Title :
Application of neural network and principal component analysis to GPR data
Author :
Pantoja, Mario F. ; Rodríguez, Jesús B. ; Bretones, Amelia R. ; de Jong, C.M. ; García, S.G. ; Martin, Rafael G. ; Vieira, Douglas A G
Author_Institution :
Dept. Electromagnetismo y Fis. de la Materia, Univ. de Granada, Granada, Spain
Abstract :
This communication presents a prediction algorithm for the detection of features in GPR-based surveys. Based on signal processing and soft-computing techniques, the coupled use of principal-component analysis and neural networks enables a definition of an efficient method for analyzing GPR electromagnetic data. Results for detecting features of geological layers demonstrate not only the accuracy of the predictions of the method but also the simple interpretation of its output through reconstructed images of the scenarios.
Keywords :
feature extraction; geomagnetism; geophysical image processing; ground penetrating radar; image reconstruction; neural nets; principal component analysis; terrestrial electricity; GPR data; GPR electromagnetic data; GPR-based surveys; feature detection; geological layers; neural network; prediction algorithm; principal component analysis; reconstructed images; signal processing; soft-computing techniques; Artificial neural networks; Finite difference methods; Ground penetrating radar; Materials; Principal component analysis; Time domain analysis; Training; Ground-Penetrating Radar; Neural network applications; Radar signal processing;
Conference_Titel :
Advanced Ground Penetrating Radar (IWAGPR), 2011 6th International Workshop on
Conference_Location :
Aachen
Print_ISBN :
978-1-4577-0332-4
Electronic_ISBN :
978-1-4577-0331-7
DOI :
10.1109/IWAGPR.2011.5963854
