Time-frequency Characteristics of Micro-seismic Signals Before and after Rock Burst
- 10.2991/coal-18.2018.2How to use a DOI?
- rock burst, micro-seismic signal, time-frequency characteristics, precursory information
In this paper, the time-frequency characteristics of micro-seismic signals before and after the rock burst occurred in the 1300 working face of a coal mine in eastern China were analyzed. The results show that before rock burst occurrence, the amplitude was small with gentle vibration fluctuations. And the spectrum distribution was concentrated in the high frequency band, belonging to the random vibration signal. However, the micro-seismic signal appeared obvious change at one hour before rock burst occurrence. The amplitude was increased, with obvious vibration fluctuations, and the frequency spectrum distribution moved to the low frequency band. When rock burst occurred, the amplitude increased by more than ten times, and the frequency spectrum was mainly concentrated in the low frequency band of 0~80 Hz. At the end of the rock burst, the amplitude was reduced. And the low frequency component was reduced, and the spectrum distribution was mainly concentrated in the high frequency band of 130~350 Hz. It can be concluded that rock burst had obvious precursory features before it occurred, therefore the precursory information can be used to predict rock burst occurrence.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xinxin Wang AU - Shijian Yu AU - Dawei Yin PY - 2018/10 DA - 2018/10 TI - Time-frequency Characteristics of Micro-seismic Signals Before and after Rock Burst BT - Proceedings of the 9th China-Russia Symposium "Coal in the 21st Century: Mining, Intelligent Equipment and Environment Protection" (COAL 2018) PB - Atlantis Press SP - 9 EP - 12 SN - 2352-5401 UR - https://doi.org/10.2991/coal-18.2018.2 DO - 10.2991/coal-18.2018.2 ID - Wang2018/10 ER -