Ground-penetrating radar (GPR) is often used to detect targets in a construction environment. Due to the different construction environments, the noise exhibits different characteristics on the GPR signal. When the noise is widely distributed on the GPR signal, and its spectrum and the spectrum of the active signal are aliased, it is difficult to separate and suppress the noise by traditional filtering methods. In this paper, we propose a deep learning GPR image noise suppression method based on a recursive guided and dual multi-scale self-attention mechanism neural network (RG-DMSA-NN), which uses a recursive guidance module and a dual multi-scale self-attention mechanism module to improve the feature extraction ability of the image and enhance the robustness and generalization ability in image noise suppression. Through the application of noise suppression on the synthesized test data and the GPR data actually collected by the Macao Science and Technology Museum, the advantages of this method over the traditional filtering, DnCNN and UNet noise suppression methods are demonstrated.

The evaluation of landslide susceptibility plays a crucial role in preventing the risks associated with landslides and debris flows, providing valuable insights for the effective prevention and mitigation of geological hazards. However, there is limited research on high-altitude areas. Therefore, this study chose the western Tibetan Plateau as the study area, a representative area known for its susceptibility to landslides and high attitudes. In this study, seven factors were identified based on research objectives. Information value (IVM), weight of evidence (WOE), information value logistic regression (IVM-LR), weight of evidence logistic regression (WOE-LR), information value multi-layer perceptron (IVM-MLP) and weight of evidence multi-layer perceptron (WOE-MLP) were selected and compared for landslide susceptibility. The percentage of disaster area included in each risk level, the AUC value and the ROC curve were used to evaluate the accuracy of the results. The ROC curves of the results were close to the upper–left corner and the AUC values exceeded 0.85, an indication that all results were highly accurate. Moreover, the percentage of disaster area included for each risk showed an upward trend regarding susceptibility. The results indicated that the hybrid model exhibited superior performance in assessing landslide susceptibility at high altitudes. Overall, the results showed great significance regarding disaster prevention and mitigation measures of local governments.

Due to the impact of climate warming and engineering construction, thaw-slumping has developed extensively along the Qinghai–Tibet Project Corridor. These landslide disasters not only destroy the fragile ecology of the Qinghai–Tibet Plateau but also threaten the security of the Qinghai–Tibet Project Corridor. Because remote-sensing images lack imaging data inside the landslide body, and the excavation of boreholes has blindness and inefficiency, the ground-penetrating radar method with high efficiency and deep imaging has been developed and applied in the detection and treatment of thaw-slumping. To more accurately divide the soil-layered structure of the thaw-slumping body and obtain the key elements of the thaw-slumping such as temperature change trend and relative water content, we propose the use of amplitude event axis tracking and amplitude energy attenuation calculation to divide the fine layering of the thaw-slumping body. In addition, based on layer division, we introduce two attribute parameters to participate in the calculation of relative water content. These two attribute parameters are the weighted average frequency attribute, which reflects the temperature change trend, and the sweetness attribute, which reflects the change in the physical properties of the underground medium. The calculated 3D profile and time slice of the relative water content comprehensively show the change characteristics and enrichment area of the internal relative water content of the thaw-slumping. These methods and results are valuable for the characterization, evaluation, and treatment of thaw-slumping.