Zhonghui Li

Time-Varying Multifractal Characteristics and Formation Mechanism of Loaded Coal Electromagnetic Radiation

Dynamic collapses of deeply mined coal rocks are severe threats to miners. To predict the collapses more accurately using electromagnetic radiation (EMR), we investigate the time-varying multifractal characteristics and formation mechanism of EMR induced by underground coal mining. A series of uniaxial compression and multi-stage loading experiments with coal samples of different mechanical properties were carried out. The EMR signals during their damage evolution were monitored in real-time; the inherent law of EMR time series was analyzed by fractal theory. The results show that the time-varying multifractal characteristics of EMR are determined by damage evolutions process, the dissipated energy caused by damage evolutions such as crack propagation, fractal sliding and shearing can be regard as the fingerprint of various EMR micro-mechanics. Based on the Irreversible thermodynamics and damage mechanics, we introduced the damage internal variable, constructed the dissipative potential function and established the coupled model of the EMR and the dissipative energy, which revealed the nature of dynamic nonlinear characteristics of EMR. Dynamic multifractal spectrum is the objective response of EMR signals, thus it can be used to evaluate the coal deformation and fracture process.

Microseismic Signal Spectra, Energy Characteristics, and Fractal Features Prior to Rock Burst: A Case Study from the Qianqiu Coal Mine, China

Microseismic (MS) technology has been widely adopted for monitoring coal and rock dynamic disasters. Insights into MS wave characteristics contribute to the accurate prediction of these disasters. In this study, MS wave characteristics were analysed from three aspects: the signal spectra, wavelet packet energy and fractal features. It is shown that prior to the rock burst, the MS wave main frequency decreased following a power law, the amplitude linearly increased, the wavelet packet energy tended to become concentrated on the low frequency bands, and the correlation dimension decreased. When the rock burst occurred, the MS wave main frequency, wavelet packet energy and correlation dimension declined to their lowest levels. Meanwhile, the amplitude rose to a maximum. Therefore, the MS wave characteristics in this study were found to effectively identify and extract precursor information of value for predicting rock dynamic disasters.

EMR: An effective method for monitoring and warning of rock burst hazard

Rock burst may cause serious casualties and property losses, and how to conduct effective monitoring and warning is the key to avoid this disaster. In this paper, we reviewed both the rock burst mechanism and the principle of using electromagnetic radiation (EMR) from coal rock to monitor and forewarn rock burst, and systematically studied EMR monitored data of 4 rock bursts of Qianqiu Coal Mine, Yima Coal Group, Co. Ltd. Results show that (1) Before rock burst occurrence, there is a breeding process for stress accumulation and energy concentration inside the coal rock mass subject to external stresses, which causes it to crack, emitting a large amount of EMR; when the EMR level reaches a certain intensity, which reveals that deformation and fracture inside the coal rock mass have become serious, rock burst may occur anytime and its necessary to implement an early warning. (2) Monitored EMR indicators such as its intensity and pulses amount are well and positively correlated before rock bursts occurs, generally showing a rising trend for more than 5 continuous days either slowly or dramatically, and the disaster bursts generally occurs at the lower level within 48 h after reaching its peak intensity. (3) The rank of EMR signals sensitive to rock burst in a descending order is maximum EMR intensity > rate of change in EMR intensity > maximum amount of EMR pulses > rate of change in the amount of EMR pulses.

Application of Electromagnetic Radiation (EMR) Technology in Monitoring and Warning of Coal and Rock Dynamic Disasters

Coal and rock dynamic disasters induced by the large-scale deformation and fracture of surrounding coal and rock, have significant impact on the safe and efficient extraction of coal. Effective and accurate early warning is the elementary basis for the efficient and affordable prevention and control of such kind of disasters. Laboratory experiments have demonstrated that EMR signals are generated during the process of deformation and fracturing of coal or rock under various loading conditions. EMR are paroxysmal pulse signals and increase with stress applied; and the higher the load, the larger the EMR signals. The genetic mechanism, characteristics and laws of EMR of coal and rock were studied by laboratory experiments, based on which, an EMR monitoring and warning method has been put forward. Based on the research results and the method, EMR detectors, Model KBD5 and KBD7, have been developed and employed to predict coal and rock dynamic disasters, such as coal and gas outbursts, rockbursts, monitor goaf roof stability, observe pressure of the surrounding rocks, and determine the width of relief areas. Before the occurrence of a coal and rock dynamic disaster, EMR usually has a corresponding precursor. According to the characteristics of the EMR precursor in time and space, it is possible to recognize the dangerous areas of rockbursts around underground openings and thus give early warnings of the dynamic phenomena as well.

