A prediction algorithm for data analysis in GPR-based surveys

作者：Rodriguez J B; Pantoja M F^*; Travassos X L; Vieira D A G; Saldanha R R

来源：Neurocomputing, 2015, 168: 464-474.

DOI：10.1016/j.neucom.2015.05.081

• Ground-penetrating radar
• Neural network applications
• Radar signal processing
• Geophysics

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.

• 出版日期2015-11-30