Ground-based synthetic aperture radar interferometry (GB-InSAR) has the characteristics of high precision, high temporal resolution, and high spatial resolution, and is widely used in highwall ...deformation monitoring. The traditional GB-InSAR real-time processing method is to process the whole data set or group in time sequence. This type of method takes up a lot of computer memory, has low efficiency, cannot meet the timeliness of slope monitoring, and cannot perform deformation prediction and disaster warning forecasting. In response to this problem, this paper proposes a GB-InSAR time series processing method based on the LSTM (long short-term memory) model. First, according to the early monitoring data of GBSAR equipment, the time series InSAR method (PS-InSAR, SBAS, etc.) is used to obtain the initial deformation information. According to the deformation calculated in the previous stage and the atmospheric environmental parameters monitored, the LSTM model is used to predict the deformation and atmospheric delay at the next time. The phase is removed from the interference phase, and finally the residual phase is unwrapped using the spatial domain unwrapping algorithm to solve the residual deformation. The predicted deformation and the residual deformation are added to obtain the deformation amount at the current moment. This method only needs to process the difference map at the current moment, which greatly saves time series processing time and can realize the prediction of deformation variables. The reliability of the proposed method is verified by ground-based SAR monitoring data of the Guangyuan landslide in Sichuan Province.
The Xishan coal mine area in Beijing, China has a long history of mining. Many landslide hazards, in addition to collapses and ground fractures, have occurred in this area. This study used ...multi-temporal satellite images to extract this region’s deformation information, identify landslides and analyze the deformation evolution process of these landslides. Taking the Anzigou ditch as an example, we investigate the “Quarry–Landslide–Mudslide” disaster chain model. We found that the landslide evolution process is closely related to the geological conditions, and usually goes through four stages: initial deformation, slope front swelling and collapsing, rear part connecting and rupturing, and landslide creeping. The surface deformation can be identified and tracked by high-resolution optical images and InSAR monitoring. Under the combined effects of rainfall and topographic conditions, medium and large landslides may occur and trigger a “Quarry–Landslide–Mudflow” disaster chain. The identification and analysis of these landslide hazards and the disaster chain help with geological disaster prevention, and provide reference for early identification and research of similar disasters.
The complex terrain and abundant ravines in the western mountainous areas of Beijing have led to dramatic changes in the geological environment. Monitoring and assessing the stability of landslide ...surfaces is of great significance for disaster prevention and ensuring the safety of the capital city. According to the spatial similarity characteristics of landslide surfaces, we propose a new interferometric synthetic aperture radar (InSAR) deformation enhancement method by taking into account the time-series deformation information of spatially adjacent homogeneous monitoring points. Taking Dongjiang Gully, Beijing as a typical study area, using multitemporal InSAR technology, 80 scenes of RADARSAT-2 data from September 2016 to September 2022 were processed to obtain their time-series surface deformation to verify the advantages of the proposed method. The results show that the standard deviation of the deformation difference of all monitoring points is generally reduced after the deformation enhancement, and the mean value is reduced from 5.1 to 3.3, which is 35.2% lower in comparison. Then this study assesses the stability of the landslide surface based on the deformation enhancement results. First, the optical image interpretation was combined with the angular distortions derived from deformation gradients to analyze the spatial location and boundaries of the landslide, and then to identify the infrastructure that is more susceptible to landslide impact. Second, through the principal component analysis method, the correlation between each component and the distribution characteristics of surface deformation was analyzed. Finally, starting from geological factors and triggering conditions, the driving force for landslide surface deformation was discussed, and it was drawn that seasonal precipitation is a major influencing factor. The proposed method can provide a reference for landslide monitoring and assessment in similar areas.
