Analyzing high-frequency seismic signals generated during a landslide using source discrepancies between two landslides

High-frequency seismic signals are generated during landslides and can be analyzed to obtain detailed information regarding landslide dynamics. However, a better understanding of the physical process that produces such high-frequency signals is required. The two massive landslides that occurred on A...

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Bibliographic Details
Published inEngineering geology Vol. 272; p. 105640
Main Authors Zhang, Zhen, He, Siming, Li, Qianfeng
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.07.2020
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Summary:High-frequency seismic signals are generated during landslides and can be analyzed to obtain detailed information regarding landslide dynamics. However, a better understanding of the physical process that produces such high-frequency signals is required. The two massive landslides that occurred on April 11, 2013 in the Bingham Canyon Mine pit (Utah, USA) generated low- and high-frequency seismic signals. Using the low-frequency signals, we inverted the force-time functions of the two events and estimated their dynamics. We used the signals generated by the two events and filtered in three frequency bands to obtain the source discrepancies between the two events. By analyzing these source discrepancies, we found that the low- and medium-frequency signals were generated due to the bulk movement of sliding material. Furthermore, the medium-frequency signals contained information regarding small-particle impacts along the sliding path. The high-frequency (1–3 Hz) signals are controlled by small-scale process of sliding materials, and are sum of stochastic phenomena. This clarification of the source of the high-frequency signals is expected to improve the analyses of seismic signals generated during landslides. •Landslide dynamics were estimated by inverting seismic signals.•Low- and medium-frequency signals reflect the bulk movement of sliding material.•High-frequency signals of landslides are the sum of stochastic phenomena.
ISSN:0013-7952
1872-6917
DOI:10.1016/j.enggeo.2020.105640