Enhanced Arrival Time Picking for Acoustic Emission Signals Via 2D CNN and Waveform Transformation in Low-SNR Environments

Abstract

Accurately picking acoustic emission (AE) arrival times remains a significant challenge, particularly for low signal-to-noise ratio (SNR) signals where manual picking is subjective and unreliable. This article introduces an improved manual picking method for AE arrival times, developed by integrating sensor acquisition principles with wave velocity attenuation laws. This method provides a derivation formula that enables the determination of “ground truth” arrival times for low SNR signals by leveraging characteristics from high SNR signals. These derived values serve as labels to train a two-dimensional convolutional neural network (2D CNN) for automated arrival time picking. A key innovation is converting the one-dimensional AE signal directly into a two-dimensional matrix using a transformation matrix as the CNN’s input, thereby significantly streamlining preprocessing by eliminating the need for additional feature extraction. The labeled 2D matrices are then fed into the 2D CNN for training to enhance its ability to recognize crucial temporal patterns. Finally, the AIC algorithm picks the arrival times picked from the CNN-processed signals. A major advantage of CNNs in this context is that it does not require additional feature extraction and can extract features from the original elements. In addition, it can identify high-order statistics and nonlinear correlations of images. The third convolutional neuron can process data in its receptive domain or restricted subregion, reducing the need for a large number of neurons with large input sizes and enabling the network to be trained more deeply with fewer parameters. Results demonstrate that the proposed method significantly outperforms mainstream detection methods, including AIC and Floating Threshold (FT), achieving high accuracy and stability, particularly in scenarios with limited data and low SNR.