基于微震时空流图的冲击地压危险区域预测方法

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中图分类号：TD324 文献标志码：A

Abstract：The spatiotemporal distribution characteristics of microseismic events are closely related to the intensity ofrockbursts incoal mines.Prediction methods and visualrepresentations of microseismic databased on machine learning are important technical means for monitoring and early warning of rockbursts.However， challenges remain in effectively predicting the movement pathsof hazard areas，visualizing spatiotemporal paterns of microseismic data inacoordinated manner，andclearly displaying stackeddata.Toaddress this issue，a prediction method for rockburst hazard areas based on a microseismic spatiotemporal flow map was proposed.In thedata preprocessing moduleof this method，atwo-dimensional kernel density estimationmethod was used to continuouslyrepresent discretemicroseismic data，anda kernel density heat map wasconstructed toreflect the spatial aggregation degree of microseismic events.In thespatiotemporal flow map construction module，the gravity model was improved to extractthe spatiotemporal features of microseismic data，and arrows were used to visualize the direction of the spatiotemporal flow. In the hazard area prediction module，the K-means clustering algorithm was used tooptimizethe visualizationresults.Microseismic data from the2215 working face of a mine in Inner Mongolia and the 41O6 working face of a mine in Shaanxi were used to conduct rockburst hazard prediction experiments separately for fault zones and goaf areas.The results showed that this method could effectively and accurately predict the movement direction of rockburst hazard areas. After applying the K-means clustering algorithm， the number of arows in the microseismic spatiotemporal flow maps was optimized by 77.27% and 87.5% ， respectively， making the visualizations more concise and intuitive.

Key words： coal mine rockburst; microseismic monitoring; hazard area prediction; spatiotemporal coordinated visualization; spatiotemporal flow map

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