## 1 Introduction  
Rock burst, a major hazard in coal mining, frequently results in significant casualties and property damage. Investigating risk indicators prior to rock burst events is essential for forecasting such disasters in mining operations; some scholars have studied the current status of coal and gas outburst risk identification in underground coal mines, analyzed the challenges and limitations of existing methods, and proposed directions for future research [1, 2, 3]. Therefore, it is critical that we use a method to accurately obtain the precursor characteristics of rock burst as soon as possible. To accurately forecast rock burst disasters,numerous monitoring and early warning techniques have been developed, achieving significant advancements, including the fracture-induced electromagnetic radiation (FEMR) monitoring method, which are generally applied in the field of rock burst disaster early warning [4, 5]. Researchers have investigated the formation mechanisms and transmission properties of FEMR signals; some scholars have provided insights into the activity of the Dead Sea transition zone by studying electromagnetic radiation signals from rock fractures before and after earthquakes in the Syrian-Turkish border area. Others have explored the electromagnetic radiation characteristics of rock fractures near the seismically active area in southern Israel [6, 7, 8]. Furthermore, numerous studies have explored the time-frequency domain features relevant to rock burst FEMR monitoring and early warning. Some scholars have proposed an early warning method based on microseismic monitoring for rockburst monitoring in Qinling water tunnels. In addition, some scholars have studied the improved waveform adaptive threshold method for microseismic signal denoising [9, 10, 11]. Thus, the FEMR monitoring process offers convenience, ensures continuity in monitoring data, and demonstrates efficacy in responding to rock burst precursory information and early warnings. However, the workload associated with manual procedures for analyzing rock burst precursor characteristics within FEMR signals is substantial, leading to an insufficient advance in warning time and inadequate accuracy in the recognition of precursor characteristics [12, 13]. The enhancement in computational power has led to remarkable advancements in diverse deep-learning model algorithms and applications. These developments have enabled breakthroughs in areas such as computer vision, time-series data analysis, and beyond [14, 15, 16, 17, 18]. In the domain of geophysical studies, advanced machine-learning methodologies, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been utilized across various geophysical methods, which can not only realize recognizing the precursor characteristics of rock burst, but also predict the risk coefficient of disasters [19, 20, 21]. However, in the above method, the timing advance for early warning through precursor characteristics recognition is limited, generally only within about 7 days in advance. Some scholars have proposed a rock burst precursor electromagnetic radiation signal recognition and early warning method based on recursive neural network (RNN). Simultaneously, if the precursor characteristics signal lasts too long, it can result in delayed warning [5, 22]. Hence, some scholars have opted for an alternative methodology, and used the RNNs and the transformer architecture to realize the disaster early warning in the type of time-series prediction. The time-series prediction method can predict FEMR signal data about 7 days in future, achieving the signal prediction in the short-term future. Some scholars have proposed a meteorological early warning method based on deep neural networks for predicting landslides caused by precipitation [23, 24, 25]. However, to achieve the high level of signal prediction accuracy, this method necessitates the integration of multi-variable data through multi-source fusion training, demanding an extensive array of sensor types deployed onsite.