Classification of rock fracture acoustic emission signals based on improved EFD-wavelet denoising algorithm

Abstract In order to accurately identify the acoustic emission signals generated in different stages of rock fracture process, this paper proposed an algorithm based on improved empirical Fourier decomposition (EFD) combined with wavelet denoising to preprocess the collected acoustic emission signals for feature extraction and classification using Learning vector quantization (LVQ) algorithm. Firstly, the improved EFD algorithm was used to decompose the acoustic emission signals of rock core fracture, with variance contribution rate set as the screening criterion, and then the wavelet threshold denoising method was used to further filter out noise and reconstruct the signals. Then, Gaussian mixture model was used to obtain the probability distribution of feature vectors, and the different stages of fracture process were analyzed. Finally, the parameters of acoustic emission signals were extracted to construct feature vectors for classification and recognition of rock core fracture acoustic emission signals by LVQ algorithm. According to the experimental results, this method could accurately identify different stages of rock core fracture based on acoustic emission signals.