Research on unsupervised sensing methods of typical coal and rock based on reflectance spectroscopy

In view of problem of poor recognition effect of existing supervised recognition methods of coal and rock based on reflectance spectroscopy when positions of coal and rock change, in order to study self-adaptive recognition of typical coal and rock based on reflectance spectroscopy, an unsupervised sensing methods of typical coal and rock based on reflectance spectroscopy and fuzzy C-means clustering (FCM) algorithms with improved clustering distances was proposed. Four typical types of coal and rock samples of Xinglongzhuang Coal Mine including gas coal, mudstone, siltstone and argillaceous limestone were studied and spectral reflectance curves of each sample were measured in near infrared band at multiple back reflection angles. The characteristic band with the most different spectral curves of the four types was analyzed and 2 150-2 400 nm were selected as the characteristic bands with the differences of the four types. In the characteristic band, the unsupervised recognition of reflectance spectra of coal and rock was studied for each coal-rock spectra combination of gas coal-mudstone, gas coal-siltstone and gas coal-argillaceous limestone. The results showed that with increasing of back reflection angle, back spectral reflectance of surfaces of all the four types increased first and then decreased. Meanwhile, the depth of absorption valleys of mudstone, siltstone and argillaceous limestone slightly decreased, and the decrease of the depth of absorption valleys of gas coal was relatively obvious. The improved FCM (RFCM, CFCM) methods were used to cluster spectral data quickly, and classifications of the spectral data were determined by membership probability matrix of the final clustering to recognize classifications of coal and rock at different positions. Comparing with FCM, the recognition rates of each coal-rock combination were both more than 90% using the two improved FCM methods. Among them, CFCM took the least number of iteration to cluster and recognize each coal-rock combination, and its total time consumptions were all less than 0.1 s. CFCM is the preferred method and provides a reference for the application of reflectance spectroscopy technology to the highly efficient and adaptive recognition of coal and rock at different positions of coal-rock interface.