Resumen
In view of the inconsistency of guided wave energy in distributed acoustic sensing coal mine maps and the difficulty in distinguishing the vibration levels of coal mines, which leads to the poor sensitivity and accuracy of microseism detection, a coal mine microseism detection method based on time?space characteristics and a support vector regression algorithm is proposed to ensure the safety of coal mine operations. The spatiotemporal sliding window was used to collect the coal mine data in real-time, and the continuous attribute discretization algorithm based on entropy was used to discretize the coal mine data, then the data were mapped to different state spaces to build a Markov chain; by calculating the state transition probability matrix and the cross-state probability transition matrix, respectively, the temporal and spatial characteristics of the coal mine microseisms at the target node were extracted. The extracted spatiotemporal characteristics of the coal mine microseisms were used as the input to the particle-swarm-optimization-improved support vector regression model, and the regression solution results of the coal mine microseism detection signals were output. The error penalty factor and kernel function parameters were improved, and the particle swarm optimization algorithm was introduced to optimize the detection results of microseisms in coal mines. The experimental results showed that this method can accurately and detect in real-time the microseisms in coal mines in the mining area, can effectively control the rate of missing detections in the detection process, and can ensure the stability of the overall detection operation. When the inertia weight was set at 0.9 and the number of particles was 45, this method had the highest sensitivity and the best-detection accuracy for microseisms in coal mines.