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<p>Deep-seated gravitational-deformation slopes (DGDSs) in the Jinsha River Fault Zone pose significant hazards due to their complex geological structures and dynamic deformation patterns. By integrating PS-InSAR and SBAS-InSAR with UAV LiDAR data, borehole investigation, and rainfall analysis, this study aims to validate the efficacy, characterize the deformation patterns, and quantify the rainfall-deformation response. Seventeen DGDSs were identified within the Diwu Township section of the Jinsha River Fault Zone. Ten landslides, including the Diwu and Gonghuo, are experiencing significant deformation. The Diwu landslide, the primary case study, spans approximately 3800&#xa0;m in length and 1330&#xa0;m in width. UAV LiDAR uncovered 152 collateral landslides, and three distinct sliding zones were identified at depths of 32.8–34.0&#xa0;m, 53.2–58.0&#xa0;m, and 63.5–66.1&#xa0;m. The total volume is 1.28 × 10<sup>8</sup>–2.58 × 10<sup>8</sup> m<sup>3</sup>. Our findings indicate that PS-InSAR is more effective than SBAS-InSAR in analyzing DGDSs in this study. Analysis reveals a time lag of 6–12&#xa0;days between rainfall and temporary deformation acceleration, and the landslides usually accelerate after daily rainfall surpasses 20&#xa0;mm. The study highlights the interaction between active fault zones, rainfall, and resultant deformation patterns, emphasizing the importance of monitoring for landslide instability. This study substantially advances our understanding of large DGDS mechanisms in active fault zones and informs risk prevention strategies for geological disasters.</p>

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Types and mechanism of deep-seated gravitational-deformation slopes in tectonics active zone: a high-resolution study of the Diwu landslide along the Jinsha River Fault Zone, Tibetan Plateau

  • 期刊论文

  • 发表日期：

    2025年9月26日

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  • Yiqiu Yan,
  • Changbao Guo,
  • Zhendong Qiu,
  • Caihong Li,
  • Gui Liu,
  • Hao Yuan,
  • Shiva P. Pudasaini

摘要

Deep-seated gravitational-deformation slopes (DGDSs) in the Jinsha River Fault Zone pose significant hazards due to their complex geological structures and dynamic deformation patterns. By integrating PS-InSAR and SBAS-InSAR with UAV LiDAR data, borehole investigation, and rainfall analysis, this study aims to validate the efficacy, characterize the deformation patterns, and quantify the rainfall-deformation response. Seventeen DGDSs were identified within the Diwu Township section of the Jinsha River Fault Zone. Ten landslides, including the Diwu and Gonghuo, are experiencing significant deformation. The Diwu landslide, the primary case study, spans approximately 3800 m in length and 1330 m in width. UAV LiDAR uncovered 152 collateral landslides, and three distinct sliding zones were identified at depths of 32.8–34.0 m, 53.2–58.0 m, and 63.5–66.1 m. The total volume is 1.28 × 108–2.58 × 108 m3. Our findings indicate that PS-InSAR is more effective than SBAS-InSAR in analyzing DGDSs in this study. Analysis reveals a time lag of 6–12 days between rainfall and temporary deformation acceleration, and the landslides usually accelerate after daily rainfall surpasses 20 mm. The study highlights the interaction between active fault zones, rainfall, and resultant deformation patterns, emphasizing the importance of monitoring for landslide instability. This study substantially advances our understanding of large DGDS mechanisms in active fault zones and informs risk prevention strategies for geological disasters.