The electromagnetic radiation (EMR) method is a promising geophysical method used to monitor and provide early warnings of coal rock burst disasters. In the underground mining process, personnel activities and electromechanical equipment produce EMR interference signals that affect the accuracy of EMR monitoring. Existing methods for identifying EMR interference signals mainly use the time and amplitude characteristics of the signals. However, these methods need further improvement. The recent advancements in deep learning provide an opportunity to develop a new method for identifying and filtering EMR interference signals. We have developed a method for EMR interference signal recognition based on deep-learning algorithms. The method uses bidirectional long short-term memory recurrent neural networks and the Fourier transform to analyze numerous EMR interference signals along with other signals to intelligently identify and filter EMR signal sequences. The results indicate that our method can respond positively to EMR interferences and accurately eliminate EMR interference signals. The method can significantly improve the reliability of EMR monitoring data and effectively monitor rock burst disasters.

中文翻译：

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基于递归神经网络的煤岩开采电磁辐射干扰信号识别

电磁辐射（EMR）方法是一种很有前景的地球物理方法，用于监测和提供煤岩爆灾害的早期预警。在地下开采过程中，人员活动和机电设备会产生EMR干扰信号，影响EMR监测的准确性。现有的识别EMR干扰信号的方法主要是利用信号的时间和幅度特性。但是，这些方法需要进一步改进。深度学习的最新进展为开发识别和过滤 EMR 干扰信号的新方法提供了机会。我们开发了一种基于深度学习算法的 EMR 干扰信号识别方法。该方法利用双向长短期记忆循环神经网络和傅立叶变换，对众多EMR干扰信号和其他信号进行分析，智能识别和过滤EMR信号序列。结果表明，我们的方法可以积极响应EMR干扰并准确消除EMR干扰信号。该方法可显着提高EMR监测数据的可靠性，有效监测岩爆灾害。