IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i7p5851-d1109377.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/7/5851/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/7/5851/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jun Jia & Xiangjun Pei & Gang Liu & Guojun Cai & Xiaopeng Guo & Bo Hong, 2023. "Failure Mechanism of Anti-Dip Layered Soft Rock Slope under Rainfall and Excavation Conditions," Sustainability, MDPI, vol. 15(12), pages 1-21, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Salman Sharifazari & Shahab Araghinejad, 2015. "Development of a Nonparametric Model for Multivariate Hydrological Monthly Series Simulation Considering Climate Change Impacts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5309-5322, November.
    2. Mohammad Zounemat-Kermani, 2016. "Investigating Chaos and Nonlinear Forecasting in Short Term and Mid-term River Discharge," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1851-1865, March.
    3. Quande Qin & Huangda He & Li Li & Ling-Yun He, 2020. "A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1249-1273, April.
    4. Vidhi Vig & Anmol Kaur, 2022. "Time series forecasting and mathematical modeling of COVID-19 pandemic in India: a developing country struggling to cope up," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 2920-2933, December.
    5. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Lin Qiu & Can-can Liu, 2017. "The Annual Maximum Flood Peak Discharge Forecasting Using Hermite Projection Pursuit Regression with SSO and LS Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 461-477, January.
    6. Baoying Shan & Ping Guo & Shanshan Guo & Zhong Li, 2019. "A Price-Forecast-Based Irrigation Scheduling Optimization Model under the Response of Fruit Quality and Price to Water," Sustainability, MDPI, vol. 11(7), pages 1-21, April.
    7. Liangxu Liu & Xueyong Zhao & Qinglan Meng & He Zhao & Xiaoqian Lu & Junkai Gao & Xueli Chang, 2017. "Annual Precipitation Fluctuation and Spatial Differentiation Characteristics of the Horqin Region," Sustainability, MDPI, vol. 9(1), pages 1-16, January.
    8. Ali Danandeh Mehr & Vahid Nourani, 2018. "Season Algorithm-Multigene Genetic Programming: A New Approach for Rainfall-Runoff Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(8), pages 2665-2679, June.
    9. Fu Qiao, 2020. "Study on Price Fluctuation of Industry Index in Chinas Stock Market Based on Empirical Mode Decomposition," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 10(5), pages 559-573, May.
    10. Adolfo Quesada-Román & Lidia Torres-Bernhard & Karla Hernández & Natalia Martínez-Rojas, 2024. "Historical trends and future implications of disasters in Honduras," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(13), pages 12313-12339, October.
    11. Parisa-Sadat Ashofteh & Taher Rajaee & Parvin Golfam, 2017. "Assessment of Water Resources Development Projects under Conditions of Climate Change Using Efficiency Indexes (EIs)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3723-3744, September.
    12. Ervin Shan Khai Tiu & Yuk Feng Huang & Jing Lin Ng & Nouar AlDahoul & Ali Najah Ahmed & Ahmed Elshafie, 2022. "An evaluation of various data pre-processing techniques with machine learning models for water level prediction," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(1), pages 121-153, January.
    13. Zhong-kai Feng & Wen-jing Niu & Zhi-qiang Jiang & Hui Qin & Zhen-guo Song, 2020. "Monthly Operation Optimization of Cascade Hydropower Reservoirs with Dynamic Programming and Latin Hypercube Sampling for Dimensionality Reduction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(6), pages 2029-2041, April.
    14. Fan, Xinying, 2022. "A method for the generation of typical meteorological year data using ensemble empirical mode decomposition for different climates of China and performance comparison analysis," Energy, Elsevier, vol. 240(C).
    15. Zong-chang Yang, 2018. "Predictive Modeling of Hourly Water-Level Fluctuations Based on the DCT Least-Squares Extended Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 1117-1131, February.
    16. Peng Chen & Andrew Vivian & Cheng Ye, 2022. "Forecasting carbon futures price: a hybrid method incorporating fuzzy entropy and extreme learning machine," Annals of Operations Research, Springer, vol. 313(1), pages 559-601, June.
    17. Emanuele Ogliari & Alfredo Nespoli & Marco Mussetta & Silvia Pretto & Andrea Zimbardo & Nicholas Bonfanti & Manuele Aufiero, 2020. "A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network," Forecasting, MDPI, vol. 2(4), pages 1-19, October.
    18. Yun Bai & Nejc Bezak & Klaudija Sapač & Mateja Klun & Jin Zhang, 2019. "Short-Term Streamflow Forecasting Using the Feature-Enhanced Regression Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4783-4797, November.
    19. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing, 2017. "Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction," Renewable Energy, Elsevier, vol. 113(C), pages 1345-1358.
    20. Jihong Xiao & Xuehong Zhu & Chuangxia Huang & Xiaoguang Yang & Fenghua Wen & Meirui Zhong, 2019. "A New Approach for Stock Price Analysis and Prediction Based on SSA and SVM," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 287-310, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:5851-:d:1109377. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.