Neural Networks as a Tool for Georadar Data Processing

Piotr Szymczyk; Sylwia Tomecka-Suchoń; Magdalena Szymczyk

doi:10.1515/amcs-2015-0068

.blurhash-client-img { display: none !important; }

Neural Networks as a Tool for Georadar Data Processing

International Journal of Applied Mathematics and Computer Science

Volume 25 (2015): Issue 4 (December 2015)

By: Piotr Szymczyk, Sylwia Tomecka-Suchoń and Magdalena Szymczyk

Open Access

|Dec 2015

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.

References

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 Geophysics6(4) 233–243.10.3997/1873-0604.2008018
Search in Google Scholar
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 Geophysics73(2): B11–B23.10.1190/1.2825408
Search in Google Scholar
Miaskowski, A. and Cieszczyk, S. (2011). Two-step inverse problem algorithm for ground penetrating radar technique, Przegląd Elektrotechniczny87(12b): 22–24.
Search in Google Scholar
Tadeusiewicz, R. (2010). New trends in neurocybernetics, Computer Methods in Materials Science10(1): 1–7.
Search in Google Scholar
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. 1-1–1-12.10.1201/b10604-3
Search in Google Scholar
Tadeusiewicz, R., Chaki, R. and Chaki, N. (2014). Exploring Neural Networks with C#, CRC Press, Boca Raton, FL.
Search in Google Scholar
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.
Search in Google Scholar

Articles in this issue

DOI: https://doi.org/10.1515/amcs-2015-0068 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X

Journal RSS Feed

Language: English

Page range: 955 - 960

Submitted on: Jul 7, 2014

Published on: Dec 30, 2015

Published by: University of Zielona Góra

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

neural networks,

artificial neural networks,

ground penetrating radar,

classification of a geological structure,

sinkhole

Related subjects:

Mathematics,

Applied mathematics

© 2015 Piotr Szymczyk, Sylwia Tomecka-Suchoń, Magdalena Szymczyk, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Volume 25 (2015): Issue 4 (December 2015)