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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Computer Integrated Manufacturing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
