Distributed fiber optic acoustic signal denoising and P-wave arrival picking method based on POA-LMD-LSTM

To address the common issues of low signal-to-noise ratio in distributed fiber optic acoustic signals and instability in P-wave first arrival picking using the Akaike Information Criterion (AIC) method, a new signal denoising and P-wave first arrival picking method has been proposed. This method leverages the Pelican Optimization Algorithm, Local Mean Decomposition, and Long Short-Term Memory network (POA-LMD-LSTM) to predict the signal, and an improved AIC method has been developed. Laboratory experimental tests indicate that the proposed method offers significant advantages over existing technologies in terms of improving the SNR of P-wave arrivals, picking stability, and accuracy. The signal-to-noise ratio of the denoised signal increases by an average factor of 1.15, and the average picking error of the P-wave arrival using the improved AIC method is only 1.0 sample point, which is significantly lower than that of the S/L-Skewness method.