FDNet: Knowledge and Data Fusion-Driven Deep Neural Network for Coal Burst Prediction.

Coal burst prediction is an important research hotspot in coal mine production safety. This paper presents FDNet, which is a knowledge and data fusion-driven deep neural network for coal burst prediction. The main idea of FDNet is to extract explicit features based on the existing mine seismic physical model and utilize deep learning to automatically extract the implicit features of mine microseismic data. The key innovations of FDNet include an expert knowledge indicator selection method based on a subset search strategy, a mine microseismic data extraction method based on a deep convolutional neural network, and a feature deep fusion method of mine microseismic data based on an attention mechanism. We conducted a set of engineering experiments in Gaojiapu Coal Mine to evaluate the performance of FDNet. The results show that compared with the state-of-the-art data-driven machines and knowledge-driven methods, the prediction accuracy of FDNet is improved by 5% and 16%, respectively.

Evolution Characteristics of Seismic Detection Probability in Underground Mines and Its Application for Assessing Seismic Risks-A Case Study.

Seismic hazards are typical mining hazards causing dynamic failure of coal and rock mass, which greatly threatens the safety of personnel and equipment. At present, various seismic analysis methods are used to assess seismic risks but their accuracy is significantly limited by the incompleteness of seismic data. The probability of detecting earthquakes (PDE) method has been proven as a powerful means for retrieving missed seismic events and enhancing the seismic data integrity in mines. However, to date, the reliability of the results of the PDE method has not been assessed and the highly integrated seismic data have not been linked with the actual hazard potential. To fill these gaps, this paper investigated the impacts of the seismic data volume used for calculation and the modification of the layout of sensors on the reliability and robustness of the PDE method. The event counts and seismic energy were compensated using the PDE method, correlated with strong seismic events. The results indicated that the compensated seismic data presented higher accuracy in locating future hazardous events than before. This research provides references on enhancing the performance of seismic analysing methods for seismic risk assessments.

Focal Mechanism and Source Parameters Analysis of Mining-Induced Earthquakes Based on Relative Moment Tensor Inversion.

Mining-induced earthquakes (MIEs) in underground coal mines have been a common phenomenon that easily triggers rock bursts, but the mechanism is not understood clearly. This research investigates the laws of focal mechanism and source parameters based on focal mechanism and source parameters analysis of MIEs in three frequent rock burst areas. The relative moment tensor inversion (MTI) method was introduced, and the way to construct the inversion matrix was modified. The minimum ray and source number conditions were calculated, and an optimized identification criterion for source rupture type was proposed. Results show that the geological structure, stress environment, and source horizon influence the focal mechanism. The tensile type sources can distribute in the roof and coal seam, while the shear types are primarily located in the coal seam. In the typical fold structure area, the difference in source rupture strength and stress adjustment between tensile and shear types is negligible, while the disturbance scale of tensile types is distinct. The shear types have higher apparent volume and seismic moment in the deep buried fault area but lower source energy. The apparent stress of the tensile types is higher than that of the shear types, representing that the stress concentration still exists in the roof after the MIEs, but the stress near the faults could be effectively released. In the high-stress roadway pillar area, the primary fracture of the coal pillar easily produces a continuous shear rupture along the dominant stress direction under the extrusion of the roof and floor. The source parameters (except apparent stress) of shear types are higher than tensile types and have higher dynamic risk. The results contribute to expanding the understanding of rock burst mechanisms and guide MIEs' prevention.