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JACIII Vol.18 No.3 pp. 347-352
doi: 10.20965/jaciii.2014.p0347
(2014)

Paper:

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Integration of Results from Recognition Algorithms Applied to the Uranium Deposits

Ravil I. Muhamediyev*1,*2, Yedilkhan Amirgaliyev*2,
Syrymbet Kh. Iskakov*1, Yan I. Kuchin*3,
and Elena Muhamediyeva*4

*1International IT University, 34A/8A, Manas str./Zhandosov str., Almaty 050040, Kazakhstan

*2Institute of Problems of Information and Control, Ministry of Education and Science of the Republic of Kazakhstan, 125 Pushkina str., Almaty 050010, Kazakhstan

*3LLC Geotechnoservice, Kazatomprom, 156-156a, Bogenbay-batyr str., Almaty 050020, Kazakhstan

*4Riga Technical University, 1 Kaļķu str., Riga LV-1658, Latvia

Received:
October 14, 2013
Accepted:
January 31, 2014
Published:
May 20, 2014
Creative Commons License
Keywords:
machine learning, artificial neural network, k-NN algorithm, uranium deposit, post-processing stage
Abstract
Data interpretation of electric logging can be performed using self-learning systems such as artificial neural networks (ANNs). Preliminary research shows that by using ANN we can achieve 52-73% of coincidence of interpretable data and experimental results. Therefore, it is necessary to analyze the possibility of using other classification algorithms, and that of using several classification algorithms simultaneously through a unified system (referred to as an integrator. These algorithms may improve the quality of recognition of individual species. The problem of developing a recognition system that combines several classification algorithms, also known as the integrator, is formulated here. A simple algorithm is developed for the learning and recognition of an integrator for the post processing stage; this enhances the recognition accuracy by 1-3%.
Cite this article as:
R. Muhamediyev, Y. Amirgaliyev, S. Iskakov, Y. Kuchin, and E. Muhamediyeva, “Integration of Results from Recognition Algorithms Applied to the Uranium Deposits,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.3, pp. 347-352, 2014.
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References
  1. [1] J. L. Baldwin, R. M. Bateman, and C. L. Wheatley, “Application of a neural network to the problem of mineral identification from well logs,” The Log Analyst, Vol.31, pp. 279-293, 1990.
  2. [2] B. Benaouda and G. Wadge, R. B. Whitmark, R. G. Rothwell, and C. MacLeod, “Inferring the lithology of borehole rocks by applying neural network classifiers to downhole logs – an example from the Ocean Drilling Program,” Geophysical J. Int. Vol.136, pp. 477-491, 1999.
  3. [3] M.M. Saggaf and E. L. Nebrija, “Estimation of missing logs by regularized neural networks,” AAPG Bulletin, Vol.87, No.8, pp. 1377-1389, 2003.
  4. [4] V. A. Tetenev, B. A. Yakimovich, M. A. Senilov, and N. B. Paklin, “Intellectual interpretation systems of geophysical research of boreholes,” Shtuchny Intellect, Vol.3, 2002.
  5. [5] K. Yelbig and S. Treitel, “Computational Neural Networks For Geophysical Data Processing,” M. M. Poulton (Ed.), p. 335, 2001.
  6. [6] M. Borsaru, B. Zhou, T. Aizawa, H. Karashima, and T. Hashimoto, “Automated lithology prediction from PGNAA and other geophysical logs,” Applied Radiation and Isotopes, Vol.64, pp. 272-282, 2006.
  7. [7] S. J. Rogers, H. C. Chen, D. C. Kopaska-Merkel, and J. H. Fang, “Predicting permeability from porosity using artificial neural networks,” AAPG Bulletin, Vol.79, pp. 1786-1797, 1995.
  8. [8] L. Kapur, L. Lake, K. Sepehrnoori, D. Herrick, and C. Kalkomey, “Facies prediction from core and log data using artificial neural network technology,” Trans. of the 39th Society of Professional Well Log Analysts Annual Logging Symposium, p. 11, 1998.
  9. [9] S. P. Aleshin and A. L. Lyahov, “Neural network estimation of mineral and raw material source of a region by the data of geophysical monitoring,” New Technology, Science journal of KUEITU, Vol.31, No.1, 2011.
  10. [10] A. N. Karpenko and O. V. Bulmasov, “Use of neuronet technology to interpretation data of well logging.”
    http://oil-gas.platinov-s.com/index.php?name=articles&op=view&id=11&pag=3&num=1 [Accessed August 28, 2013]
  11. [11] J. C. Raynal, A. Serge, A. M. Sagot et al., “Organization of field tests and evaluation of tricone bit performance using statistical analysis and sonic logs,” J. of Petroleum Technology, Vol.23, No.4, pp. 506-512, 1971.
  12. [12] S. J. Rogers, J. H. Fang, C. L. Karr, and D. A. Stanley, “Determination of lithology from well logs using a neural network,” AAPG Bulletin, Vol.76, No.5, pp. 731-739, 1992.
  13. [13] E. O. J. Bueno, I. C. Perez, G. Escamilla et al., “Applications of artificial neural networks and dipole sonic anisotropy in low-porosity, naturally fractured, complex lithology formations in the Southern Land Region ofMexico,” First Int. Oil Conf. and Exhibition inMexico, Cancun, Mexico, Aug. 31 to Sep. 2, SPE paper 103662, 2006.
  14. [14] D. Benaouda, G. Wadge, R. B. Whitmarsh et al., “Inferring the lithology of borehole rocks by applying neural network classifiers to downhole logs: An example from the Ocean Drilling Program,” Geophysical J. Int. Vol.136, No.2, pp. 477-491, 1999.
  15. [15] D. Kostikov, “Tools of well logging interpretation based on the converted logs using a multilayer neural network,” Dissertation of the candidate of technical sciences, Moscow: Russian State Library, p. 189, 2007.
  16. [16] Y. Kuchin, R. Muhamedyev, and L. Muhamedyeva, “Interpretation of log data of boreholes,” The 9th Int. Conf. Information Technologies and Management 2011, Information Systems Management Institute, Riga, Latvia, April 14-15, 2011.
  17. [17] Y. I. Kuchin, R. I. Muhamedyev, E. L. Muhamedyeva, P. Gricenko, Z. Nurushev, and K. Yakunin, “The analysis of the data of geophysical research of boreholes by means of artificial neural networks,” 5th Int. Conf. Innovative Information Technologies for Science, Business and Education (IIT-2012) Vilnius, May 10-12, 2012.
  18. [18] R. I.Muhamediyev, Y. Kuchin, and E.Muhamedyeva, “Recognition of Geological Rocks At the Bedded-infiltration Uranium Fields by Using Neural Networks,” 2012 IEEE Conf. on Open Systems, Kuala Lumpur, DOI: 10.1109/ICOS.2012.6417622, 2012.
  19. [19] S. A. Jashin, “Underground acidic in situ leaching of uranium at the Kazakhstan deposits,” Gorny Zurnal, Scientific-technical and industrial journal, No.3, 2008.
  20. [20] Y. Amirgalieyev, Y. Kuchin, S. Iskakov, R. Muhamedyev, and E. Muhamedyeva, “Evaluation of the quality of the neural network recognition of lithologic layers on uranium deposits,” Proc. of the scientific-practical Conf “Actual Problems of Informatics and Control,” Almaty: Institute of Problem Informatics and Control, pp. 262-270, 2014.
  21. [21] T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, “Introduction to algorithms, Second edition,” pp. 44-45, 2001.

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