Research On Detection And Early Warning Method Of Coal Mine Water Disaster Based On Microseismic Monitoring And Electromagnetic Detection Information

Coal is an important energy source in production and life.In the process of coal mine production,water disaster is a common disaster accident.Once it occurs,it may cause heavy casualties and serious environmental damage.What’s more,it can lead to the abandonment of the mine,resulting in huge economic and social impacts.This paper conducts in-depth research on the factors that induce water disaster accidents,and its purpose is to reduce the occurrence probability of mine water disaster accidents and improve the ability to predict and prevent water disaster accidents.Aiming at the research goal of coal mine flood accident prediction and prevention,the research on coal mine production monitoring technology combining microseismic and electromagnetic is carried out,focusing on the automatic identification method of multi-scale morphological coal mine microseismic effective signal,the extraction of microseismic signal attribute feature domain and the Clustering and full connection—Intelligent classification of microseismic events based on long short-term memory neural network An early warning method of coal mine water disaster accident is proposed.In view of the characteristics of weak energy,large environmental noise and low signal-to-noise ratio(SNR)of microseismic effective signals,the multiscale top-hat transformation of mathematical morphology is applied to automatic identification of microseismic effective signals in this paper,which realizes the rapid and accurate extraction and identification of microseismic effective signals under the condition of low SNR and provides effective signals with high SNR for subsequent processing and interpretation.According to the difference of the attributes of the effective signals generated by layer fracture,tectonic activation and groundwater movement and the noise signals generated by mechanical vibration and power frequency noise interference,which are included in the microseismic events recorded by geophones,the Mahalanobis distance between different waveform events is taken as the characterization index of similarity distance,and the minimum distance is taken as the judgment standard,so that the agglomerative hierarchical clustering analysis method is realized,and the unsupervised classification of the microseismic signals is completed in the attribute feature domain.In order to identify the microseismic events related to water disasters,an improved FCNLSTM(Fully Connected Long and Short-Term Memory Network)is designed.Combining with the time series data enhancement technique,the FCN-LSTM can solve the problem of intelligent classification of microseismic events under the condition of limited data and uneven distribution and improve the accuracy of microseismic event interpretation.Considering the close relationship between underground water-rich areas and lowresistivity anomalies,this paper integrates microseismic fracture imaging technology with electromagnetic detection of low-resistivity anomalies distribution and develops a coal mine flood accident early warning that integrates fracture interpretation technology and electromagnetic detection information.Method,use electromagnetic detection information to draw out the spatial distribution of low-resistance abnormal bodies,analyze the causes of abnormal bodies to predict the distribution of water sources,and realize early warning of coal mine water hazards.