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<p>Rock failure is accompanied by abnormal stress accumulation and sudden release. However, the invisible internal structure and complex stress environment make it challenging to effectively capture temporal precursors and spatial distribution of stress mutation in the failure process. This paper integrates acoustic emission (AE), traveltime tomography, and critical slowing down theory as a method for investigating granite failure under biaxial compression. The proposed method is applied to monitor the spatial-temporal evolution of stress and identify precursors to stress mutations during rock failure. It not only can investigate expansion trend of micro-cracks and reveal stress evolution patterns through velocity, but also can characterize temporal precursors and spatial distribution of stress mutation. Experiment results show: (1) Traveltime tomography reveals stress-velocity correlations that velocity increases (crack closure/elastic stages) reflect micro-crack compaction and stress concentration, while velocity decreases (stable/unstable stages) indicate fracture propagation; (2) Intermediate principal stress critically modulates these patterns that higher intermediate principal stress amplifies early-stage velocity heterogeneity (local stress concentration) but induces premature micro-fracturing in elastic stages (local velocity drops), resulting in diminished overall velocity changes; (3) Autocorrelation coefficient and spatial variance effectively characterize stress mutation thresholds in time- and space-domain respectively. The autocorrelation coefficient detects critical slowing phenomena prior to instability, while spatial variance pinpoints stress anomaly localization. These indexes provide earlier warnings than conventional AE rate thresholds, validated by spatial-temporal alignment with macroscopic fractures. The proposed method enhances real-time monitoring of stress redistribution, offering technical support for early warning and risk mitigation in deep mining engineering.</p>

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Precursor characterization of granite biaxial loading failure using the velocity critical slowing down theory

  • 期刊论文

  • 发表日期：

    2026年2月1日

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  • Zhong-wei Pei,
  • Long-jun Dong,
  • Yi-han Zhang,
  • Dao-yuan Sun

摘要

Rock failure is accompanied by abnormal stress accumulation and sudden release. However, the invisible internal structure and complex stress environment make it challenging to effectively capture temporal precursors and spatial distribution of stress mutation in the failure process. This paper integrates acoustic emission (AE), traveltime tomography, and critical slowing down theory as a method for investigating granite failure under biaxial compression. The proposed method is applied to monitor the spatial-temporal evolution of stress and identify precursors to stress mutations during rock failure. It not only can investigate expansion trend of micro-cracks and reveal stress evolution patterns through velocity, but also can characterize temporal precursors and spatial distribution of stress mutation. Experiment results show: (1) Traveltime tomography reveals stress-velocity correlations that velocity increases (crack closure/elastic stages) reflect micro-crack compaction and stress concentration, while velocity decreases (stable/unstable stages) indicate fracture propagation; (2) Intermediate principal stress critically modulates these patterns that higher intermediate principal stress amplifies early-stage velocity heterogeneity (local stress concentration) but induces premature micro-fracturing in elastic stages (local velocity drops), resulting in diminished overall velocity changes; (3) Autocorrelation coefficient and spatial variance effectively characterize stress mutation thresholds in time- and space-domain respectively. The autocorrelation coefficient detects critical slowing phenomena prior to instability, while spatial variance pinpoints stress anomaly localization. These indexes provide earlier warnings than conventional AE rate thresholds, validated by spatial-temporal alignment with macroscopic fractures. The proposed method enhances real-time monitoring of stress redistribution, offering technical support for early warning and risk mitigation in deep mining engineering.