Neural networks as a tool for georadar data processing

• Autorzy: Piotr Szymczyk , Sylwia Tomecka-Suchoń , Magdalena Szymczyk
• 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.

Słowa kluczowe

EN

neural networks; artificial neural networks; ground penetrating radar; classification of a geological structure; sinkhole

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2015
• Tom: 25
• Numer: 4
• Strony: 955-960

Daty

wydano

2015

otrzymano

2014-07-07

poprawiono

2014-10-14

Twórcy

autor

Piotr Szymczyk

• Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Kraków, Poland

autor

Sylwia Tomecka-Suchoń

• Department of Geophysics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Kraków, Poland

autor

Magdalena Szymczyk

• 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.

Identyfikatory

DOI

10.1515/amcs-2015-0068