微震监测预警对保障矿井安全具有重要意义，微震信号降噪和P波初至的准确拾取是微震监测结果可靠性的基础。通过观察海量微震信号，发现单个微震信号的噪声段具有良好的重复性，由此创新性提出基于预测数据的信号降噪思路。具体地，构建了基于自适应增强(Adaptive Boost-ing，Adaboost)策略提升长短期记忆网络(Long Short-Term Memory，LSTM)的微震信号预测模型，提出了基于模型预测数据与观测数据之差的微震信号降噪方法，研发了长短时窗均值比(Short-Time Average/Long-Time Average，STA/LTA)与赤池信息准则(Akaike Information Criterion，AIC)联合的P波初至拾取方法(S/L-AIC法)，并使用P波初至拾取误差评估和方法比较不同降噪信号和拾取效果。含噪Ricker子波理论测试和耿村煤矿微震数据应用均表明，Adaboost_LSTM模型对于噪声具有很好的拟合性，而对于未进行神经网络训练的微震有用信号拟合性较差，且Adaboost_LTSM模型的信号预测和降噪效果均优于LSTM模型的结果。基于Adaboost_LTSM模型的预测数据几乎能全部去掉微震信号噪声，其降噪效果显著优于小波低频系数重构结果，对非平稳信号的适应性明显增强。小波和Adaboost_LSTM降噪信号能明显提升微震信号P波初至拾取效果，且Adaboost_LSTM降噪信号的P波初至拾取效果更优。S/L-AIC法的P波初至拾取效果优于STA/LTA法和AIC法，兼具了STA/LTA法的稳定性和AIC法的准确性特点，降噪信号S/L-AIC法P波初至拾取误差整体在 10 ms以内。综上，矿山微震信号降噪和P波初至拾取方法能为矿山微震监测预警提供重要保障，可尝试推广至天然地震信号降噪和P波初至拾取。
Mine microseismic signal denoising method and application based on Adaboost_LSTM prediction
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.
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