Rock burst hazard evaluation of coal mining face based on a Gaussian mixture model

Authors

CUI Feng, College of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, China; Key Laboratory of Western Mine Mining and Disaster Prevention, Ministry of Education, Xi’an University of Science and Technology, Xi’an 710054, China; Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources，Xi’an 710021, ChinaFollow
LI Yifei, College of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
JIA Chong, College of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
LU Changliang, College of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
HE Shifeng, College of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
ZHANG Suilin, College of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
TIAN Mengqi, College of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, China

Abstract

Objective Deep insights into the probabilistic information contained in the energy distribution of acoustic emission (AE) or microseismic events are significant for the rock burst hazard evaluation of coal mining face. Methods This study investigated No.40111 mining face of the No.4 coal seam at the Dafosi Coal Mine in Shaanxi Province. Using physical simulation experiments with similar materials, theoretical analysis, and on-site monitoring, this study investigated the evolutionary patterns of AE monitoring data during coal mining and illustrated the physical meaning of fluctuations in the probability distribution of AE energy. Accordingly, this study proposed an index model for the rock burst hazard evaluation based on a Gaussian mixture model (GMM) and confidence intervals and validated the proposed model based on field microseismic data. Results and Conclusions The results indicate that the overlying strata collapsed periodically during mining, with the collapse being accompanied by intensive release of AE energy. The probability density function (PDF) of the total energy exhibited a multi-degree-of-freedom asymmetric distribution. The comparison of multiple indices of fitting effects, such as the residual sum of squares, reveals that the GMM is the optimal fitting model. As indicated by the GMM clustering analysis based on the expectation-maximization (EM) algorithm, the total energy distribution of AE events can be categorized into two types, namely the high-frequency/low-energy and low-frequency/high-energy AE signals, with the latter type consistent with the sudden occurrence and high-energy destruction of rock burst events. This study conducted a rock burst hazard evaluation of the mining face based on the probability-energy gradient variations, providing a novel probabilistic approach for the rock burst hazard evaluation of coal mining face. The new assessment method based on probabilistic information has the potential to be applied in the monitoring, early warning, and subsequent prevention of rock bursts.

Keywords

Gaussian mixture model, probability density function, cluster analysis, rock burst hazard evaluation, early warning for dynamic disaster

DOI

10.12363/issn.1001-1986.24.01.0072

Recommended Citation

CUI Feng, LI Yifei, JIA Chong, et al. (2024) "Rock burst hazard evaluation of coal mining face based on a Gaussian mixture model," Coal Geology & Exploration: Vol. 52: Iss. 10, Article 9.
DOI: 10.12363/issn.1001-1986.24.01.0072
Available at: https://cge.researchcommons.org/journal/vol52/iss10/9

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