Monitoring and Predictive Analysis of Surface Deformation Using Combined SBAS-InSAR Technology and CS-Elman Neural Network: A Case Study of Wenchuan County, Sichuan Province, China

Earthquakes deposit loose materials in gullies, making seismic mountain gorges prone to landslides and debris flows. Monitoring and predicting ground deformation in these areas is essential. This study introduces a CS-Elman prediction model based on SBAS-InSAR monitoring. SBAS-InSAR technology analyzes 36 Sentinel-1A im- ages from Wenchuan County, Sichuan Province, China (June 2021 to October 2023), focusing on deformation areas and precipitation data. The CS algorithm optimizes the Elman network's parameters, using SBAS-InSAR data as training samples. Validation shows that: (1) Wenchuan County experiences varied deformation, with Banzi Gully in Miansi Town showing the highest uplift at 183.74 mm/a due to heavy rain. (2) As sample size increases, prediction error decreases and accuracy improves. Predictions suggest ongo- ing uplift at about 4.50 mm per month above Cutou Gully over the next four months, highlighting the need for continued monitoring.