Classification of mineral components of granitoid rocks by using methods of digital petrography and machine learning

Machine learning methods have begun to be used in petrography relatively recently. However, thanks to the rapid programming development, more powerful algorithms and tools appear, the use of which to solve petrographic tasks hasn’t yet been considered. That’s why the purpose of this work was to use modern machine learning methods to identify mineral components from macro images of rock samples, as well as to use digital image processing methods. This article presents the method of determination of quantitative characteristics and the method of classification of minerals on macro images of rocks. An open source program for analyzing and processing images ImageJ, and its plugin Trainable Weka Segmentation were used as a toolkit. Macro images are obtained by scanning polished granite samples. Seven macro images of various representatives of the granites were selected for the experiment. Training with a teacher was conducted, where the decision tree method was used for classification. Based on this data set, classes were created for each of the rock-forming minerals: quartz (Q), potassium feldspar (Fps), plagioclase (Pl) and biotite (Bi). Regions of interest were prepared and stored in one database on the basis of which the classifier was trained. Based on the obtained classification data, masks of each mineral were created. A quantitative analysis was performed based on these masks: the percentage content and amount of grains of each mineral were determined. Results are presented in tabular and graphical forms.