Time series prediction refers to the learning of existing observed data of a parameter and predicting its future evolution. Based on the application of machine/deep learning methods in the field of engineering geology, it is desirable to predict the time series evolution of microseismic parameters in the process of rockburst development. Our study explores key microseismic indices that help describe the development process of rockbursts based on abundant rockburst data obtained from deep underground engineering construction. The integrated process of dynamic moving-window method and improved convolutional neural network (CNN) realizes the evolution prediction of multiple microseismic parameters or their different combinations, and the modified model structures of a univariate, multivariate input and a single-step, multi-step output are established. Various models of the multiple microseismic parameters for the CNN-based time series prediction are innovated, including a univariate prediction model, a multiple parallel series model, a multiple input series model, and a multivariate multi-step prediction model. Model training, testing, and interpretation of the rockburst risk and comparative analyses of the different models are performed for the complete process of multiple rockbursts. The results show that the proposed models can well predict the evolution trends in the various key characteristics during rockbursts. The predicted trend of multiple microseismic parameters provides time labels for rockburst prediction and risk judgement, which is conducive to rockburst early warning. This study provides a new research idea for the prediction and early warning of rockbursts in the field of deep underground and mining engineering.

时间序列预测是指学习一个参数的现有观测数据并预测其未来的演变。基于机器/深度学习方法在工程地质领域的应用，期望预测岩爆发展过程中微地震参数的时间序列演化。我们的研究基于从深部地下工程建设中获得的大量岩爆数据，探索了有助于描述岩爆发展过程的关键微地震指标。动态移动窗口法和改进卷积神经网络（CNN）的集成过程，实现了多个微地震参数或其不同组合的演化预测，以及单变量、多变量输入和单步、多步的修正模型结构。输出建立。创新了基于CNN的时间序列预测的多种微震参数模型，包括单变量预测模型、多平行序列模型、多输入序列模型和多元多步预测模型。针对多个岩爆的完整过程进行了岩爆风险的模型训练、测试和解释以及不同模型的比较分析。结果表明，所提出的模型可以很好地预测岩爆过程中各种关键特征的演化趋势。多个微震参数的预测趋势为岩爆预测和风险判断提供了时间标签，有利于岩爆预警。