A novel tree-based algorithm for real-time prediction of rockburst risk using field microseismic monitoring

Abstract

Rockburst is a kind of complex and catastrophic dynamic geological disaster in the development and utilization of underground space, which seriously threatens the safety of personnel and environment. Due to the suddenness in time and randomness in space, the prediction of rockburst becomes a great challenge. Microseismic monitoring is capable to continuously capture rock microfracture signals in real time, which offers an effective means for rockburst prediction. With the explosive growth of monitoring data, the conventional manual forecasting methods are laborious and time-consuming. Therefore, artificial intelligence was introduced to improve the prediction efficiency. A novel tree-based algorithm was proposed. Its basic idea was to automatically recognize precursory microseismic sequences for the real-time prediction of rockburst intensity.The database consisting of 1500 microseismic events was analyzed. In order to establish precursory microseismic sequences, dimensionality reduction of the database was first implemented by t-SNE algorithm. Then, k-means clustering algorithm was employed for labelling 1500 microseismic events. Before that, canopy algorithm was adopted to determine the number of clusters. Finally, 300 precursory microseismic sequences were formed by grouping rule. They were further partitioned into two parts through stratified sampling: 70% for training and 30% for validation. The validation results indicated that the precursor tree with pruning achieved higher prediction accuracy of 98.9% than one without pruning on the validation set. And the increase was separately 12.2%, 9.2% and 28.6% on the whole validation set and each classes (low/moderate rockburst). In comparison with low rockburst, moderate rockburst was minority class. The improved accuracy on moderate rockburst suggested that pruning can enhance the recognition ability of precursor tree for minority class. Additionally, two extra rockburst cases were collected from a diversion tunnel in northwestern China, which provided a complete workflow about how to apply the built precursor tree model to achieve field rockburst warning in engineering practice. The tree-based algorithm served as a new and promising way for the real-time rockburst prediction, which successfully integrated field microseismic monitoring and artificial intelligence.

Mining Stress Distribution in Stope and Overlying Rock Fracture Characteristics and Its Disaster-Pregnant Mechanism of Coal Mine Earthquake

“…Te gestation process of coal mine earthquake was a growth process of stress, strain, and energy in the hypocenter area. Te coal mine earthquake occurred when the stress or energy in the hypocenter area reaches critical stress or energy of coal mine earthquake [45][46][47][48]. Te strain growth in the hypocenter area during gestation of coal mine earthquakes includes the elastic strain and inelastic strain [49].…”

Investigation of Microseismic Monitoring of and Precursor Information on Roof Collapse

“…In the elevation range of 450-530 m, MS events continue to gather, and the emergence of Cluster 5 indicates a strengthening connection between the mining area and the surface fractures. Variance analysis was used to assess the differences between different clusters [31][32][33][34][35]. Pairwise comparisons between different clusters were conducted using t-tests, and the results are presented in Table 2.…”

“…The p-value for the comparison between Cluster 2 and Cluster 6 is 0.2252, suggesting nonsignificance, likely because Clusters 2 and 6 overlap spatially Based on the field measurements, it can be concluded that the grouping of these two com ponents is reasonable. Variance analysis was used to assess the differences between different clusters [31][32][33][34][35]. Pairwise comparisons between different clusters were conducted using t-tests, and the results are presented in Table 2.…”

Rock pressure evaluation in coal face based on multi-factor decision-making theory

“…Adoko et al predicted rock pressure strength through the use of a fuzzy reasoning system, adaptive neurofuzzy reasoning system, and field measurement data [ 9 ]. Xin proposed a new tree-based algorithm that introduced artificial intelligence to improve the prediction ability, automatically identify precursor microseismic sequences, and forecast the rock pressure strength in real-time [ 10 ]. Bagaraja combined energy balance and induced stress to predict occurrences of rock bursts [ 11 ].…”