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Seismic signal recognition and interpretation of the 2019 “7.23” Shuicheng landslide by seismogram stations


A systematic study of the physical and mechanical processes of landslide development and evolution is important for forecasting, early warning, and prevention of landslide hazards. In the absence of on-site monitoring data, seismic networks can be employed to continuously record ground seismicity generated during landslides. However, landslide seismic signals are relatively weak and inevitably affected by noise interference. Furthermore, systematic characterization and reconstruction of the landslide evolution process remain poorly reported. An evaluation method to recognize landslide events based on seismic signal characteristics is therefore important. This study analyzes the 2019 “7.23” Shuicheng landslide based on data from nearby seismic stations. A landslide seismic signal recognition method is developed based on short-time Fourier transform (STFT) and band-pass filter (BP-filter) analysis. Data from 14 stations near the landslide were reviewed and the landslide data from one station was selected for analysis. The landslide seismic signal was noise-attenuated by using the empirical mode decomposition (EMD) and BP-filter methods. Fast Fourier transform (FFT), STFT, and power spectral density analyses were applied to the landslide seismic signal with higher signal-to-noise ratio (SNR) to obtain the time–frequency signal characteristics of the landslide process. Finally, combined with landslide field survey data, the dynamic process of the landslide was reconstructed based on the seismic signal, and the landslide was divided into four stages: the fracture-transition stage, the accelerated initiation stage, the bifurcation-scraping stage, and the deposition stage. The dynamic characteristics of each stage of the landslide are presented. The results indicate that the initial fracture point of the landslide is located between the bottom of the sliding source area and the top of the acceleration zone, not as traditionally thought, at the top of the sliding source area; this would be difficult to determine through field survey and analysis only. These results provide theoretical guidance for the study of seismic signal extraction, identification of landslide dynamic parameters, and characterization and reconstruction of landslide processes.

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We acknowledge the Institute of Geophysics, China Earthquake Administration, for providing the seismic signal data recorded near the Shuicheng landslide. We thank Esther Posner, PhD, from Liwen Bianji, Edanz Editing China, for editing the English text of a draft of this manuscript.


This study was financially supported by the International Science & Technology Cooperation Program of China (grant no. 2018YFE0100100), National Natural Science Foundation of China (grant no. 41901008), National Key R&D Program of China (grant no. 2018YFC1505201), Open Fund Project of the Key Laboratory of Mountain Hazards and Surface Processes of the Chinese Academy of Sciences, and the Fundamental Research Funds for the Central Universities (grant no. 2682018CX05). This work was also financially supported by the China Scholarship Council.

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Correspondence to Yifei Cui.

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Yan, Y., Cui, Y., Tian, X. et al. Seismic signal recognition and interpretation of the 2019 “7.23” Shuicheng landslide by seismogram stations. Landslides 17, 1191–1206 (2020).

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  • Seismic network
  • Shuicheng landslide
  • Signal extraction and recognition
  • Signal interpretation
  • Landslide process reconstruction