Rockbursts have become a significant hazard in underground mining, underscoring the need for a robust early warning model to ensure safety management. This study presents a novel approach for rockburst prediction, integrating the Mann-Kendall trend test (MKT) and multi-indices fusion to enable real-time and quantitative assessment of rockburst hazards. The methodology employed in this study involves the development of a comprehensive precursory index library for rockbursts. The MKT is then applied to analyze the real-time trend of each index, with adherence to rockburst characterization laws serving as the warning criterion. By employing a confusion matrix, the warning effectiveness of each index is assessed, enabling index preference determination. Ultimately, the integrated rockburst hazard index Q is derived through data fusion. The results demonstrate that the proposed model achieves a warning effectiveness of 0.563 for Q, surpassing the performance of any individual index. Moreover, the model's adaptability and scalability are enhanced through periodic updates driven by actual field monitoring data, making it suitable for complex underground working environments. By providing an efficient and accurate basis for decision-making, the proposed model holds great potential for the prevention and control of rockbursts. It offers a valuable tool for enhancing safety measures in underground mining operations.

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Keywords: Rockburst, Microseismicity, Early warning, Mann-Kendall trend test, Confusion matrix, Multi-indices fusion