Abstract Rockburst is a phenomenon where the elastic deformation may be emitted suddenly because of rock fragmentation, ejection, launching, and even earthquake. This may result in casualties, failure or deformation of the support structure, and damage to field equipment. Therefore, early warning of rockburst is significant. In this paper, a dynamic early warning model of rockburst using microseismic multi-parameters based on Bayesian network is proposed. Taking the moment magnitude, seismic energy, source radius, apparent stress, and dynamic stress drop as Bayesian network parameters input. 114 sets of parameters required for Bayesian network structure learning are obtained by pre-processing the historical data of rockburst, and belief update is performed by the Junction Tree algorithm. Besides, the model passes self-validation, 6-fold cross-validation, ROC curve analysis and using new historical data to real-time early warning analysis. The results indicate that the proposed model has good precision and can effectively realize early warning of rockbursts. Furthermore, through the strength of the influence of parent–child nodes and sensitivity analysis, it can find that moment magnitude and seismic energy are the most influential parameters. They can as a significant reference for the early warning of rockburst.

中文翻译：

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基于贝叶斯网络的微震多参数岩爆动态预警

摘要 岩爆是由于岩石破碎、抛射、发射甚至地震而可能突然发出弹性变形的现象。这可能会导致人员伤亡、支撑结构故障或变形以及现场设备损坏。因此，岩爆预警具有重要意义。本文提出了一种基于贝叶斯网络的微震多参数岩爆动态预警模型。以矩震级、地震能量、震源半径、视应力和动态应力降作为贝叶斯网络参数输入。通过对岩爆历史数据进行预处理，得到贝叶斯网络结构学习所需的114组参数，通过Junction Tree算法进行置信度更新。此外，该模型通过了自我验证，6折交叉验证，ROC曲线分析，利用新的历史数据进行实时预警分析。结果表明，该模型具有较好的精度，能有效实现岩爆预警。此外，通过父子节点的影响强度和敏感性分析，可以发现矩震级和地震能量是影响最大的参数。可作为岩爆预警的重要参考。可以发现，矩震级和地震能量是影响最大的参数。可作为岩爆预警的重要参考。可以发现，矩震级和地震能量是影响最大的参数。可作为岩爆预警的重要参考。