EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Detecting Deep-seated Landslide Movement Using Seismic Signal Analysis of MEMS

Meei-Ling Lin1, Sheng-Yu Chiu1, Kuo-Lung Wang2, and Yo-Ming Hsieh3
Meei-Ling Lin et al.
  • 1National Taiwan University, Department of Civil Engineering, Taipei, Taiwan (
  • 2Department of Civil Engineering, National Chinan University, Puli, Taiwan
  • 3Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

The deep-seated landslides often caused severe hazard due to the large area and landslide mass associated with the landslide movement. Thus, monitoring the landslide movement is an important task for landslide hazard management. The Microelectromechanical Systems (MEMS) technique developed rapidly in recent years provides the ability of low-cost sensors and easy installation for monitoring of the landslide movement in field. Typically, the landslide movement monitoring using MEMS is based on the tilt angle determined from the measured ground acceleration variations in three directions, and being subjected to the signal noise. We adopt Moving Window Fast Fourier Transform and other seismic wave analysis in this study to improve resolution of the seismic signals and achieve a sound detection of deep-seated landslide movement. The MEMS was installed at the Lantai deep-seated landslide study area, which measured the ground accelerations mid-slope of the landslide. The seismic signals recorded for eleven earthquake events and three heavy rainfall events are selected for analysis. It was found that the signal frequency can be separated from the system responses and related to the landslide movement. Validations were conducted by comparing the analysis results to the field monitoring data of in-place inclinometer and borehole extensometer while available. It is suggested that the landslide movement can be identified with seismic signal at approximately 17 Hz, and the results are consistent for both earthquake-induced and rainfall-induced events. 

How to cite: Lin, M.-L., Chiu, S.-Y., Wang, K.-L., and Hsieh, Y.-M.: Detecting Deep-seated Landslide Movement Using Seismic Signal Analysis of MEMS, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-11251,, 2023.