Landslide mechanisms unraveled by RFID monitoring with a Machine Learning approach

 Radio-Frequency Identification (RFID) shows great potential for earth-sciences applications [1], notably in landslide surface monitoring at high spatio-temporal resolution [2] with meteorological robustness [3]. Ten 865MHz RFID tags were deployed on part of a landslide (Harmalière) and continuously monitored for 12 months by a station composed of 4 reader antennas. 2D relative localization was performed using a Phase-of-Arrival approach [4,5], and compared with optical reference measurements.

    The spatio-temporal accuracy of the method allowed for a thorough exploration of the landslides mechanisms during a 6-months period of activity. Laplacian clustering was applied to the RFID data and groups of tags with coherent behavior were identified, allowing a fine description of the kinematic motion of the landslide blocks and various mass transfer mechanisms. Each identified block can be monitored individually. 
    
    Different deformation zones were highlighted on the monitored zone. The surface movement was initiated by the topmost blocks, transferring after several weeks to the bottom of the monitored zone. This opens the way to building a landslide mechanical model in order to interpret the acquired data.

    RFID landslide monitoring allows dense observation of ground surface movements at a centimeter scale and with sub-hourly time precision, and new results bring a finer understanding the the landslides inner mechanisms.

 

References :

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