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<p>The transient macro-deformation of seismic rockfalls demonstrates significant nonlinearity and complexity, occurring over a brief period before initiation. Conventional landslide early warning techniques struggle to accurately capture this catastrophic transformation process and provide timely alerts. To elucidate the critical transition dynamics of the system and predict the threshold for catastrophic bifurcation, this study proposes a dynamic warning model based on catastrophe theory and critical slowing down (CSD) theory. This model indicates that as the recovery rate of perturbations decreases, the autocorrelation coefficient (AC) approaches 1, and the variance's tangent angle nears 90°. Here we simulate the damage and instability processes of three typical rockfalls subjected to seismic excitation through shaking table tests to validate the model's applicability. By analyzing sensitive state parameters such as inclination, acceleration, and displacement of the monitored rockfalls, we identify the normal form and scaling behavior of the dynamical system near the critical point. Although the seismic rockfall is defined by abrupt shifts, this transition is found to be practically relevant. Consequently, the autocorrelation behavior of the system before the bifurcation crossing presents a viable opportunity for short-term warnings of rockfall initiation. In addition, we explore the interplay between various influences and CSD characteristics. Our findings reveal that broader datasets yield smoother AC curves with reduced amplitude. Filtering seismic time series signals and recognizing trends exhibited by multiple parameters can provide more accurate data for subsequent warning model calculations and improve warning reliability.</p>

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Critical Transitions and Short-Term Warning Signals of Seismic Rockfall Systems

  • 期刊论文

  • 发表日期：

    2025年6月30日

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  • Jie Fan,
  • Jing Lian,
  • Changwei Yang,
  • Zhikun Wang,
  • Shiguang Zhou,
  • Xuanming Ding,
  • Xunfeng Li

摘要

The transient macro-deformation of seismic rockfalls demonstrates significant nonlinearity and complexity, occurring over a brief period before initiation. Conventional landslide early warning techniques struggle to accurately capture this catastrophic transformation process and provide timely alerts. To elucidate the critical transition dynamics of the system and predict the threshold for catastrophic bifurcation, this study proposes a dynamic warning model based on catastrophe theory and critical slowing down (CSD) theory. This model indicates that as the recovery rate of perturbations decreases, the autocorrelation coefficient (AC) approaches 1, and the variance's tangent angle nears 90°. Here we simulate the damage and instability processes of three typical rockfalls subjected to seismic excitation through shaking table tests to validate the model's applicability. By analyzing sensitive state parameters such as inclination, acceleration, and displacement of the monitored rockfalls, we identify the normal form and scaling behavior of the dynamical system near the critical point. Although the seismic rockfall is defined by abrupt shifts, this transition is found to be practically relevant. Consequently, the autocorrelation behavior of the system before the bifurcation crossing presents a viable opportunity for short-term warnings of rockfall initiation. In addition, we explore the interplay between various influences and CSD characteristics. Our findings reveal that broader datasets yield smoother AC curves with reduced amplitude. Filtering seismic time series signals and recognizing trends exhibited by multiple parameters can provide more accurate data for subsequent warning model calculations and improve warning reliability.