Advanced remote monitoring and prediction of deep-seated landslide risk using SAR and physical modeling
Unmet Need
Deep-seated landslides are some of the most catastrophic natural hazards, gradually moving almost imperceptibly over many years before suddenly collapsing in a dramatic fashion. Conventional prediction methods depend on physical access through boreholes, which are costly to install and maintain and require significant infrastructure, making remote communities particularly vulnerable. To address the challenges of on-site data collection, remote sensing has emerged as a valuable tool for monitoring landslides in recent decades. Synthetic Aperture Radar (SAR) allows for remote observation of surface deformations without the need for on-site equipment. However, SAR's effectiveness can be limited in steep or densely vegetated areas, which are common in remote regions, and it often fails to provide detailed information about the internal structure of deep-seated landslides. Therefore, there is a pressing need for a remote early warning technology for deep-seated landslides that overcomes these challenges.
Technology
Duke inventors have developed a method to predict the internal movement and stability of deep-seated landslides using SAR data. This approach, which utilizes a temperature-driven physics model, allows for sub-surface monitoring without the need for expensive on-site equipment, making it ideal for remote locations. The model has been validated with SAR and borehole data from the El Forn landslide in Andorra, showing accurate predictions of both seasonal and off-seasonal sub-surface ground motion.
Other Applications
This technology can be applied to predict locations on a scarp that need further instrumentation, maximizing monitoring capabilities given finite equipment resources
Advantages
- Integrates remote sensing with physical modeling
- Forecasts internal movement and stability changes of deep-seated landslides over time, including time to failure
- Operates without on-site equipment, making it suitable for monitoring in remote communities