Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

[1] ZAO NAK “Kazatomprom,” TOO IVT. Metodičeskie rekomendacii po kompleksu geofizičeskih metodov issledovanija skvažin pri podzemnom vyŝelačivanii urana. Almaty, 2003.Search in Google Scholar

[2] “World Uranium Mining Production,” 2017 [Online]. http://world-nuclear.org/information-library/nuclear-fuel-cycle/mining-of-uranium/world-uranium-mining-production.aspxSearch in Google Scholar

[3] V. N. Dahnov, Interpretacija rezultatov geofizičeskih issledovanij razrezov skvažin. M: Nedra, 448 s. tabl.4, il. 213, 1982.Search in Google Scholar

[4] Ja. I. Kučin, Sistema kompleksnoj interpretacii rezultatov geofizičeskih issledovanij skvažin na plastovo-infiltracionnyh mestoroždenijah urana. Vestnik Akademii Nauk, Almaty, 2008.Search in Google Scholar

[5] TOO GRK, Tehničeskaja instrukcija po provedeniju geofizičeskih issledovanij v skvažinah na plastovo infiltracionnyh mestoroždenijah urana. Almaty, 2010.Search in Google Scholar

[6] Š. A. Guberman, M. L. Izvekova, Ja. I. Xurgin, “Primenenie metodov raspoznavanija obrazov pri interpretacii geofizičeskih dannyh,” Sb. Samoobučajuŝiesja avtomatičeskie sistemy. M: Nauka, 1966.Search in Google Scholar

[7] G. N. Zverev, D. D. Halilov, I. B. Golovackaja. Primenenie algoritma raspoznavanija obrazcov dlja interpretacii promyslovo-geofizičeskih dannyh v Baškirii. Tr. MINHiGP, Vyp. 62. M.: Nedra, 1966.Search in Google Scholar

[8] P. Delfiner, O. Peyret, O. Serra, “Automatic determination of lithology from well logs,” SPE Formation Evaluation, vol. 2, no. 3, pp. 303–310, 1987. https://doi.org/10.2118/13290-pa10.2118/13290-PASearch in Google Scholar

[9] J. M. Busch, W. G. Fortney, L. N. Berry, “Determination of Lithology from Well Logs by Statistical Analysis,” SPE Formation Evaluation, vol. 2, no. 4, pp. 412–418, 1987. https://doi.org/10.2118/14301-pa10.2118/14301-PASearch in Google Scholar

[10] R. I. Muhamediev, E. L. Muhamedieva, Ja. I. Kučin, “Taksonomija metodov mašinnogo obučenija i ocenka kačestva klassifikacii I obučaemosti,” Cloud of Science, Vol. 2. No. 3, pp. 359–378, 2015.Search in Google Scholar

[11] T. Kohonen, “Self-organized formation of topologically correct feature maps,” Biological Cybernetics, vol. 43, no. 1, pp. 59–69, 1982. https://doi.org/10.1007/bf0033728810.1007/BF00337288Search in Google Scholar

[12] A. K. Jain, M. N. Murty, P. J. Flynn, “Data clustering: a review,” ACM Computing Surveys, vol. 31, no. 3, pp. 264–323, 1999. https://doi.org/10.1145/331499.33150410.1145/331499.331504Search in Google Scholar

[13] W. A. Barbakh, Y. Wu, C. Fyfe, “Review of Clustering Algorithms,” Studies in Computational Intelligence, vol. 249, pp. 7–28, 2009. https://doi.org/10.1007/978-3-642-04005-4_210.1007/978-3-642-04005-4_2Search in Google Scholar

[14] C. C. Fung, K. W. Wong, H. Eren and R. Charlebois, “Lithology classification using self-organizing map,” International Conference on Neural Networks, 27 November to 1 December, Perth, Western Australia, vol. 1, pp. 526–531, 1995. https://doi.org/10.1109/icnn.1995.48823310.1109/ICNN.1995.488233Search in Google Scholar

[15] M. Hassibi, I. Ershaghi, F. Aminzadeh, “Chapter 15 High resolution reservoir heterogeneity characterization using recognition technology,” Developments in Petroleum Science, vol. 51, pp. 289–307, 2003. https://doi.org/10.1016/s0376-7361(03)80019-510.1016/S0376-7361(03)80019-5Search in Google Scholar

[16] A. Torghabeh, R. Rezaee, R. Moussavi-Harami, B. Pradhan, M. Kamali, A. Kadkhodaie-Ilkhchi, “Electrofacies in gas shale from well log data via cluster analysis: A case study of the Perth Basin, Western Australia,” Open Geosciences, vol. 6, no. 3, pp. 393–402, Jan. 2014. https://doi.org/10.2478/s13533-012-0177-910.2478/s13533-012-0177-9Search in Google Scholar

[17] A. Toumani, “Fuzzy Classification for Lithology Determination from Well Logs,” Modern Approaches in Geophysics, vol. 21, pp. 125–142, 2003. https://doi.org/10.1007/978-94-017-0271-3_910.1007/978-94-017-0271-3_9Search in Google Scholar

[18] A. N. Corina, Automatic Lithology Prediction from Well Logging Using Kernel Density Estimation. Norwegian University of Science and Technology, 2016.Search in Google Scholar

[19] R. Gelfort, “On Classification of Logging Data,” Dissertation, 2005.Search in Google Scholar

[20] E. Howat, S. Mishra, J. Schuetter, B. Grove and A. Haagsma, “Identification of Vuggy Zones in Carbonate Reservoirs from Wireline Logs Using Machine Learning Techniques,” American Association of Petroleum Geologists Eastern Regional Meeting, 2015.Search in Google Scholar

[21] B. V. Dasarathy, Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, 1991.Search in Google Scholar

[22] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, 2000.10.1017/CBO9780511801389Search in Google Scholar

[23] J. L. Baldwin, R. M. Bateman, C. L. Wheatley, “Application of a neural network to the problem of mineral identification from well logs,” The Log Analyst, vol. 31, no. 5, pp. 279–293, 1990.Search in Google Scholar

[24] L. Aliouane, S.-A. Ouadfeul and A. Boudella, “Well-Logs Data Processing Using the Fractal Analysis and Neural Network,” Fractal Analysis and Chaos in Geosciences, 2012. https://doi.org/10.5772/5187510.5772/51875Search in Google Scholar

[25] M. A. Senilov, “Matematičeskie modeli i programmno-apparatnye sredstva intellektual’nyh sistem dlja interpretacii geofizičeskih issledovanij skvažin,” Dissertacija na soiskanie zvanija doktora tehničeskih nauk, 2005.Search in Google Scholar

[26] I. M. Jasoveev, “Intellektualnaja sistema programmnogo i informacionnogo obespečenija processov kontrolja i obrabotki karotažnyh dannyh i ih interpretacii,” Dissertacija na soiskanie zvanija kandidata tehničeskih nauk, 2006.Search in Google Scholar

[27] P. Tahmasebi, A. Hezarkhani, “A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation,” Computers & Geosciences, vol. 42, pp. 18–27, 2012. https://doi.org/10.1016/j.cageo.2012.02.00410.1016/j.cageo.2012.02.004426858825540468Search in Google Scholar

[28] D. V. Kostikov, “Instrumentalnye sredstva interpretacii geofizičeskih issledovanij skvažin na osnove preobrazovannyh karotažnyh diagramm s pomoŝju mnogoslojnoj nejronnoj seti,” Dissertacija na soiskanie zvanija kandidata tehničeskih nauk, 2007.Search in Google Scholar

[29] Y. Bengio, “Learning Deep Architectures for AI,” Foundations and Trends® in Machine Learning, vol. 2, no. 1, pp. 1–127, 2009. https://doi.org/10.1561/220000000610.1561/2200000006Search in Google Scholar

[30] “Deep Neural Networks,” 2017 [Online]. Available: https://www.slideshare.net/Technosphere1/lecture-12-47107587Search in Google Scholar

[31] “Convolutional Neural Networks,” 2017 [Online]. Available: http://cs231n.github.io/convolutional-networks/#addSearch in Google Scholar

[32] “Deep Belief Nets,” 2017 [Online]. Available: https://www.cs.toronto.edu/~hinton/nipstutorial/nipstut3.pdfSearch in Google Scholar

[33] “Recurrent Networks,” 2017 [Online]. Available: http://www.intuit.ru/studies/courses/61/61/lecture/20456Search in Google Scholar