## 1 Introduction  
Mine microseism detection monitors the microseismic phenomena in the mine and mining area through vibration sensors online in real-time, detects the energy size of the seismic source, combines the change characteristics of the microseismic wave and microseismic source [1], monitors the geological movement inside the mine, including earthquakes, surface settlement, fracture activities, etc., and understands the coal seam's existence to identify the fragile strata and brittle coal seams. Through timely monitoring and analysis of microseismic data, potential geological disaster risks can be detected in advance, and ultimately, real-time monitoring of rock explosion hazards can be realized to provide early warning and protection for the safety of miners. Coal mine microseism detection requires synchronized sensor data acquisition because of the large number of acquisition points (tens to hundreds) and the complex geological structure of the underground space. The data acquisition points are arranged in the complex underground environment, and the acquisition frequency varies from tens of Hertz to thousands of Hertz due to the limitations of the field environment [2]. Because the data acquisition frequency is greatly limited by the field environment, the sensitivity of the sensors cannot meet the actual dynamic detection requirements of the microseism detection in coal mines, and the data acquisition method often fails to achieve the desired detection effect in terms of real-time detection and accuracy. Therefore, it is necessary to find an effective microvibration detection method for coal mines underground. In terms of microseism detection in coal mines, [PERSON], [PERSON] and [PERSON], [PERSON] et al. proposed a method to automatically classify microseismic events in coal mines using convolutional neural networks. The convolutional neural networks were trained to identify guided wave energy in distributed acoustic sensing coal mine maps, because microseismic events originating in or near low-speed reservoirs (such as Eagle Ford) would produce significant guided wave energy. The field events with guided waves are classified as guiding energy that occurs inside or near, and microseisms in coal mines are detected according to the characteristic guided waves [3]. [PERSON], [PERSON] et al. developed an event detection method based on a deep convolutional neural network (CNN). The labels of 4000 synthetic microseismic events and random noise records were created to realize microseism detection [4]. [PERSON], [PERSON] et al. studied the vibration prediction method of Vietnam's New Biot Open Pit Coal Mine based on artificial neural networks. This method combined nine artificial neural networks (ANNs) to jointly realize the prediction of coal mine vibration and verified through experiments that the root-mean-squared error (RMSE), determination coefficient (R2), and average absolute error (MAE) of vibration prediction are good [5]; however, the above three methods do not determine the number and location of mine data during the process of coal mine microseism detection and can only determine whether vibration has occurred. They cannot obtain high-precision coal mine microseism detection results and do not extract the spatial features of coal mine microseismic signals. Therefore, the sensitivity of coal mine microseism detection is relatively low. In terms of spatiotemporal feature extraction, the Markov chain is widely used to extract spatiotemporal change feature data due to its memory property [6] and calculate the state transition matrix and cross-state transition matrix to extract the spatiotemporal features of sensor data streams, and it is widely used in various fields due to the simplicity of its algorithm [7]. The support vector regression algorithm has gradually become widely used after neural networks [8].