this fault segment.

The knowledge of Arctic Sea ice coverage is of particular importance in studies of climate change. This study develops a new sea ice classification approach based on machine learning (ML) classifiers through analyzing spaceborne GNSS-R features derived from the TechDemoSat-1 (TDS-1) data collected over open water (OW), first-year ice (FYI), and multi-year ice (MYI). A total of eight features extracted from GNSS-R observables collected in five months are applied to classify OW, FYI, and MYI using the ML classifiers of random forest (RF) and support vector machine (SVM) in a two-step strategy. Firstly, randomly selected 30% of samples of the whole dataset are used as a training set to build classifiers for discriminating OW from sea ice. The performance is evaluated using the remaining 70% of samples through validating with the sea ice type from the Special Sensor Microwave Imager Sounder (SSMIS) data provided by the Ocean and Sea Ice Satellite Application Facility (OSISAF). The overall accuracy of RF and SVM classifiers are 98.83% and 98.60% respectively for distinguishing OW from sea ice. Then, samples of sea ice, including FYI and MYI, are randomly split into training and test dataset. The features of the training set are used as input variables to train the FYI-MYI classifiers, which achieve an overall accuracy of 84.82% and 71.71% respectively by RF and SVM classifiers. Finally, the features in every month are used as training and testing set in turn to cross-validate the performance of the proposed classifier. The results indicate the strong sensitivity of GNSS signals to sea ice types and the great potential of ML classifiers for GNSS-R applications.

The Sikkim–Bhutan seismic gap has witnessed a long earthquake quiescence since the 1714 M7.5~8.5 earthquake. The state of stress accumulation beneath the Sikkim–Bhutan Himalaya and its spatial correlation with seismicity remains unclear due to the lack of geodetic measurements and the low levels of seismic activity. We compile Global Positioning System (GPS) measurements in southern Tibet with the available velocities in the Sikkim–Bhutan Himalaya to reveal the characteristics of strain buildup on the Main Himalayan Thrust (MHT). We correct non-tectonic hydrological loading effects in a GPS time series to accurately determine the Three-Dimensional (3D) velocities of each continuous station. Extensive GPS measurements yield convergence rates of 16.2~18.5 mm/y across the Sikkim–Bhutan Himalaya, which is quite consistent with that observed elsewhere in the Himalaya. Based on a double-ramp structure of the MHT, a refined 3D coupling image is inverted using a dense network of GPS velocities. The result indicates significant along-strike variations of fault coupling beneath the Sikkim–Bhutan Himalaya. The locking width (coupling > 0.5) of western Bhutan reaches ~100 km, which is 30~40% wider than Sikkim and eastern Bhutan. An obvious embayment of decoupling zone near the border between Sikkim and western Bhutan is recognized, and coincides spatially with the rupture terminates of the 1934 Mw8.2 and the 1714 M7.5~8.5 earthquakes, indicating that the large megathrust earthquakes along the Sikkim–Bhutan Himalaya are largely segmented by the spatial variation of frictional properties on the MHT. Using a new compilation of seismic records in the Sikkim–Bhutan Himalaya, we analyze the spatial correlation between fault coupling and seismic activity. The result suggests that the seismicity in the Bhutan Himalaya is broadly distributed, instead of restricted in the lower edge of the interseismic locking zone. This implies that the seismic activity in the Bhutan Himalaya is not uniquely controlled by the stress accumulation at the downdip end of the locked portion of the MHT.

