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<p>Extreme weather significantly challenges the effective and timely monitoring of landslide disasters. Distributed acoustic sensing (DAS) offers unique capabilities for monitoring slope failures during extreme weather events such as typhoons by transforming pre-deployed optical fiber cables into high-resolution vibration-acoustic sensor arrays. This study documents sudden shifts in landslide disturbance signals during a super typhoon’s passage using DAS with 1 Hz downsampled modulated signals. By leveraging multi-domain analysis (time-frequency-space), we identify landslide disturbance micro-deformation signatures, revealing interconnected spatial responses and dynamic patterns. We introduce a spatiotemporal indicator evaluation framework to monitor landslide occurrence and evolution under extreme weather conditions. The monitoring of landslide occurrence correlates well with post-disaster incident records and meteorological data. These results demonstrate that DAS systems can enhance early detection and high-resolution monitoring of landslide disasters under extreme weather conditions, highlighting the potential for comprehensive natural disaster management.</p>

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Monitoring landslide disturbances using distributed acoustic sensing under extreme weather conditions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月27日

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  • Chengyuan Zhu,
  • Yiyuan Yang,
  • Kaixiang Yang,
  • Weiqiang Zhu,
  • Qinmin Yang

摘要

Extreme weather significantly challenges the effective and timely monitoring of landslide disasters. Distributed acoustic sensing (DAS) offers unique capabilities for monitoring slope failures during extreme weather events such as typhoons by transforming pre-deployed optical fiber cables into high-resolution vibration-acoustic sensor arrays. This study documents sudden shifts in landslide disturbance signals during a super typhoon’s passage using DAS with 1 Hz downsampled modulated signals. By leveraging multi-domain analysis (time-frequency-space), we identify landslide disturbance micro-deformation signatures, revealing interconnected spatial responses and dynamic patterns. We introduce a spatiotemporal indicator evaluation framework to monitor landslide occurrence and evolution under extreme weather conditions. The monitoring of landslide occurrence correlates well with post-disaster incident records and meteorological data. These results demonstrate that DAS systems can enhance early detection and high-resolution monitoring of landslide disasters under extreme weather conditions, highlighting the potential for comprehensive natural disaster management.