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A prediction algorithm for data analysis in GPR-based surveys
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DOI：10.1016/j.neucom.2015.05.081.png)
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Abstract
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This paper presents a prediction algorithm for features detection in Ground Penetrating Radar (GPR) based surveys. Based on signal processing and soft-computing techniques, the coupled use of principal-component analysis and neural networks enable a definition of an efficient method for analyzing GPR electromagnetic data. To guarantee a low error rate, a study of the algorithm main numerical parameters was performed by means of electromagnetic synthetic-data models. Results for detecting features of geological layers demonstrate not only the method predictions accuracy but also the simple interpretation of its output through scenarios reconstructed images. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Ground-penetrating radar
Neural network applications
Radar signal processing
Geophysics
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