CHIM-Net: A Combined Hierarchical Information Model for Predicting Time, Space and Intensity of Mining Microseismic Events

During the coal-mining process, high-energy mining microseismic events constrain the safety production of coal mines. To address the issues of low accuracy in predicting the time, space, and intensity (energy) of mining microseismic events, as well as insufficient feature extraction of mining microseismic monitoring data, the Combined Hierarchical Information Modeling (CHIM-Net) is proposed, integrating deep learning theory and technology. This model consists of a data decomposition module, a data partitioning module, and two prediction branch modules. The original mining microseismic monitoring data is decomposed and segmented, and then input into different prediction modules for training to obtain the trained model, ultimately obtaining the prediction results. To evaluate the model, mining microseismic monitoring data collected from coal mines in different provinces were used to train and test the model. Compared with several models such as Autoformer and Informer, the effectiveness of this model in the field of mining microseismic prediction is verified. The ablation experiment demonstrated the effectiveness of the data decomposition module in improving prediction performance. The results show that when at a prediction length of 48, the model achieves an average reduction of 18.92% in mean squared error and 10.57% in mean absolute error compared to other models, while at a prediction length of 96, it reduces by an average of 8.46% and 10.23%, respectively. Experimental results demonstrate that the proposed model performs well in predicting high-energy mining microseismic events, providing valuable insights for mining microseismic prediction and early warning.