Neural networks as a tool for georadar data processing
Piotr Szymczyk; Sylwia Tomecka-Suchoń; Magdalena Szymczyk
International Journal of Applied Mathematics and Computer Science (2015)
- Volume: 25, Issue: 4, page 955-960
- ISSN: 1641-876X
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topPiotr Szymczyk, Sylwia Tomecka-Suchoń, and Magdalena Szymczyk. "Neural networks as a tool for georadar data processing." International Journal of Applied Mathematics and Computer Science 25.4 (2015): 955-960. <http://eudml.org/doc/275901>.
@article{PiotrSzymczyk2015,
abstract = {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.},
author = {Piotr Szymczyk, Sylwia Tomecka-Suchoń, Magdalena Szymczyk},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {neural networks; artificial neural networks; ground penetrating radar; classification of a geological structure; sinkhole},
language = {eng},
number = {4},
pages = {955-960},
title = {Neural networks as a tool for georadar data processing},
url = {http://eudml.org/doc/275901},
volume = {25},
year = {2015},
}
TY - JOUR
AU - Piotr Szymczyk
AU - Sylwia Tomecka-Suchoń
AU - Magdalena Szymczyk
TI - Neural networks as a tool for georadar data processing
JO - International Journal of Applied Mathematics and Computer Science
PY - 2015
VL - 25
IS - 4
SP - 955
EP - 960
AB - 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.
LA - eng
KW - neural networks; artificial neural networks; ground penetrating radar; classification of a geological structure; sinkhole
UR - http://eudml.org/doc/275901
ER -
References
top- 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.
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- Miaskowski, A. and Cieszczyk, S. (2011). Two-step inverse problem algorithm for ground penetrating radar technique, Przegląd Elektrotechniczny 87(12b): 22-24.
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- 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.
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