Analysis of spatio-temporal characteristics of mine rock burst microseismic monitoring data based on ST-DBSCAN clustering algorithm
Mine rock burst is a common geological disaster in underground mines,which poses a threat to the safe production of mines and the personal safety of employees.Conventional data analysis in mines often uses mathematical and statistical methods for hazard warning analysis,which has certain one sidedness and limitations.There is a significant lack of analysis on the spatiotemporal character-istics of high-dimensional microseismic monitoring data,and data analysis lacks real-time performance.Warning analysis relies too heav-ily on specific software.In order to improve the early warning capability of rock burst,a method based on the ST-DBSCAN spatio-tempo-ral clustering algorithm was proposed to analyze the spatio-temporal characteristics of the mine rock burst microseismic monitoring data,and the method showed higher robustness compared with the traditional clustering algorithm.Through the verification and analysis of real microseismic data in the mine,it was proved that the ST-DBSCAN spatio-temporal clustering algorithm can meet the requirements of an-alyzing the rock burst microseismic monitoring data in terms of time,space,and strength.It can identify and classify the stress concen-tration area in the process of mining back to the working face,and provide decision-making support for the early warning and manage-ment of the rock burst in the mine.
mine rock burstmicroseismic monitoring dataST-DBSCAN clustering algorithmspatio-temporal characteristicsearly warning
万方数据
维普
