Novel Method for Detecting Weak AE Signals Based on the Similarity of Time-Frequency Spectra

Abstract

As a key monitoring method, the acoustic emission (AE) technique has played a critical role in characterizing the fracturing process of laboratory rock mechanics experiments. However, this method is limited by low signal-to-noise ratio (SNR) because of a large amount of noise in the measurement and environment and inaccurate AE location. Furthermore, it is difficult to distinguish two or more hits because their arrival times are very close when AE signals are mixed with the strong background noise. Thus, we propose a new method for detecting weak AE signals using the mathematical morphology character correlation of the time-frequency spectrum. The character in all hits of an AE event can be extracted from time-frequency spectra based on the theory of mathematical morphology. Through synthetic and real data experiments, we determined that this method accurately identifies weak AE signals. Compared with conventional methods, the proposed approach can detect AE signals with a lower SNR.