Fractal Characteristics of Temporal-Spatial Distribution of Acoustic Emission during Coal Bursts

"Due to recent coal bursts in Australian coalmines, research of coal bursts has attracted intensive interest from mining researchers and engineers worldwide. Previous research has shown that the sudden drop of fractal dimension may be a precursor of catastrophic failure of rock. This paper examines the fractal characteristics of temporal-spatial distribution of acoustic emission signals released during the compression loading process of coal. This research suggests the possibility of coal burst forecasting based on acoustic emission data. The concept and calculation equation of fractal dimension is introduced in this paper. Additionally, uniaxial compression experiments of Australian coal samples are conducted, and the acoustic emission waveform and 3D location are recorded simultaneously by eight sensors Express-8 AE monitoring equipment in the loading process. The experimental results indicate that the continued slowdown of fractal dimension in time sequence can be used as a precursory signal of coal sample brittle failure. The fractal dimension of the spatial distribution of acoustic emission events decreases with the evolution of coal fracturing.INTRODUCTIONCoal bursts refer to the catastrophic failure of overstressed coal subject to the dynamic and static loads. In recent years, coal bursts have occurred in major coal mining countries and this phenomenon has attracted the research interests of international mining and geotechnical scholars. A total of five coal burst accidents happened in Australian coalmines since 2014. On October 20, 2018, a coal burst occurred at Longyun Coalmine in China’s Shandong province, which led to 21 fatalities. Unfortunately, even after decades of research, it seems neither more accurate forecasting methods nor comprehensive controlling measures put forward by researchers have solved the problems posed by coal bursts.As an instantons energy releasing process, coal bursts are always associated with energy dissipation in the forms of acoustic emission, micro seismicity, and electromagnetic radiation (Yamada, Masuda, and Mizutani, 1989). It is believed by many researchers that the frequency–magnitude relationship of micro seismicity induced by rock failure satisfies Gutenberg-Richer relationship as well (Feng et al., 2017). Hence, based on the principal and theory of earthquake source locating, micro-seismicity monitoring equipment has been adopted in many coalmines to locate seismic event origins associated with coal and rock failure in an attempt to better forecast coal and rock bursts. Although many parameters such as b value (parameter of Gutenberg and Richter’s law) (Lu et al., 2015) have been adopted as indicators of risk, the forecasting of coal bursts is still of low accuracy. The same is true with earthquake prediction, as it remains as a worldwide problem even after decades of research and practice. Acoustic emission (AE) methods are now widely adopted to detect rock failure processes under an applied stress for application predicting the abrupt failure of rock in the laboratory (Li et al., 2017). Previous studies have found that significant overlap exists between research regarding AE and micro seismicity with respect to generation process (both are concerned with the generation and propagation of elastic wave) and event origin location, as geophysical signals of both are same (Lockner, 1993). That is, the frequency–magnitude relationship and early-warning indicators found by AE research in the laboratory have a potential applicability to in-field micro seismic monitoring of coal bursts. Through experimental study, Feng and Seto found that the temporal distribution of acoustic emission counts of rock have distinct fractal structures during load-increase processes (Feng and Seto, 1999). Landis and Lucie found good correlation between fracture energy and AE energy during testing of mortar specimens as well (Landis and Lucie, 2002). Based on preliminary experimental study, many schol"