Geological hazard susceptibility assessment and forecasting analysis based on InSAR and C-L-A model

Subsidence along expressways and railways poses significant risks to transportation infrastructure safety and environmental stability. Predicting ground settlement enables enhanced understanding of deformation characteristics along transportation corridors and facilitates timely warnings for high risk areas. This study focuses on the area within 50 km of the Jishishan earthquake epicenter, employing InSAR technology to obtain preseismic and postseismic surface deformation maps. Utilizing 35 preseismic and 21 postseismic Sentinel-1A descending orbit images from 2021 to 2024, we derived surface deformation rates through PS-InSAR and SBAS-InSAR time series analysis. The preseismic annual deformation rate was incorporated with 12 influencing factors including PGA for coseismic geological hazard susceptibility evaluation. A novel Convolutional Neural Network and Long Short-Term Memory model with attention mechanism (C-L-A) was developed for settlement forecasting by integrating deformation rate data. The results show, integrated analysis incorporating InSAR derived deformation velocities and seismic dynamic factors such as PGA significantly enhances geological hazard susceptibility assessment precision. Compared to conventional static evaluation models, the novel methodology achieves a 21 % reduction in the spatial extent of very high susceptibility zones while elevating the hazard occurrence frequency ratio by 33 %, effectively mitigating false alarm risks. This approach particularly highlight extreme hazard vulnerability in areas exhibiting annual deformation rates exceeding 30 mm. Time series InSAR monitoring unequivocally delineates regional deformation patterns: significant preseismic subsidence (reaching 118 mm/year) prevailed across the study area, while the coseismic deformation field (maximum uplift: 7.85 cm) confirms a thrust type earthquake with strike slip components, tectonically linked to the South Margin Fault of the Lajishan Mountains. Persistent postseismic uplift within a 25-km radius of the epicenter reflects ongoing stress adjustment processes. The proposed C-L-A land subsidence forecasting model demonstrates superior performance across critical metrics, including Δx(MAX) = 2.94 mm, MAE = 1.74 mm, MSE = 3.39 mm², and RMSE = 1.84 mm, outperforming benchmark models (RF, CNN, LSTM, CNN-LSTM). This architecture effectively captures spatiotemporal deformation characteristics along transportation corridors, with its high accuracy short term forecasts (about 5 months) providing reliable foundations for infrastructure risk early warning systems and disaster mitigation decision support.