Abstract: Microseismic early warning is of great significance for ensuring mine safety, where a good denoising and accurate P-wave arrival picking of a microseismic signal is fundamental to the reliability of microseismic monitoring. By observing 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. Specifically, a microseismic signal prediction model was built that enhances the Long Short-Term Memory (LSTM) with the Adaptive 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 Criterion (AIC) (S/L-AIC method). Additionally, the noise reduction and P-wave arrival time picking performance was evaluated by the total cost function of P-wave arrival picking errors. Both the synthetic tests of noisy Ricker wavelet and the microseismic 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-frequency 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 Adaboost_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 potential for extending to the denoising and P-wave arrival time picking of natural earthquake signals.