Artificial Neural Networks and Machine Learning techniques applied to Ground Penetrating Radar: A review. Issue 2 (28th July 2020)

• Record Type: Journal Article
• Authors: Travassos, Xisto L.; Avila, Sérgio L.; Ida, Nathan
• Is Part Of: Applied computing and informatics. Volume 17:Issue 2(2021)
• Page Start: 296
• Page End: 308
• Publication Date: 2020-07-28
• Subjects: Ground Penetrating Radar -- Artificial Neural Networks -- Machine Learning -- Review; Information science -- