AUTHOR=Qin Zixiu , Chen Xianjie , Yan Yuanfang , Liu Zonghui , Wang Zhengfu TITLE=A SVM-based method for identifying fracture modes of rock using WVD spectrogram features of AE signals JOURNAL=Frontiers in Earth Science VOLUME=11 YEAR=2023 URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2023.1206269 DOI=10.3389/feart.2023.1206269 ISSN=2296-6463 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.