Distriformer: Research on a Distributed Training Rockburst Prediction Method

Abstract

The precise forecasting of rockburst is fundamental for safeguarding human lives and property, upholding national energy security, and protecting social welfare. Traditional methods for predicting rockburst suffer from poor accuracy and extended model training durations. This paper proposes a distributed training rockburst prediction method called Distriformer, which uses deep learning technology combined with distributed training methods to predict rockburst. To assess the efficacy of the Distriformer rockburst model proposed herein, five datasets were used to compare the proposed method with Transformer and Informer. The experimental results indicate that, compared with Transformer, the proposed method reduces the mean absolute error by 44.4% and the root mean square error by 30.7% on average. In terms of training time, the proposed method achieves an average accelaration ratio of 1.72. The Distriformer rockburst model enhances the accuracy of rockburst prediction, reduces training time, and serves as a reference for development of subsequent real-time prediction models for extensive rockburst data.