MUSEnet: High Temporal-Frequency Estimation of Landslide Deformation Field Through Joint InSAR and Hydrological Observations Using Deep Learning

Abstract

The Three Gorges hydropower station in China creates a large reservoir by diverting water from the Yangtze River, increasing the risk of geological disasters, especially massive landslides along the reservoir shoreline. To mitigate these risks, improving geological monitoring and early warning systems is crucial. Interferometric Synthetic Aperture Radar (InSAR) is widely used to monitor reservoir bank landslides. However, its potential in early warning systems is limited due to temporal resolution constraints, preventing timely warnings. To address this, we propose integrating daily hydrological data (precipitation and water level observations) with historical InSAR deformation sequences using our deep learning-based multivariate united state estimation network, “MUSEnet.” This approach generates customized daily landslide deformation products for high-risk areas, greatly enhancing early warning capabilities by providing timely and accurate information on landslide occurrence and magnitude. We validated our method using 161 Sentinel-1 A images of the Xinpu landslide in the Three Gorges Reservoir area. Through statistical analysis, we identified different degrees of influence from rainfall and reservoir water level on the deformation of the Xinpu landslide at various locations. Additionally, we observed distinct lag times between deformation and corresponding rainfall and reservoir water level events. By utilizing deep learning, our method estimates nonlinear states by considering hysteresis and intelligently accounts for the impact of rainfall and reservoir water level, resulting in more accurate estimations compared to traditional models.

Keywords

• Deep learning
• InSAR
• landslide
• prediction