An Improved Onset Time Picking Method for Low SNR Acoustic Emission Signals

Automatic and accurate onset time picking of the acoustic emission (AE) signals is of great significance for the location of acoustic events. However, the onset times are difficult to be picked accurately due to the presence of noise in signal records. To overcome this problem, an improved method including three consecutive processes is proposed in this study. First, the windowing Lempel-Ziv (WLZ) complexity method is applied to determine an approximate onset time. Then, the time series selected from the start time to the vicinity of the approximate onset time is coarsened at different scales with the application of multi-scale (MS) theory. Finally, Akaike information criterion (AIC) method is utilized to pick up the accurate onset time. Feasibility and performance of the proposed improved method are tested and compared with the other automatic onset time picking methods, such as AIC, short and long time average ratio (STA/LTA), modified energy ratio (MER), AIC with low pass filter and STA/LTA with low pass filter, using the same broken load experiment and 100 rock fracture signals with various signal-to-noise ratios (SNRs). The results show that the accuracy of automatic picks provided by the improved method are far beyond the other methods mentioned. Meanwhile, both the accuracy stability and SNR adaptability of the improved method for rock fracture signals are better than other methods, which proves that the improved method has the excellent performance to pick up onset times of AE signals.