ARMA prediction model for electromagnetic radiation data preceeding a rock burst

作者：Liu Zhen Tang^*; Liu Xiao Fei; Wang En Yuan

来源：Journal of China University of Mining & Technology, 2009, 38(3): 316-320.

• ARMA model
• Auto-regressive
• Electromagnetic radiation
• Electromagnetic radiation (EMR)
• Moving averages
• Precursor
• Prediction model
• Similarity degree
• Statistical analysis
• Working face

SAS statistical analysis software was used to test the randomness of electromagnetic radiation (EMR) observed during the ';1.12 rock burst'; of the number 237 working face in the Nanshan coal mine. An auto-regressive-moving-average (ARMA) model was fitted to the EMR data and used to forecast twelve observations into the future. The results show that the rock burst EMR data are non-white noise, stationary and can be fitted with an AR(3) model. Comparing the model EMR values to the real data, the similarity degree is about 66%. An ARMA model can use data preceding an event to describe changes in the EMR trends quantitatively.

• 出版日期2009
• 单位中国矿业大学（北京）