Rock fracture type recognition based on deep feature learning of microseismic signals

Accurate identification of rock fracture types is crucial for the prediction and early warning of coal mine rockburst hazards. Microseismic monitoring has been widely used for detecting rock fractures. However, conventional machine learning methods for microseismic signal analysis exhibited limited feature extraction capabilities and were highly susceptible to noise, leading to reduced classification accuracy and poor generalization performance. To address these limitations, this study proposed a novel rock fracture type recognition method based on deep feature learning of microseismic signals. In this study, microseismic signals corresponding to tensile and shear fractures were collected through Brazilian disc splitting and direct shear tests, respectively. These signals were then processed to construct a microseismic signal aggregation (MSA) spectrogram, which integrated time-frequency spectrograms, log-Mel spectrograms, and Mel-frequency cepstral coefficients. To enhance feature extraction efficiency, an improved DenseNet model (SE-MPDenseNet) was developed by incorporating multi-feature parallel dense blocks (MP-DenseBlock) and squeeze-and-excitation transition layers (SE-TransLayer). The extracted deep feature vectors were subsequently fed into an optimized LightGBM classifier (HBL-LightGBM), which was modified with a Hinge Loss function to improve classification performance. To evaluate the effectiveness of the proposed method, a true triaxial loading test was conducted to simulate rockburst hazards under realistic underground engineering conditions. Experimental results demonstrated that the proposed approach achieved a rock fracture type recognition accuracy of 92.12%, significantly outperforming conventional methods in both feature extraction capability and generalization ability. The findings indicate that the proposed method provides a robust and effective framework for microseismic-based rock fracture classification. It offers valuable insights for rockburst hazard monitoring and mitigation in mining and geotechnical engineering.