Electromagnetic interference in underground coal mines often appears as transient bursts, caused by dense high-power equipment, narrow spaces, and severe multipath reflections. These bursts pose a threat to wireless communication reliability in smart mining. In this paper, a large-scale statistical analysis and envelope classification of 26,425 bursts from 871 min of field data at a coal mine belt conveyor is reported. Using the monotonicity and the detrended zero-crossing rate of the amplitude sequence, burst envelopes are sorted into three types: decay, oscillation, and fluctuation. To examine the physical consistency of this classification, three analyses are performed: exponential decay fitting (H1), Rician and Rayleigh distribution fitting (H2), and autocorrelation analysis (H3). The results show that decay-type bursts cluster around 1.2 MHz in both time and frequency, oscillation-type bursts have a bimodal amplitude distribution that is approximately Rician (K = 8.10), and the three types differ markedly in their autocorrelation structure. These findings provide a statistical and physical characterization of EMI burst patterns in underground coal mines, and they may provide a reference for mine EMC design and adaptive anti-interference methods, subject to validation at additional sites.
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Yuhao Jia (2026) studied this question.
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