Two effective machine learning-aided sea ice monitoring methods are investigated using 42 months of spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data collected by the TechDemoSat-1 (TDS-1). The two-dimensional delay waveforms with different Doppler spread characteristics are applied to extract six features, which are combined to monitor sea ice using the decision tree (DT) and random forest (RF) algorithms. Firstly, the feature sequences are used as input variables and sea ice concentration (SIC) data from the Advanced Microwave Space Radiometer-2 (AMSR-2) are applied as targeted output to train the sea ice monitoring model. Hereafter, the performance of the proposed method is evaluated through comparing with the sea ice edge (SIE) data from the Special Sensor Microwave Imager Sounder (SSMIS) data. The DT- and RF-based methods achieve an overall accuracy of 97.51% and 98.03%, respectively, in the Arctic region and 95.46% and 95.96%, respectively, in the Antarctic region. The DT- and RF-based methods achieve similar accuracies, while the Kappa coefficient of RF-based approach is slightly larger than that of the DT-based approach, which indicates that the RF-based method outperforms the DT-based method. The results show the potential of monitoring sea ice using machine learning-aided GNSS-R approaches.

The Tanlu fault zone, extending over 2400 km from South China to Russia, is one of the most conspicuous tectonic elements in eastern Asia. In this study, we processed the Global Positioning System (GPS) measurements of Anhui Continuously Operating Reference System (AHCORS) between January 2013 and June 2018 to derive a high-precision velocity field in the central and southern segments of the Tanlu fault zone. We integrated the AHCORS data with those publicly available for geodetic imaging of the interseismic coupling and slip rate deficit distribution in the central and southern segments of the Tanlu fault zone. This work aims at a better understanding of strain accumulation and future seismic hazard in the Tanlu fault zone. The result indicates lateral variation of coupling distribution along the strike of the Tanlu fault zone. The northern segment of the Tanlu fault zone has a larger slip rate deficit and a deeper locking depth than the southern segment. Then, we analyzed three velocity profiles across the fault. The result suggests that the central and southern segments of the Tanlu fault zone are characterized by right-lateral strike-slip (0.29–0.44 mm/y) with compression components (0.35–0.76 mm/y). Finally, we estimated strain rates using the least-squares collocation method. The result shows that the dilatation rates concentrate in the region where the principal strain rates are very large. The interface of extension and compression is always accompanied by sudden change of direction of principal strain rates. Especially, in the north of Anhui, the dilatation rate is largest, reaching $3.780\times {10}^{-8}/a$. Our study suggests that the seismic risk in the northern segment of the Tanlu fault zone remains very high for its strong strain accumulation and the lack of historical large earthquakes.

The frequent occurrence of extreme climate events has a significant impact on people’s lives. Heavy rainfall can lead to an increase of regional Terrestrial Water Storage (TWS), which will cause land subsidence due to the influence of hydrological load. At present, regional TWS is mostly obtained from Gravity Recovery and Climate Experiment (GRACE) data, but the method has limitations for small areas. This paper used water level and flow data as hydrological signals to study the land subsidence caused by heavy rainfall in the Chaohu Lake area of East China (June 2016–August 2016). Pearson’s correlation coefficient was used to study the interconnection between water resource changes and Global Navigation Satellites System (GNSS) vertical displacement. Meanwhile, to address the reliability of the research results, combined with the Coefficient of determination method, the research findings were validated by using different institutional models. The results showed that: (1) During heavy rainfall, the vertical displacement caused by atmospheric load was larger than non-tidal oceanic load, and the influence trends of the two were opposite. (2) The rapidly increasing hydrologic load in the Chaohu Lake area resulted in greater subsidence displacement at the closer CORS station (CHCH station) than the more distant CORS station (LALA station). The Pearson correlation coefficients between the vertical displacement and water level were as high as −0.80 and −0.64, respectively. The phenomenon confirmed the elastic deformation principle of disc load. (3) Although there was a systematic bias between the different environmental load deformation models, the deformation trends were generally consistent with the GNSS monitoring results. The average Coefficients of determination between the different models and the GNSS results were 0.63 and 0.77, respectively. The results demonstrated the effectiveness of GNSS in monitoring short-term hydrological load. This study reveals the spatial-temporal evolution of land deformation during heavy rainfall around Chaohu Lake, which is of reference significance for water resource management and infrastructure maintenance in this area.