InSAR (Interferometric Synthetic Aperture Radar) is widely recognized as a crucial remote sensing tool for monitoring various geological disasters because it provides all-day and all-weather ...monitoring. Nevertheless, the current interpretation methods for InSAR heavily depend on the interpreter’s experience, which hinders efficiency and fails to meet the requirements for the timely detection of geologic hazards. Furthermore, the results obtained through current InSAR processing carry inherent noise interference, further complicating the interpretation process. To address those issues, this paper proposes an approach that enables automatic and rapid identification of deformation zones. The proposed method leverages IPTA (Interferometric Point Target Analysis) technology for SAR data processing. It combines the power of HNSW (Hierarchical Navigable Small Word) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms to cluster deformation results. Compared with traditional methods, the computational efficiency of the proposed method is improved by 11.26 times, and spatial noise is suppressed. Additionally, the clustering results are fused with slope units determined using DEM (Digital Elevation Model), which facilitates the automatic identification of slopes experiencing deformation. The experimental verification in the western mountainous area of Beijing has identified 716 hidden danger areas, and this method is superior to the traditional technology in speed and automation.
Classifying Hyperspectral images with few training samples is a challenging problem. The generative adversarial networks (GAN) are promising techniques to address the problems. GAN constructs an ...adversarial game between a discriminator and a generator. The generator generates samples that are not distinguishable by the discriminator, and the discriminator determines whether or not a sample is composed of real data. In this paper, by introducing multilayer features fusion in GAN and a dynamic neighborhood voting mechanism, a novel algorithm for HSIs classification based on 1-D GAN was proposed. Extracting and fusing multiple layers features in discriminator, and using a little labeled samples, we fine-tuned a new sample 1-D CNN spectral classifier for HSIs. In order to improve the accuracy of the classification, we proposed a dynamic neighborhood voting mechanism to classify the HSIs with spatial features. The obtained results show that the proposed models provide competitive results compared to the state-of-the-art methods.
Hyperspectral data contains abundant information in spectral domain, which is very useful for mineral classification and geological body mapping. But, due to the lack of labeled data, it is difficult ...to get an acceptable result by just using the small number of labeled data. We adopt a semi-supervised method called CNN, which can effectively extract inner features of hyperspectral image to classify hyperspectral data. However, with constraint to the size of receptive field, it can hardly get higher level features. We propose dilated CNN for mineral classification of hyperspectral data. At the same size of kernels, dilated CNN has bigger receptive field. At the meanwhile, it can get higher accuracy of classification. We test our model on hyperspectral data at Tianshan area, where is rich in minerals. From the result, we can find that our method can get a great result on the mineral classification task, which can be used for making geological map.
Background Associated with enzyme deficiencies causing glycosaminoglycans (GAGs) accumulation, mucopolysaccharidosis type VI (MPS VI) is lysosomal storage disorder. In the treatment of MPS VI, ...galsulfase (Naglazyme) is commonly used as an enzyme replacement therapy (ERT). There remains a need for comprehensive real-world data on its safety and associated adverse events (AEs). Objective An analysis of the FDA Adverse Event Reporting System (FAERS) database will be conducted to identify potential risks and adverse reactions associated with galsulfase in real-life settings. Methods The FAERS database was used to extract data from Q2 2005 to Q4 2023. A total of 20,281,876 reports were analyzed after duplicate elimination, with 3,195 AE reports related to galsulfase identified. The association between galsulfase and AEs was investigated by utilizing four algorithms: reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS). The analysis focused on the timing of onset, signs of AEs, and clinical significance. Results Twenty seven organ systems were involved, and significant system organ classes (SOCs) included respiratory, thoracic and mediastinal disorders, and infections and infestations. At the PT level, 72 PTs corresponding to 15 SOCs were identified, with some AEs not previously mentioned in the product label. AEs associated with galsulfase had a median onset time of 1,471 days, with over half of the cases occurred within the first 5 years of treatment initiation. Conclusion This investigation delivers an exhaustive and indicative assessment of galsulfase’s safety profile, grounded in authentic, real-world evidence. The findings emphasis the importance of continuous safety surveillance and the emergence of new AEs. The identification of previously unreported urologic adverse events, such as glomerulonephritis membranous and nephritic syndrome, warrants further investigation. The study emphasizes the need for enhanced pharmacovigilance to ensure patient safety and the effectiveness of galsulfase treatment.
