doi: 10.17706/ijapm.2014.4.6.426-433
Noise Attenuation from GPR Data Using Wavelet Transform and Artificial Neural Network
Sid-Ali Ouadfeul
1
,
- 1 Algerian Petroleum Institute, IAP, Avenue 1er Novembre. Boumerdes, 35000, Algerian
- 2 Labophyt, FHC, UMBB, Avenue de l’indépenance, 35000, Boumerdes, Algeria.
Abstract
In this paper, a tentative of noise attenuation from the 3D Ground Penetrating Radar (GPR) data using the wavelet transform and the Multilayer Perceptron neural network model is implanted. GPR data recorded in Algerian Sahara are filtered firstly using the discrete wavelet transform, after that a MLP machine with three layers is trained in a supervised learning mode, the input is an extracted profile from the raw 3D GPR data and the output consists of the GPR data of the same profile but after filtering. The estimated weights of connection are used to propagate the remaining non-filtered data through the implanted machine, the calculated output consists to the filtered GPR data. Comparison between the filtered data using the MLP machine and the continuous wavelet transform shows that the neural network machine can be used for S/N ratio improvement of the noisy GPR data.
Keywords
- Noise
- attenuation
- GPR
- discrete wavelet transform
- neural network
- multilayer perceptron
How to Cite
Sid-Ali Ouadfeul and Leila Aliouane, "Noise Attenuation from GPR Data Using Wavelet Transform and Artificial Neural Network," International Journal of Applied Physics and Mathematics, vol. 4, no. 6, pp. 426-433, 2014. https://doi.org/ 10.17706/ijapm.2014.4.6.426-433
Copyright & License
Copyright © 2014 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).