Improved first arrival picking of microseismic P-waves in coal mines using multi-denoising and adaptive characteristic functions

Microseismic monitoring is a critical technology for mine safety monitoring, but existing microseismic picking methods exhibit instability and limitations when dealing with high-noise data in coal mine environments. This paper proposes a method for improving the picking of high-noise microseismic P-wave first arrivals, called IWTSE-MSDACF-AIC. The method first uses the improved complete ensemble empirical mode decomposition with adaptive noise to decompose the microseismic signal into a series of intrinsic mode functions (IMFs). Then, the sample entropy of the IMFs is calculated, and an appropriate threshold is set to perform wavelet denoising on the IMFs. The signals are then reconstructed to distinguish noise from useful signals. Finally, the denoised signal’s P-wave first arrival is automatically determined using the proposed picking method based on the moving standard deviation-adaptive characteristic function and Akaike information criterion, which incorporates the relative energy coefficient and relative energy time series. Tests using synthetic seismic records with different signal-to-noise ratios and validation on real coal mine seismic datasets show that the proposed denoising strategy and picking method achieve high accuracy and robustness. In practical data tests, 90.01% of data errors fell within the range of 0s to 0.06s, demonstrating excellent picking performance. Furthermore, in grid search localization using five calibration blasts at the Dongtan coal mine, the localization results based on the proposed method significantly outperformed those based on traditional methods and PhaseNet.