Arrival Picking of Acoustic Emission Signals Using a Hybrid Algorithm Based on AIC and Histogram Distance

Automatic picking of the arrival of acoustic emission (AE) and microseismic signals is very important in geophysics and process monitoring. However, because an acoustic signal undergoes many reflections and refractions before being received by an AE sensor, its signal-to-noise ratio (SNR) varies and, in some cases, can he very low. A new hybrid method for arrival-time picking based on the Akaike information criterion (AIC) picker and Manhattan distance is presented. A criterion and a standard framework for determining an arrival point are formulated, and two key parameters are discussed, namely, the length of the time window and the size of the selected time series. Experimental results show that the proposed method can detect the arrival points of signals with various SNRs while overcoming some of the drawbacks of the conventional AIC method. It could provide a better alternative for determining the arrival points of AE signals in process monitoring and other fields.