编号：CDUT-2024-32

标题：Characterizing large rockfalls using their seismic signature: A case study of Hongya rockfall

入藏号：WOS:00105833660000      

中国科学院文献情报中心期刊分区：工程技术1区TOP（2023）

本校作者：李伟；王东坡；易雪斌；闫帅星

来源出版物：ENGINEERING GEOLOGY  卷: 323  文献号: 107222

出版年：2023

第一地址：成都理工大学

关键词：Hongya rockfall；Assessment scheme；Rockfall identification；Rockfall detection and location；Rockfall characterization

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摘要：A preliminary, yet timely characterization of rockfall hazards is essential for effective emergency response and mitigation measures. Such characterization is, however, particularly challenging for events occurring on highmountain slopes in remote locations, for which precursory information remains often undetected. Large rockfalls are known to produce seismic signatures that, if properly interpreted, provide key information on the magnitude and destructiveness of the event. To achieve this, we establish a seismic signal-based assessment scheme and demonstrate its capability by taking a large event - the 5 April 2021 Hongya rockfall (Sichuan, China) - as a case study. First, we show how a rockfall can be distinguished from an earthquake and a rockslide by analyzing its seismic signatures. Then, we demonstrate how the kurtosis-based method can be used to rapidly detect the initiation of a rockfall and determine the seismic wave velocity accordingly, as well as how the arrival time-based location method can be used to locate the event. The rockfall volume can be estimated from the magnitude of radiated seismic energy. Furthermore, we characterize the different phases in seismograms and link them to the typical stages of rockfall, i.e., precursory small rockfalls, impact and fragmentation, sliding and entrainment, and gravity deposition stages. Finally, a discussion and suggestions are provided for further improving the robustness of the assessment scheme. Our results show that the seismic signal-based scheme presented in this study is suitable for characterizing large rockfalls and has the potential for rapid and effective emergency management.

文章链接地址: https://www.sciencedirect.com/science/article/pii/S0013795223002405?via%3Dihub#ab0005