Quantitative petrography: approaches and applications
Keywords:
digital petrography, multifocal petrography, fluorescence microscopy, image segmentation, quantitative analysis of rocks samplesAbstract
Quantitative petrography is a scientific and industrial direction of geology, which made huge progress due to developments and inventions in information technology and optics in the last decade. This article is introducing the modern and scientific directions of quantitative petrography and describes their current state of art as well as methodical approaches and their application. The research objects of quantitative macropetrography are hand specimens, borehole cores and polished tiles, and of micropetrography are thin and polished sections of rocks samples, splitted rock surfaces and immersion preparations. The goal of the research is to develop and present new methodological approaches of digital microscopy for the analysis of ores, rocks and minerals, as well as to investigate the morphological image analysis capabilities for the transforming from the classical description methods to quantitative petrography.
References
- Marschallinger R, Hofmann P. The application of object based image analysis to petrographic micrographs. In: Méndez-Vilas A, Díaz J, editors. Microscopy: science, technology, applications and education. Badajoz: Formatex Research Center; 2010. p. 1526–1532.
- Vasilionak AA, Samodurov VP. Mafic index of the igneous rocks definition by the digital petrography data. In: Makhnach AA, Astapenko VN, Demidova SV, Zui VI, Kruchek SA, Kutyrlo VE, et al., editors. Problemy geologii Belarusi i smezhnykh territorii. Materialy Mezhdunarodnoi nauchnoi konferentsii, posvyashchennoi 100-letiyu so dnya rozhdeniya akademika NAN Belarusi Aleksandra Semenovicha Makhnacha; 21–22 noyabrya 2018 g.; Minsk, Belarus’ [Problems of the geology of Belarus and adjacent territories. Proceedings of the International scientific conference, dedicated to the 100th anniversary of the academician of the National Academy of Sciences of Belarus Alexander Semenovich Makhnach; 2018 November 21–22; Minsk, Belarus]. Minsk: StroiMediaProekt; 2018. p. 285–288. Russian.
- Polat Ö, Polat A, Ekici T. Automatic classification of volcanic rocks from thin section images using transfer learning networks. Neural Computing and Applications. 2021;33(18):11531–11540. DOI: 10.1007/s00521-021-05849-3.
- Izadi H, Sadri J. Application of pattern recognition in mineral segmentation and identification. In: Proceedings of the International conference on pattern recognition and artificial intelligence; 2018 May 13–17; Montreal, Canada. Montreal: Centre for Pattern Recognition and Machine Intelligence, Concordia University; 2018. p. 433–438.
- Cheng Su, Sheng-jia Xu, Kong-yang Zhu, Xiao-can Zhang. Rock classification in petrographic thin section images based on concatenated convolutional neural networks. Earth Science Informatics. 2020;13(4):1477–1484. DOI: 10.1007/s12145-020-00505-1.
- Thompson S, Fueten F, Bockus D. Mineral identification using artificial neural networks and the rotating polarizer stage. Computers & Geosciences. 2001;27(9):1081–1089. DOI: 10.1016/S0098-3004(00)00153-9.
- Ślipek B, Młynarczuk M. Application of pattern recognition methods to automatic identification of microscopic images of rocks registered under different polarization and lighting conditions. Geology, Geophysics & Environment. 2013;39(4):373–384. DOI: 10.7494/geol.2013.39.4.373.
- Arganda-Carreras I, Kaynig V, Rueden C, Eliceiri K, Schindelin J, Cardona A, et al. Trainable Weka Segmentation: a machine learning tool for microscopy image segmentation. Bioinformatics. 2017;33(15):2424–2426. DOI: 10.1093/bioinformatics/btx180.
- Buono A, Fullmer S, King H, Sansone M, Lamberti B, Peterson K. Quantitative digital petrography: thin section to plug scale quantification of pore space, grains and connectivity. In: Mountjoy carbonate research conference. Carbonate pore systems: abstracts; 2017 June 25–29; Austin, Texas, USA. [S. l.]: [s. n.]; 2017. p. 12.
- Hinds OI, Duller RA, Walker RP, Wells BT, Worden RH. Enhanced two dimensional grain size analysis through the use of calibrated digital petrography. Search and Discovery [Internet]. 2014 [cited 2021 Jule 17]:41461. Available from: https://www.searchanddiscovery.com/pdfz/documents/2014/41461hinds/ndx_hinds.pdf.html.
- Poliakov A, Donskoi E. Automated relief-based discrimination of non-opaque minerals in optical image analysis. Minerals Engineering. 2014;55:111–124. DOI: 10.1016/j.mineng.2013.09.014.
- Donskoi E, Manuel JR, Hapugoda S, Poliakov A, Raynlyn T, Austin P, et al. Automated optical image analysis of goethitic iron ores. Mineral Processing and Extractive Metallurgy. 2020:1–11. DOI: 10.1080/25726641.2019.1706375.
- Marfunin AS. Spektroskopiya, lyuminestsentsiya i radiatsionnye tsentry v mineralakh [Spectroscopy, luminescence and radiation centers in minerals]. Moscow: Nedra; 1975. 327 p. Russian.
- Vasilionak AA, Samodurov VP. [Quantitative analysis of digital images of rocks]. In: Lukashev OV, San’ko AF, Zui VI, Tvoronovich-Sevruk DL, editors. Sovremennye problemy geokhimii, geologii i poiskov mestorozhdenii poleznykh iskopaemykh. Materialy Mezhdunarodnoi nauchnoi konferentsii, posvyashchennoi 110-letiyu so dnya rozhdeniya akademika Konstantina Ignat’evicha Lukasheva (1907–1987); 23–25 maya 2017 g.; Minsk, Belarus’. Chast’ 2 [Modern problems of geochemistry, geology and prospecting for mineral deposits. Materials of the International scientific conference, dedicated to the 110th anniversary of the birth of the academician Konstantin Ignatievich Lukashev (1907–1987); 2017 May 23–25; Minsk, Belarus. Part 2]. Minsk: Pravo i ekonomika; 2017. p. 4–6. Russian.
- Wirth MA. Lecture 10. Shape analysis and measurement [Internet]. In: Image processing algorithms and applications. Guelph: University of Guelph; 2004 [cited 2021 Jule 17]. Available from: http://www.cyto.purdue.edu/cdroms/micro2/content/education/wirth10.pdf.
Downloads
Additional Files
Published
Issue
Section
License
The authors who are published in this journal agree to the following:
- The authors retain copyright on the work and provide the journal with the right of first publication of the work on condition of license Creative Commons Attribution-NonCommercial. 4.0 International (CC BY-NC 4.0).
- The authors retain the right to enter into certain contractual agreements relating to the non-exclusive distribution of the published version of the work (e.g. post it on the institutional repository, publication in the book), with the reference to its original publication in this journal.
- The authors have the right to post their work on the Internet (e.g. on the institutional store or personal website) prior to and during the review process, conducted by the journal, as this may lead to a productive discussion and a large number of references to this work. (See The Effect of Open Access.)













