Borehole pressure relief is an important measure to prevent and control rock bursts for high stress mine. However, the complex and changeable coal and rock properties cannot be effectively identified during the borehole pressure relief process, resulting in instability phenomena such as drill pipe jumping, drill sticking, and drill holding in the borehole system, which seriously affects the borehole pressure relief efficiency and operation safety. To perceive the drilling status of coal and rock in real time, this paper provides an in-depth analysis of the electromagnetic signals of coal and rock during drilling through simulations and experiments, so as to accurately identify the coal and rock properties. First, a small-size coaxially fed microstrip antenna that can be used on a specific drill pipe is designed, and some simulations are performed to analyze the propagation characteristics of electromagnetic signals under different drilling conditions. The electromagnetic signals of different coal and rock properties are significantly different in the range of 4-8 GHz, and the frequencies corresponding to the S11 extremes are 5.432 GHz, 5.436 GHz, 5.532 GHz, 5.552 GHz, 5.516 GHz, and 5.468 GHz, respectively. Then, the electromagnetic signals are mapped into time-frequency diagrams through continuous wavelet transform to provide training samples for coal and rock properties recognition model. Subsequently, the MobileNetV3-EACS model is proposed by embedding the efficient multi-channel attention mechanism and modifying the classification network structure to improve the detection accuracy and classification adaptability, achieving the effective recognition of coal and rock properties. Finally, an experimental platform of coal and rock borehole system is built and multiple sets of comparative experiments are conducted. The experimental results show that the MobileNetV3-EACS has a prediction accuracy of 99.11%, which is increased by 1.78% compared to the classical MobileNetV3, verifying the effectiveness and superiority of the proposed coal and rock properties recognition method.
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