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. [ABSTRACT FROM AUTHOR]