TY - JOUR
TI - Time series prediction of rock burst based on deep learning: A case study
AU - Li Hui
AU - Gong Si-Yuan
AU - Zhang Xiu-Feng
AU - Dou Lin-Ming
AU - Li Guo-Ying
AU - Li Hao-Ze
JN - Thermal Science
PY - 2025
VL - 29
IS - 2
SP - 1319
EP - 1324
PT - Article
AB - Rockburst is a common mining hazard causing dynamic damage to coal and rock  masses, posing significant threats to personnel and equipment safety.  Various analytical methods exist to assess impact risks, with microseismic  monitoring systems playing a pivotal role due to their stability, dynamism,  and continuity. This approach utilizes a dual residual connection and a  deeply connected stack architecture to facilitate seasonal-trend  predictions and enhance their interpretability in time series prediction  tasks using a purely deep learning model. The time-frequency and total  energy of microseismic events are predicted using the proposed approach, and  a comparative experimental study is conducted on the time window lengths of  M = 7 days and M = 4 days. The results indicate that the proposed approach  effectively predicts the evolution trend of microseismic event frequency,  with minor discrepancies between the predicted results and the actual  monitoring values, showing its excellent prediction performance and  generalization capability.