Multi-sensor observation fusion scheme based on 3D variational assimilation for landslide monitoring

Abstract

Multi-sensor observation is very important for monitoring landslide disasters. Since various surveying techniques are currently available for detecting variational slope activities from different perspectives, studies have focused on integration of multi-source information for the analysing landslide displacements. In this study, a general multi-source data fusion scheme for landslide monitoring based on three-dimensional variation (3DVar) data assimilation was developed. The scheme was used to fuse different observations of Xishancun Landslide in Li County, Sichuan Province, China. First, the displacement observations obtained by a Global Positioning System (GPS) and Borehole Inclinometers (BIs) were assimilated for accurate evaluation of slope activities. Then, slope Stability Index (SI) was introduced to validate the assimilation results within a time interval. SI_Assi values calculated using the integration model developed in the present study were compared with SI_FS simulated by a physically based landslide model. The correlation coefficient between them ss 0.75, which is larger than those with SI_GPS (0.45) or SI_BIs (0.41) values determined by the GPS and BIs respectively. The assimilation results are thus confirmed to be more credible for slope stability simulation.

Keywords:

• Landslide monitoring
• 3DVar assimilation
• multi-sensor
• observation
• slope stability

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Basic Research Program of China (Grant No. 2013CB733204), National Natural Science Foundation of China (Grant No. 41501458), and China Postdoctoral Science Foundation Funded Project (Grant No. 2016M592860).