Abstract
ABSTRACT The occurrence of mine quake is subject to coupling control of multiple factors. To study the influence of mine quake distribution on rock burst under different incentive conditions, based on massive microseismic monitoring data, this paper established time-varying signal analysis model for deep recurrent neural network and restricted Boltzmann machine deep process neural network, which supports multi-perspective and high-dimensional analysis of complex mine quake. For feature extraction of multi-source acoustic emission real-time monitoring signals and the discrimination of microquake and mine quake intensity, a deep neural network model was proposed for the identification of mine quake type and intensity. Practice has shown that the discrimination effect is fine.
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