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2015 | 25 | 4 | 955-960
Tytuł artykułu

Neural networks as a tool for georadar data processing

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this article a new neural network based method for automatic classification of ground penetrating radar (GPR) traces is proposed. The presented approach is based on a new representation of GPR signals by polynomials approximation. The coefficients of the polynomial (the feature vector) are neural network inputs for automatic classification of a special kind of geologic structure-a sinkhole. The analysis and results show that the classifier can effectively distinguish sinkholes from other geologic structures.
Rocznik
Tom
25
Numer
4
Strony
955-960
Opis fizyczny
Daty
wydano
2015
otrzymano
2014-07-07
poprawiono
2014-10-14
Twórcy
  • Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Kraków, Poland
  • Department of Geophysics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Kraków, Poland
  • Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Kraków, Poland
Bibliografia
  • Marcak, H., Gołębiowski, T. and Tomecka-Suchoń, S. (2008). Geotechnical analysis and 4D GPR measurements for the assessment of the risk of sinkholes occurring in a Polish mining area, Near Surface Geophysics 6(4) 233-243.
  • McClymont, A.F., Green, A.G., Streich, R., Horstmeyer, H., Tronicke, J., Nobes, D.C., Pettinga, J., Campbell, J. and Langridge, R. (2008). Visualization of active faults using geometric attributes of 3D GPR data: An example from the alpine fault zone, New Zealand Geophysics 73(2): B11-B23.
  • Miaskowski, A. and Cieszczyk, S. (2011). Two-step inverse problem algorithm for ground penetrating radar technique, Przegląd Elektrotechniczny 87(12b): 22-24.
  • Tadeusiewicz, R. (2010). New trends in neurocybernetics, Computer Methods in Materials Science 10(1): 1-7.
  • Tadeusiewicz, R. (2011). Introduction to intelligent systems, in B.M. Wilamowski and J.D. Irvis (Eds.), Fault Diagnosis. Models, Artificial Intelligence, Applications, CRC Press, Boca Raton, FL, Chapter 1, pp. 1-1-1-12.
  • Tadeusiewicz, R., Chaki, R. and Chaki, N. (2014). Exploring Neural Networks with C#, CRC Press, Boca Raton, FL.
  • Wei-Li, Huilin-Zhou and Xiaoting-Wan (2012). Generalized Hough transform and ANN for subsurface cylindrical object location and parameters inversion from GPR data, 14th International Conference on Ground Penetrating Radar GPR, Shanghai, China, pp. 281-285.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv25i4p955bwm
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