Research on the Time Series Prediction of Acoustic Emission Parameters Based on the Factor Analysis–Particle Swarm Optimization Back Propagation Model

Early warning for rock blasting is crucial for ensuring the safety of deep underground engineering. Existing methods primarily focus on classifying rock blasting levels, which makes it difficult to provide timely warnings. This paper proposes a novel early warning framework for rock blasting based on time series prediction of acoustic emission (AE) parameters. Based on uniaxial rock tests, ten AE parameters (rise time, ring count, energy, duration, amplitude, average frequency, RMS voltage, average signal level, peak frequency, and initial frequency) are identified as potential indicators for rock blasting early warning. These ten parameters collectively affect the accuracy of AE monitoring. Factor analysis is employed to process the normalized AE data, simplifying the data structure and identifying common variables. Additionally, it is found that the BP neural network optimized by Particle Swarm Optimization (PSO) is more suitable for predicting the future evolution of these AE parameters. This makes it possible to establish a comprehensive multi-indicator early warning system. The proposed framework provides a new perspective for rock blasting early warning systems.