Interferometric Synthetic Aperture Radar (InSAR) is an important method for acquiring surface deformation. Considering the difficulty of the identification work, the identification of landslides needs to be combined with the context of the pregnant disaster and the precipitating conditions. To identify potential landslides, we applied spatial and temporal filtering to the InSAR results, which consists of rainfall and landslide susceptibility mapping. In this paper, taking the Badong Ecological Barrier Zone of the Three Gorges reservoir area as the study area, the deformation aggregation areas in the study area were obtained by applying Small Baseline Subset InSAR (SBAS-InSAR) technology and spatial statistical analysis. We screened deformation aggregation areas by combining the susceptibility map and the correlation analysis of rainfall and deformation. Field verification and investigation were conducted on the suspected deformation areas, and 11 landslides were found to have signs of deformation, two of them are newly discovered landslides. In addition, we selected one of the landslides, the Songjiawuchang landslide, and compared the InSAR results with the GPS accumulated displacement to verify the reliability of the results. This research demonstrated the feasibility of combining InSAR results with spatial susceptibility maps and monthly rainfall factors for landslides identification methods.

A Mw 6.6 earthquake struck Menyuan, Qinghai, China, on 7 January 2022. To determine the rupture parameters of this event, the coseismic InSAR deformation fields were mapped and further employed to estimate the focal mechanism. The best-fitting solution emphasized that the 2022 Menyuan earthquake ruptured at the junction of the Tuolaishan fault and the Lenglongling fault. Both rupturing faults were dominated by sinistral strike-slip, and the main slip was concentrated on the shallow part of the rupture plane. The latter was the main rupture segment with a strike of 106° and a dip of 86°. The slip mainly occurred at depths of 0–8 km, and the rupture was exposed at the surface. The maximum slip reached ~3.5 m, which occurred mainly at a depth of 4 km. Joint analysis of the optimal slip model, relocated aftershocks, Coulomb stress change, and field observation suggested that the strain energy in the Tuolaishan fault may not have been fully released and needs further attention. Moreover, the 2022 Mw6.6 Menyuan earthquake caused a significant stress loading effect on the western Tuolaishan fault and eastern Lenglongling fault, which implies that the 2022 event increased the seismic hazard in these regions.

On 4 October 2023, a glacier lake outburst flood (GLOF) occurred at South Lhonak Lake in the northwest of Sikkim, India, posing a severe threat to downstream lives and property. Given the serious consequences of GLOFs, understanding their triggering factors is urgent. This paper conducts a comprehensive analysis of optical imagery and InSAR deformation results to study changes in the surrounding surface of the glacial lake before and after the GLOF event. To expedite the processing of massive InSAR data, an InSAR processing system based on the SBAS-InSAR data processing flow and the AI Earth cloud platform was developed. Sentinel-1 SAR images spanning from January 2021 to March 2024 were used to calculate surface deformation velocity. The evolution of the lake area and surface variations in the landslide area were observed using optical images. The results reveal a significant deformation area within the moraine encircling the lake before the GLOF, aligning with the area where the landslide ultimately occurred. Further research suggests a certain correlation between InSAR deformation results and multiple factors, such as rainfall, lake area, and slope. We speculate that heavy rainfall triggering landslides in the moraine may have contributed to breaching the moraine dam and causing the GLOF. Although the landslide region is relatively stable overall, the presence of a crack in the toparea of landslide raises concerns about potential secondary landslides. Our study may improve GLOF risk assessment and management, thereby mitigating or preventing their hazards.

Landslides, as a major geological hazard, caused the significant threats to human life and property. Therefore, the identification of potential landslides is a crucial concern. This study combines synthetic aperture radar interferometry phase-gradient stacking with deep learning to achieve efficient and accurate identification of large-scale potential landslides. By stacking phase gradients to highlight local surface deformation information and refining the YOLOv8 model based on small target features of local deformations, this study introduces improvements. This involves adding a convolutional block attention module layer to the backbone, replacing the C2f modules with GhostNetV2 to suppress information loss during long-distance feature transmission, and enhancing the network's perception and detection capabilities for small targets. In addition, a 160 × 160 small target detection head is added to the detection module to specifically handle small target detection tasks, improving accuracy and performance. A new loss function, FocalSIoU loss, is introduced based on the characteristics of the dataset, combining SIoU with the gamma bias option to make the model more targeted during training. The improved model achieved a maximum mAP50 value of 93.4% on the validation set. Finally, using mask factors to identify region deformation points, this study reduces misjudgments of non-landslides, identifying 378 potential landslides in the study area with a false positive rate of only 10.2%.

Total volatile basic nitrogen (TVB-N) content is an important indicator for evaluation of duck-meat freshness. The accuracy of the existing near-infrared spectrometric model is affected by sample characteristics of different varieties. Hence, improvement in the model is needed. In order to improve the applicability of the model for TVB-N content in duck meat with different varieties, calibration maintenance methods were done, based on different principles including model updating (MU), direct standardization (DS), and slope/bias correction (SBC). The numbers of samples in the standardization sets were investigated for transfer effects. The results showed that the univariate SBC method exhibited a more significant transfer effect than multivariate DS transfer or complete recalibration of MU. The best results were obtained by the SBC method with 20 standard samples, for which the root mean square error of prediction and mean bias were 0.930 and –0.130. The calibration maintenance and transfer study done will likely provide reference for model maintenance between samples with different varieties and lay a foundation for industrial application.