Key Takeaways & Executive Findings
- •• Rockburst stress initially increases then decreases with loading rate, with mica-schist showing more severe damage and tensile cracking. • AE amplitude variance outperforms entropy for identifying rockburst precursors, especially in mica-schist. • Early warning time negatively correlates with loading rate; mica-schist consistently yields shorter warning times than slate. • Critical slowing down (CSD) theory provides enhanced sensitivity for early warning and is recommended for practical systems.
Abstract
Rockburst precursors are critical for disaster warning, yet the complexity of rockburst has hindered the identification of a unified precursor. Furthermore, the influence of loading rates (LRs) on acoustic emission (AE) precursors in different rock types remains poorly understood. This study investigates the AE characteristics and early warning times of rockburst in slate and mica-schist under four LRs (0.05, 0.15, 0.25, and 0.5 MPa/s) using true triaxial unloading tests. The micro-crack state of the samples was evaluated using entropy, while critical slowing down (CSD) theory was applied to interpret AE precursors. The results reveal that as the LR increases, the rockburst stress of both rocks initially rises and then declines, with mica-schist exhibiting more severe damage and a higher dominance of tensile cracks. Notably, identifying rockburst precursors in mica-schist proved more challenging compared to slate. Among the methods tested, AE amplitude variance outperformed entropy in precursor identification. Additionally, the rockburst early warning time was found to be negatively correlated with the LR, with mica-schist consistently showing shorter warning times than slate. The CSD-derived precursor, due to its enhanced sensitivity, is recommended for early warning systems. These findings provide new insights into the role of LRs in rockburst dynamics and offer practical guidance for improving precursor identification and disaster mitigation strategies.
1. Introduction
Rockburst is a complex and dynamic instability phenomenon that occurs in rock masses during underground excavation [1]. Excavation speed usually leads to different degrees of rockburst. With the increasing depth of mining and underground excavation, rockburst has become a more severe disaster [2,3]. The occurrence of rockbursts is closely linked to the initiation and propagation of microcracks. On a laboratory scale, acoustic emission (AE) activity can indicate the development of microcracks and serve as a reference for rockburst warnings [4]. In practical engineering, microseismic (MS) monitoring is considered a powerful tool for predicting rockburst. Therefore, studying the AE or MS precursors is crucial for rockburst prediction.
The analysis methods for AE precursors mainly include parameter characteristics analysis, statistical parameter analysis, fractal dimension theory, G-R (the Gutenburg-Richter law) relationship theory, waveform analysis, and others. Numerous rockburst precursors have been identified by analyzing the characteristics of AE parameters in laboratory or field studies. The AE parameters, including count, energy rate, RA (the ratio of rise time to amplitude) value, entropy, damage, and event number, were used to identify rockburst precursors. In most rocks, these AE parameters exhibit a distinct sudden increase prior to a rockburst event [5,6]. Before the rockburst, there is often a preceding ‘quiet period’ reflected in the number of AE events, energy, and hit rates [7,8]. Coarse-grained granite exhibits a dynamic instability precursor characterized by a sudden increase in AE hit and count rates, followed by a subsequent decline (with the AE hit rate continuing to decrease) [9]. However, the sudden increase in AE parameters is not always consistent across different rock types and loading conditions, highlighting the need for more robust precursor identification methods.
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Chun Zhu, Ming Huang, Fuqiang Ren, Xiaoshuang Li, Jinze Gu, Haibo Li, Manchao He (2025). Multivariate acoustic emissions precursors of rockburst from the perspective of early warning. SinoTechIntel Verified Research. https://doi.org/10.1016/j.ijmst.2025.04.002
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Frequently Asked Questions
What is the main objective of this study?
The study aims to investigate the influence of loading rates on acoustic emission precursors of rockburst in slate and mica-schist, and to identify effective precursor indicators for early warning.
How was the rockburst experiment conducted?
True triaxial unloading tests were performed on slate and mica-schist samples under four different loading rates (0.05, 0.15, 0.25, and 0.5 MPa/s) to simulate rockburst conditions.
What are the key findings regarding precursor identification?
AE amplitude variance outperformed entropy in identifying rockburst precursors, and mica-schist proved more challenging than slate. The critical slowing down (CSD) theory provided enhanced sensitivity and is recommended for early warning systems.
How does loading rate affect early warning time?
Early warning time is negatively correlated with loading rate, meaning higher loading rates result in shorter warning times. Mica-schist consistently showed shorter warning times than slate.
What are the practical implications of this research?
The findings offer practical guidance for improving rockburst precursor identification and disaster mitigation strategies, particularly by recommending CSD-derived precursors for early warning systems.
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