| Citation: | CAO Kai，LU Yuan，PANG Xiaolong，et al. Intelligent early warning method for subsidence deformation in goaf based on the frequency of microseismic events[J]. Bulletin of Geological Science and Technology，2025，44（4）：78-89 doi: 10.19509/j.cnki.dzkq.tb20240646
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The subsidence deformation caused by the "three underground" mining pose a threat to the safety of surface buildings such as transmission lines, and there is an urgent need for an early perception and intelligent warning method for subsidence and deformation in goaf areas.
This paper proposed an intelligent early warning framework for subsidence and deformation in goaf areas based on the frequency of microseismic events. This framework utilized a Distributed Acoustic Sensing (DAS) system to collect microseismic data, extracted microseismic events using the STA/LTA algorithm, and classified the microseismic events using a deep clustering method that combined AutoEncoder (AE) and Gaussian Mixture Models (GMM). Based on the correlation coefficient between microseismic event frequency and subsidence deformation data, microseismic events that induced subsidence deformation were selected. The VGG-16 deep learning model was then used to achieve intelligent recognition of such microseismic events, and real-time warning was carried out by setting warning thresholds.
This paper took a typical coal mine goaf in western China as the research area and applied the framework to field monitoring. The results show that the framework classifies the collected microseismic events in goaf into five categories, extracts one type of microseismic event that induces subsidence deformation, and combines with an intelligent microseismic event recognition model to successfully issue a warning for the sudden increase in tower inclination caused by subsidence deformation.
Therefore, this framework can effectively capture the correlation between microseismic events and subsidence and deformation, to achieve early warning of subsidence and deformation in goaf areas, and has practical feasibility and engineering application value.
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