gscienceMining Science and Technology (Russia)Горные науки и технологии2500-0632The National University of Science and Technology MISiIS (NUST MISIS)10.17073/2500-0632-2025-05-416gscience-974Research ArticleGEOLOGY OF MINERAL DEPOSITSГЕОЛОГИЯ МЕСТОРОЖДЕНИЙ ПОЛЕЗНЫХ ИСКОПАЕМЫХFrom visual diagnostics to deep learning: automatic mineral identification in polished section imagesОт визуальной диагностики к глубокому обучению: автоматическое определение минералов на фотографиях аншлифовhttps://orcid.org/0000-0002-8500-7193КоршуновД. М.KorshunovD. M.

Дмитрий Михайлович Коршунов – кандидат геолого-минералогических наук, старший научный сотрудник

г. Москва

Dmitrii M. Korshunov – Cand. Sci. (Geol. and Miner.), Senior Researcher

Moscow

dmit0korsh@gmail.comhttps://orcid.org/0000-0002-4217-7141ХвостиковА. В.KhvostikovA. V.

Александр Владимирович Хвостиков – кандидат физико-математических наук, научный сотрудник лаборатории математических методов обработки изображений факультета вычислительной математики и кибернетики

г. Москва

 Scopus ID 57188856261

Alexander V. Khvostikov – Cand. Sci. (Phys. and Math.), Senior Researcher of the Laboratory of Mathematical Methods for Image Processing, Faculty of Computational Mathematics and Cybernetics (CMC)

Moscow

Scopus ID 57188856261

khvostikov@cs.msu.ruhttps://orcid.org/0009-0003-0814-3997НиколаевГ. В.NikolaevG. V.

Глеб Витальевич Николаев – студент магистратуры факультета вычислительной математики и кибернетики

г. Москва

Gleb V. Nikolaev – M.Sc. Student, Faculty of Computational Mathematics and Cybernetics (CMC)

Moscow

nickolaev.gleb03@gmail.comhttps://orcid.org/0000-0003-3299-2545СорокинД. В.SorokinD. V.

Дмитрий Васильевич Сорокин – кандидат физико-математических наук, старший научный сотрудник лаборатории математических методов обработки изображений факультета вычислительной математики и кибернетики

г. Москва

Dmitry V. Sorokin – Cand. Sci. (Phys. and Math.), Senior Researcher of the Laboratory of Mathematical Methods for Image Processing, Faculty of Computational Mathematics and Cybernetics

Moscow

dsorokin@cs.msu.ruhttps://orcid.org/0009-0007-0936-4088ИндычкоО. И.IndychkoO. I.

Олеся Игоревна Индычко – аспирант факультета вычислительной математики и кибернетики

г. Москва

Olesya I. Indychko – PhD-Student, Faculty of Computational Mathematics and Cybernetics (CMC)

Moscow

olesyaindychko@gmail.comhttps://orcid.org/0000-0003-0133-7185БогуславскийМ. А.BoguslavskiiM. A.

Михаил Александрович Богуславский – кандидат геолого-минералогических наук, доцент кафедры геологии, геохимии и экономики полезных ископаемых геологического факультета

г. Москва

ResearcherID V-4671-2017

Mikhail A. Boguslavskii – Cand. Sci. (Geol. and Miner.), Associate Professor of the Department of Geology, Geochemistry and Mineral Economics, Faculty of Geology

Moscow

ResearcherID V-4671-2017

mboguslavskiy@yandex.ruhttps://orcid.org/0000-0001-9910-4501КрыловА. С.KrylovA. S.

