A fuzzy comprehensive evaluation methodology for rock burst forecasting using microseismic monitoring. (October 2018)

• Record Type: Journal Article
• Authors: Cai, Wu; Dou, Linming; Zhang, Min; Cao, Wenzhuo; Shi, Ji-Quan; Feng, Longfei
• Is Part Of: Tunnelling and underground space technology. Volume 80(2018)
• Page Start: 232
• Page End: 245
• Publication Date: 2018-10
• Subjects: Rock burst forecasting -- Microseismic (MS) monitoring -- Acoustic emission (AE) -- Fuzzy comprehensive evaluation -- Confusion matrix; Tunneling -- Periodicals; Underground construction -- Periodicals; Tunnels -- Periodicals; Underground areas -- Periodicals; 624.193
• DOI: 10.1016/j.tust.2018.06.029
• Languages: English
• ISSNs: 0886-7798

Abstract

Highlights: A fuzzy comprehensive evaluation model was developed for the forecasting of rock bursts. Laboratory AE measurements were performed to select MS indices used as input to the fuzzy model. The application of this model has been successfully demonstrated in a coal mine in China. Abstract: Rock bursts have become one of the most severe risks in underground coal mining and its forecasting is an important component in the safety management. Subsurface microseismic (MS) monitoring is considered potentially as a powerful tool for rock burst forecasting. In this study, a methodology for rock burst forecasting involving the use of a fuzzy comprehensive evaluation model was developed, which allows for a more quantitative evaluation of the likelihood for the occurrence of a rock burst incident. In the fuzzy model, the membership function was built using Gaussian shape combined with the exponential distribution function from the reliability theory. The weight of each index was determined utilising the performance metric F score from the confusion matrix. The comprehensive forecasting result was obtained by integrating the maximum membership degree principle (MMDP) and the variable fuzzy pattern recognition (VFPR). This methodology has been applied to a coal mine in China to forecast rock bursts. To select MS indices for rock burst forecasting using the fuzzy evaluation model, laboratory acoustic emission (AE) measurements of coal samples collected from the mine were performed. The model parameters were first calibrated using historical MS data over a period of four months, during which six rock burst incidents were observed. This calibrated model was able to forecast the occurrence of a subsequent rock burst incident in the mine.