Multi-scale Fractured Coal Gas–Solid Coupling Model and Its Applications in Engineering Projects

With coal mining entering the geological environment of “high stress, rich gas, strong adsorption and low permeability,” the difficulty of joint coal and gas extraction clearly augments, the risk of solid–gas coupling dynamic disasters greatly increases, and the underlying mechanisms become more complex. In this paper, based on the characteristics of coal’s multi-scale structure and spatiotemporal variation, the multi-scale fractured coal gas–solid coupling model (MSFM) was built. In this model, the interaction between coal matrix and its fractures and the mechanical characteristics of gas-bearing coal were considered, as well as their coupling relationship. By MATLAB software, the stress–damage–seepage numerical computation programs were developed, which were applied into Comsol Multiphysics to simulate gas flow caused by coal mining. The simulation results showed the spatial variability of coal elastic modulus and cross-flow behaviors of coal seam gas, which were superior to the results of traditional gas–solid coupling model. And the numerical results obtained from MSFM were closer to the measured results in field, while the computation results of traditional model were slightly higher than the measured results. Furthermore, the MSFM in a large scale was verified by field engineering project.

Experimental research on the electromagnetic radiation (EMR) characteristics of cracked rock

Coal rock would emit the electromagnetic radiation (EMR) while deformation and fracture, and there exists structural body in the coal rock because of mining and geological structure. In this paper, we conducted an experimental test the EMR characteristics of cracked rock under loading. Results show that crack appears firstly in the prefabricated crack tip then grows stably parallel to the maximum principal stress, and the coal rock buckling failure is caused by the wing crack tension. Besides, the compressive strength significantly decreases because of the precrack, and the compressive strength increases with the crack angle. Intact rock EMR increases with the loading, and the cracked rock EMR shows stage and fluctuant characteristics. The bigger the angle, the more obvious the stage and fluctuant characteristics, that is EMR becomes richer. While the cracked angle is little, EMR is mainly caused by the electric charge rapid separates because of friction sliding. While the cracked angle is big, there is another significant contribution to EMR, which is caused by the electric dipole transient of crack expansion. Through this, we can know more clear about the crack extends route and the corresponding influence on the EMR characteristic and mechanism, which has important theoretical and practical significance to monitor the coal rock dynamical disasters.

Response Characteristics and Monitoring-Warning of Acoustic-Electromagnetic Signals in Coal Roadway Heading

Accurate prediction of coal and gas outburst risk is essential for outburst prevention and control. In this paper, the evolution law of electromagnetic radiation (EMR) and acoustic emission (AE) in the process of deformation and rupture of coal and rock are analyzed. Then, the characteristic laws of EMR and AE signals in coal roadway during the driving process of Jiulishan Coal Mine are studied. The critical values of acoustic and electromagnetic monitoring and warning is solved using the fuzzy pattern recognition method. Results show that: (1) The change trending of AE and EMR signals is consistent with stress level. However, there are abrupt changes in the acoustic and electromagnetic signals before the stress peak. The degree of signal fluctuation is significant. (2) EMR is mainly produced by rheological and frictional deformation after coal body ruptures, while AE is mainly caused by the rupture of coal and rock. The intensity and pulse of EMR can better respond and early-warn the danger of coal and gas outburst during tunneling. (3) By using fuzzy pattern recognition method, the critical values of acoustic and electromagnetic monitoring and warning can be solved. It is more reliable to predict the outburst danger when the membership degree \( \mu_{A} (x_{0} ) \) is 0.6–0.8.

Early-Warning of Rockbursts Based on Time Series Analysis of Electromagnetic Radiation Data

Based on time series analysis of electromagnetic radiation (EMR), an EMR model of rockburst early-warning was established to quantitatively analyze EMR trends and characterize EMR precursors before an impending rockburst phenomenon, with the goal to improve the EMR prediction accuracy of a rockburst danger. The model includes two parts: one is warning criterion of EMR abnormity based on normal distribution of mean and variance parameters; the other is the risk identification method based on trends forecast utilizing time series analysis. It has been applied successfully at the No.237 working face in Hegang coalmine. The results show that the model can quantitatively determine the degree of rockburst risk and identify hazardous area, which provides a profound basis for the quantitative identification of electromagnetic radiation precursor and accurate prediction of rockbursts.