Андрей Серджевич Крылов – доктор физико-математических наук, профессор, заведующий лабораторией математических методов обработки изображений факультета факультета вычислительной математики и кибернетики

г. Москва

Scopus ID 7202280261

ResearcherID B-9651-2014

Andrey S. Krylov – Dr. Sci. (Phys. and Math.), Professor, Head of the Laboratory of Mathematical Methods for Image Processing, Faculty of Computational Mathematics and Cybernetics

Moscow

Scopus ID 7202280261

ResearcherID B-9651-2014

kryl@cs.msu.ruГеологический институт РАНРоссияGeological Institute of the Russian Academy of Sciences (GIN RAS)Russian FederationМосковский государственный университет имени М. В. ЛомоносоваРоссияLomonosov Moscow State UniversityRussian Federation202513102025103232244Copyright © Korshunov D.M., Khvostikov A.V., Nikolaev G.V., Sorokin D.V., Indychko O.I., Boguslavskii M.A., Krylov A.S., 20252025Коршунов Д.М., Хвостиков А.В., Николаев Г.В., Сорокин Д.В., Индычко О.И., Богуславский М.А., Крылов А.С.Korshunov D.M., Khvostikov A.V., Nikolaev G.V., Sorokin D.V., Indychko O.I., Boguslavskii M.A., Krylov A.S.This work is licensed under a Creative Commons Attribution 4.0 License.https://mst.misis.ru/jour/article/view/974

Studying mineralogical composition of ores is a fundamental step in the exploration of new deposits, as it allows determining the forms in which useful components are found, the processes of ore formation, and the potential recoverability of valuable elements. The mineral associations, textures, and structures of ores not only provide key information about the geology of a deposit, but also determine the choice of beneficiation methods. Despite the development of modern analytical tools and existing solutions for automatic mineral diagnosis, such as those based on the SEM-EDS method, optical microscopy remains the most accessible means of quantitative mineralogical analysis. However, it remains labor-intensive and requires highly skilled specialists. In addition, its visual nature limits the accuracy and reproducibility of results, creating a need for more effective approaches. One promising area is the automation of ore mineral identification based on images of polished sections. The aim of the work was to develop and validate a universal segmentation model based on deep learning. In the course of the research, related problems were also solved, including the creation of an open LumenStone dataset, the development of color adaptation methods, joint analysis of PPL and XPL images, panorama construction, and the development of a fast annotation method. The work applied convolutional neural network architectures, color correction and joint image processing algorithms, as well as an original sampling method that compensates for class imbalance. The proposed segmentation model demonstrated high accuracy (IoU up to 0.88, PA up to 0.96) for nine minerals. The obtained results confirmed the effectiveness of integrating deep learning and modern image processing algorithms in mineralogical analysis systems and laid the foundation for further development of digital methods in automated petrography.

Изучение минерального состава руд является основополагающим этапом при разведке новых месторождений, поскольку именно оно позволяет определить формы нахождения полезных компонентов, процессы рудообразования и потенциальную извлекаемость ценных элементов. Минеральная ассоциация, текстуры и структуры руд не только дают ключевые сведения о геологии месторождения, но и определяют выбор методов обогащения. Несмотря на развитие современной аналитической базы и существующие решения автоматической диагностики минералов, например, на основе СЭМ-EDS метода, оптическая микроскопия является самым доступным средством количественного минералогического анализа. Однако она остаётся трудоемкой и требует высокой квалификации специалиста. А её визуальный характер ограничивает точность и воспроизводимость результатов, что создает необходимость в разработке более эффективных подходов. Одним из перспективных направлений является автоматизация идентификации рудных минералов по фотоизображениям аншлифов. Целью работы являлась разработка и валидация универсальной сегментационной модели на основе глубокого обучения. В процессе исследования также были решены сопутствующие задачи, включая формирование открытого набора данных LumenStone, разработку методов цветовой адаптации, совместного анализа PPL- и XPL-изображений, построения панорам и разработки метода быстрой разметки. В работе были применены свёрточные нейросетевые архитектуры, алгоритмы коррекции цвета и совместной обработки изображений, а также оригинальный метод семплирования, компенсирующий дисбаланс классов. Предложенная модель сегментации продемонстрировала высокую точность (IoU до 0,88, PA до 0,96) по девяти минералам. Полученные результаты подтвердили эффективность интеграции глубокого обучения и современных алгоритмов обработки изображений для задач минералогического анализа и заложили основу для дальнейшего развития цифровых методов в автоматизированной петрографии.

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The authors declare that there are no conflicts of interest present.