Abstract
Abstract. Automatic identification of minerals in images of polished section is highly demanded in exploratory geology as it can provide a significant reduction in time spent in the study of ores and eliminate the factor of misdiagnosis of minerals. The development of algorithms for automatic analysis of images of polished sections makes it possible to create of a universal tool for comparing ores from different deposits, which is also much in demand. The main contribution of this paper can be summed up in three parts: i) creation of LumenStone dataset (https://imaging.cs.msu.ru/en/research/geology/lumenstone) which unites high-quality geological images of different mineral associations and provides pixel-level semantic segmentation masks, ii) development of CNN-based neural network for automatic identification of minerals in images of polished sections, iii) implementation of software tool with graphical user interface that can be used by expert geologists to perform an automatic analysis of polished sections images.
Highlights
Practical mineragraphy is a time-consuming discipline that requires, on the one hand, a highly qualified specialist, and on the other, a large number of analyzes
It is worth noting that despite the amount of minerals analyzed by an expert geologist is much bigger, in this work we consider only 10 classes of S1 and S2 subsets of LumenStone, since collecting a large dataset of polished sections with manual annotation of all used minerals is unavailable and too resource intensive
The training of the model was performed on a personal computer with Intel Core i7-6700HQ, 16GB RAM and Nvidia GTX 960m with 2 GB of video memory
Summary
Practical mineragraphy is a time-consuming discipline that requires, on the one hand, a highly qualified specialist, and on the other, a large number of analyzes. Attempts to create software for diagnostics of ore minerals based on the results of analysis of micrographs have been undertaken for a long time. 2. using statistical principles for determining minerals in a specific sample (Berrezueta et al, 2016), (Kose et al, 2012). Both type of methods can deal with some problems of mineral identification, each of them has a number of significant disadvantages. The methods, that are based on statistical principles, can only work within a certain sample of minerals and require a new calibration taking into account chemical impurities in minerals for each new geological object. One of the most effective ways to overcome this shortcoming and to achieve the desired result is applying convolutional neural networks, as it is still possible to use single algorithm when working with different mineral associations
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