Rock burst monitoring and early warning under uncertainty based on multi-information fusion approach

Abstract

Rock burst monitoring and early warning is known as a challenging problem in underground engineering. Existing research mainly focuses on forecast the rock burst using single geophysical signal, which is found limited in characterising the hazard under changeable geological conditions. This paper proposes a novel Bayesian network-based rock burst early warning approach. A multi-index system is firstly constructed for rock burst early warning by extracting characteristic parameters of multiple geophysical signals. Redundant indices are then eliminated to decrease the inconsistency of multiple information. The probabilistically causalities between rock burst and multiple indices and its quantisation are respectively described by directed acyclic graph and conditional probabilities in Bayesian network, and the occurrence probability of rock burst is forecasted by fusing multiple geophysical signals. The study case of LW 1208, Hongyang coal mine, China illustrates the advantages of the proposed approach on rock burst hazard early warning in the presence of uncertainties.