Between January and April 2022, three moderate earthquakes (Mw 5.6 on 23 January, Mw 5.7 on 25 March, and Mw 5.1 on 15 April) struck the Hala Lake area of Delingha, Qinghai, China. Their seismogenic faults are poorly mapped, resulting in an unclear understanding of their kinematics and regional seismotectonics. In this study, we employed Interferometric Synthetic Aperture Radar (InSAR) observations to reconstruct the coseismic deformation fields of the Mw 5.6 and 5.7 events. We then utilized a Bayesian inversion algorithm to delineate the fault geometries of the two events, and further resolved their coseismic fault slip. Our results reveal that these earthquakes ruptured different fault planes: the fault plane of the Mw 5.6 event dips westward at an angle of 60°, while the Mw 5.7 event ruptured as a nearly vertical fault with a dipping angle of 89°. The finite-fault slip inversions further demonstrate that the coseismic rupture of the Mw 5.6 event was predominantly concentrated between depths of 2 km and 7 km, with a maximum slip of 0.18 m; in contrast, the Mw 5.7 event was mainly concentrated between depths of 2 km and 9 km, with a maximum slip of 0.4 m. We calculated the coseismic Coulomb failure stress change (ΔCFS) induced by these two earthquakes. Integrating the analysis of ΔCFS and the spatial distribution of aftershocks, we argue that the sequence earthquakes were triggered by the proceeding earthquakes.

Accurate high-resolution information on seabed sediment distribution is important for marine engineering investigations, submarine pipeline planning, and cross-sea passage assessment. However, multibeam seabed sediment classification is often constrained by the limited number and spatial representativeness of field samples, as well as ambiguous acoustic responses in heterogeneous seabed environments. This study presents a classification framework integrating multibeam backscatter features, topographic features, Linear Spectral Clustering (LSC) superpixel segmentation sample augmentation, and grey wolf optimizer-enhanced XGBoost (GWO-XGBoost), and applies it to the proposed Bohai Strait cross-sea passage area. Nine backscatter texture features and seven topographic features were extracted from 5 m resolution bathymetric and backscatter data acquired using a SeaBat T50-R multibeam system. LSC-based augmentation generated 12,061 pixel-level samples from 52 original grab locations. To prevent information leakage between training and validation data, 51 of the 52 original grab locations were assigned to five folds before augmentation, with all pixels derived from the same grab kept within the corresponding fold; the single sM grab was retained as training support. Under this grab-grouped validation framework, LSC combined with GWO-XGBoost achieved a mean overall accuracy of 72.38 ± 6.33% and a mean Kappa coefficient of 0.6353 ± 0.0877 at the augmented-pixel level. A complementary grab-level evaluation, in which each validation-eligible grab contributed one prediction at its original coordinate, yielded a mean overall accuracy of 58.91 ± 10.29% and a mean Kappa coefficient of 0.4850 ± 0.1341. The classification map indicates that silty sand and slightly gravelly muddy sand are the most widely mapped sediment types in the study area. Gravel-bearing sediments were more frequently mapped in areas associated with relatively steep slopes and channel- or trough-like topography, whereas sandy silt and silty sand were more widely distributed in relatively gentle areas. The results indicate the potential of the integrated LSC–GWO-XGBoost workflow for regional seabed sediment characterization under limited field sampling. Nevertheless, the augmented pixels were derived from only 52 original grab locations, of which 51 were independently evaluated, and therefore do not constitute an equivalent number of independent sediment observations. The classification map should consequently be interpreted as preliminary regional-scale sediment information rather than design-stage engineering ground truth.