GFFMNet: Prediction Method for Coal Mine Seismic Focal Mechanisms Based on GCN

As the depth of coal mining increases, seismic events pose a serious threat to mine safety, and studying the rupture mechanism helps to better understand the occurrence of mine-induced earthquakes. In this paper, we propose a new model, Graph-Fully Convolutional Focal Mechanism Network (GFFMNet), based on Graph Convolutional Networks (GCN) and Fully Convolutional Networks (FCN), for predicting the focal mechanisms of mine-induced seismic events. The model combines the spatial feature aggregation capabilities of GCN with the feature extraction abilities of FCN. Using monitoring data from the Dongtan Coal Mine in Shandong, China, we generated 210,000 samples and trained the model by minimizing the mean squared error (MSE) loss function. Experimental results show that GFFMNet significantly improves inference speed while maintaining prediction accuracy, with an average prediction time of only 0.12 s per event and strong generalization capabilities with low error. Compared to the P-wave first-arrival polarity inversion method, GFFMNet demonstrates advantages in both information utilization and accuracy, with potential applications in real-time seismic analysis. Future research will focus on further optimizing the model to enhance prediction reliability.