A SVM-based method for identifying fracture modes of rock using WVD spectrogram features of AE signals

Abstract

In order to achieve the highly efficient and accurate identification of fracture modes including tension or shear fractures during rock failure, an intelligent identification method based on Wigner-Ville distribution (WVD) spectrogram features of acoustic emission (AE) signals was proposed. This method was mainly constructed by the following steps: Firstly, AE hits corre-sponding to tension and shear fractures were obtained through conducting the Brazilian disc test (tension fracture) and direct shear test (shear fracture) of limestone. Secondly, the WVD spectro-grams of these tensile-type and shear-type AE hits were respectively extracted and then trans-formed into the image features of relatively low-dimension as the sample set based on the gray-level cooccurrence matrix (GLCM) and histogram of oriented gradient (HOG). Finally, on the basis of the processed and classified sample set of the WVD spectrogram features, an identifica-tion model of rock fracture modes was established by a support vector machine (SVM) learning algorithm. To verify this method, the fracture modes of limestone subjected to biaxial compres-sion were identified by the method. The results showed that the method not only can greatly re-veal the fracture modes change from tension-dominated to shear-dominated fractures, but also has advantages over the RA-AF value method, such as applicability, accuracy and practicality.