An optimized identification method of coal-bearing stratum lithology

In view of difficulties in obtaining stratum information parameters and low accuracy of lithology identification in existing lithology identification method of coal-bearing stratum in coal mine underground, an optimized identification method of coal-bearing stratum lithology based on principal component analysis (PCA) algorithm and kernel fuzzy C-means clustering (KFCM) algorithm was proposed. A high-dimensional drilling parameters set was constructed by using drilling test rig to obtain six kinds of drilling sensitive parameters, such as penetration rate, rotary torque, drilling pressure, rotational speed, rotary pressure and mud pump flow rate, which was taken as identification data sources, including training samples and test samples. Combining feature extraction advantage of PCA algorithm and good clustering effect of KFCM algorithm, a lithology identification model based on PCA-KFCM algorithm was established. The PCA algorithm was used to extract features of the training samples and reduce the dimension of the data to obtain eigenvalues and eigenvectors of the training samples. KFCM algorithm was used to conduct fuzzy core clustering on principal component data sets of training samples, and the test rock samples were divided into several types. The criterion was established by the Mahalanobis distance method, and the formation lithology of the test samples was identified by the minimum Mahalanobis distance. The test results show that the optimized identification method of coal-bearing stratum lithology based on PCA-KFCM algorithm can effectively identify formation lithology, and the identification accuracy is improved by 23.2% compared with the conventional KFCM algorithm.