NH54A-02
Establishment of Stereo Multi-sensor Network for Giant Landslide Monitoring and its Deploy in Xishan landslide, Sichuan, China.

Friday, 18 December 2015: 16:15
309 (Moscone South)
Chun Liu, Tongji University, Shanghai, China
Abstract:
Landslide is one of the most destructive natural disasters, which severely affects human lives as well as the safety of personal properties and public infrastructures. Monitoring and predicting landslide movements can keep an adequate safety level for human beings in those situations. This paper indicated a newly developed Stereo Multi-sensor Landslide Monitoring Network (SMSLMN) based on a uniform temporal geo-reference. Actually, early in 2003, SAMOA (Surveillance et Auscultation des Mouvements de Terrain Alpins, French) project was put forwarded as a plan for landslide movements monitoring. However, SAMOA project did not establish a stereo observation network to fully cover the surface and internal part of landslide. SMSLMN integrated various sensors, including space-borne, airborne, in-situ and underground sensors, which can quantitatively monitor the slide-body and obtain portent information of movement in high frequency with high resolution. The whole network has been deployed at the Xishan landslide, Sichuan, P.R.China.

According to various characteristic of stereo monitoring sensors, observation capabilities indicators for different sensors were proposed in order to obtain the optimal sensors combination groups and observation strategy. Meanwhile, adaptive networking and reliable data communication methods were developed to apply intelligent observation and sensor data transmission. Some key technologies, such as signal amplification and intelligence extraction technology, data access frequency adaptive adjustment technology, different sensor synchronization control technology were developed to overcome the problems in complex observation environment. The collaboratively observation data have been transferred to the remote data center where is thousands miles away from the giant landslide spot. These data were introduced into the landslide stability analysis model, and some primary conclusion will be achieved at the end of paper.