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Seismic Signal Characteristics and Numerical Modeling Analysis of the Xinmo Landslide

Author

Listed:
  • Longwei Yang

    (Wuhan Design & Research Institute Co., Ltd. of China Coal Technology & Engineering Group, Wuhan 430064, China
    College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China)

  • Yangqing Xu

    (Wuhan Design & Research Institute Co., Ltd. of China Coal Technology & Engineering Group, Wuhan 430064, China)

  • Luqi Wang

    (School of Civil Engineering, Chongqing University, Chongqing 400030, China)

  • Qiangqiang Jiang

    (Wuhan Design & Research Institute Co., Ltd. of China Coal Technology & Engineering Group, Wuhan 430064, China)

Abstract

Due to the high elevation and huge potential energy of high-level landslides, they are extremely destructive and have prominent kinetic-hazard effects. Studying the kinetic-hazard effects of high-level landslides is very important for landslide risk prevention and control. In this paper, we focus on the high-level landslide that occurred in Xinmo on 24 June 2017. The research is carried out based on a field geological survey, seismic signal analysis, and the discrete element method. Through ensemble empirical mode decomposition (EEMD) and Fourier transformation, it is found that the seismic signals of the Xinmo landslide are mainly located at low frequencies of 0–10 Hz, and the dominant frequency range is 2–8 Hz. In addition, the signal time-frequency analysis and numerical simulation calculation results reveal that the average movement distance of the sliding body was about 2750 m, and the average movement speed was about 22.9 m/s. The movement process can be divided into four main stages: rapid start, impact loading, fragmentation and migration, and scattered accumulation stages. We also provide corresponding suggestions for the zoning of high-level landslide geological hazards.

Suggested Citation

  • Longwei Yang & Yangqing Xu & Luqi Wang & Qiangqiang Jiang, 2023. "Seismic Signal Characteristics and Numerical Modeling Analysis of the Xinmo Landslide," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:5851-:d:1109377
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    References listed on IDEAS

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    1. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
    2. Paúl Carrión-Mero & Néstor Montalván-Burbano & Fernando Morante-Carballo & Adolfo Quesada-Román & Boris Apolo-Masache, 2021. "Worldwide Research Trends in Landslide Science," IJERPH, MDPI, vol. 18(18), pages 1-24, September.
    3. Wang, Shouxiang & Zhang, Na & Wu, Lei & Wang, Yamin, 2016. "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method," Renewable Energy, Elsevier, vol. 94(C), pages 629-636.
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