Geodesic
Neural networks as a tool for georadar data processing
International Journal of Applied Mathematics and Computer Science, Tome 25 (2015) no. 4, pp. 955-960.

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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.
Keywords: neural network, artificial neural network, ground penetrating radar, geological structure, sinkhole
Mots-clés : sieć neuronowa, sztuczna sieć neuronowa, georadar, penetracja gruntu, budowa geologiczna, zapadlisko górnicze 
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Szymczyk, P.; Tomecka-Suchoń, S.; Szymczyk, M. Neural networks as a tool for georadar data processing. International Journal of Applied Mathematics and Computer Science, Tome 25 (2015) no. 4, pp. 955-960. http://geodesic.mathdoc.fr/item/IJAMCS_2015_25_4_a17/

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