Mine microseismic signal denoising method and application based on Adaboost_LSTM prediction

SHANG Xueyi; CHEN Yong; CHEN Jie; CHEN Linlin; PU Yuanyuan

doi:10.13225/j.cnki.jccs.2023.1228

Abstract：Microseismic early warning is of great significance for ensuring mine safety,where a good denoising and accur-ate P-wave arrival picking of a microseismic signal is fundamental to the reliability of microseismic monitoring.By ob-serving a large amount of microseismic signals,the noise segments of an individual microseismic signal were discovered to exhibit a good repeatability.Innovatively,a signal-denoising approach was proposed based on prediction data.Specific-ally,a microseismic signal prediction model was built that enhances the Long Short-Term Memory(LSTM)with the Ad-aptive Boosting(Adaboost)strategy.Then,a method for microseismic signal denoising based on the difference between model predictive data and observational data was developed.Furthermore,a method for P-wave arrival time picking was proposed,that combines the Short-Time Average/Long-Time Average(STA/LTA)ratio with the Akaike Information Cri-terion(AIC)(S/L-AIC method).Additionally,the noise reduction and P-wave arrival time picking performance was evalu-ated by the total cost function of P-wave arrival picking errors.Both the synthetic tests of noisy Ricker wavelet and the mi-croseismic data application of the Gengcun coal mine indicate that the Adaboost_LSTM model has excellent noise fitting capabilities but poor fitting for useful microseismic signals that haven't undergone neural network training.Furthermore,the signal prediction and noise reduction effects of the Adaboost_LSTM model surpass those of the LSTM model.The Adaboost_LSTM model effectively removes noise from microseismic signals,outperforming the wavelet-based low-fre-quency coefficient reconstruction methods and significantly enhancing the P-wave arrival characteristics.The wavelet and Adaboost_LSTM denoised signals can improve the P-wave arrival picking results of microseismic signals,and the Ada-boost_LSTM denoised signal shows a superior performance.The P-wave arrival picking using the S/L-AIC method is more effective than that using the STA/LTA and AIC methods alone,combining the stability of STA/LTA method with the accuracy of AIC method.Overall,the P-wave arrival picking error of the denoised signals using the S/L-AIC method remains generally within 10 ms.In conclusion,the microseismic signal denoising and P-wave arrival picking methods provide a significant support for mine microseismic monitoring and early warning.Furthermore,this approach has the po-tential for extending to the denoising and P-wave arrival time picking of natural earthquake signals.