Characteristics and Classification of Microseismic Signals in Heading Face of Coal Mine: Implication for Coal and Gas Outburst Warning

Abstract Microseismic monitoring technique has been proved to be an effective method to predict catastrophic disasters in underground engineering. However, it is difficult to identify the microseismic signals of coal fracture due to the complex environment and operations in coal mine, which hinders...
Ausführliche Beschreibung

Abstract Microseismic monitoring technique has been proved to be an effective method to predict catastrophic disasters in underground engineering. However, it is difficult to identify the microseismic signals of coal fracture due to the complex environment and operations in coal mine, which hinders the application of microseismic monitoring on the early warning of coal and gas outbursts (outbursts). In this work, microseismic signals in the heading face of Xinyuan coal mine were recorded under different operations, including drilling of holes, roadway support, moving of belt conveyor, driving activities, blasting and no work. The records show that the microseismic events occur frequently under driving activities, while a few microseismic events happen during the no work period. The characteristics of time domain, frequency domain and time–frequency for different microseismic signals were then analyzed based on the fast Fourier transform and short-time Fourier transform methods. The results indicate that the low-frequency waveform is well developed under the drilling of holes, roadway support, moving of belt conveyor and no work period. A short and discontinuous high-frequency waveform exists in the work of roadway support, moving of belt conveyor and no work period, which belongs to the coal fracture microseismic signals. Driving activities have massive low-frequency and high-frequency waveforms, but the high-frequency waveform corresponds to the coal fracture microseismic signals. The high-frequency waveform existing in the blasting work presents an obvious periodic feature. Combined with the long short-term memory (LSTM) network, different MS signals were further classified after extracting the maximum amplitude, dominant frequency, rising time, duration time and period from separation waveforms. Compared with the k-nearest neighbor and support vector machine models, the classification result of LSTM has the lowest loss value and highest accuracy rate of about 0.18 and 85%. The microseismic events of coal fracture identified by the LSTM have a better sequential response to gas concentration, which can be used to warn the outbursts in advance. This study provides a theoretical support for the early warning of outbursts and is meaningful for the safety and efficient production of coal mines. Ausführliche Beschreibung

Autor*in:	Shu, Longyong [verfasserIn] Liu, Zhengshuai Wang, Kai Zhu, Nannan Yang, Jian

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Coal and gas outbursts Microseismic monitoring Waveform features Coal fracture microseismic signals Long short-term memory network

Anmerkung:

© The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Rock mechanics and rock engineering - Wien [u.a.] : Springer, 1969, 55(2022), 11 vom: 23. Aug., Seite 6905-6919
Übergeordnetes Werk:	volume:55 ; year:2022 ; number:11 ; day:23 ; month:08 ; pages:6905-6919

Links:	Volltext

DOI / URN:	10.1007/s00603-022-03028-x

Katalog-ID:	SPR048457531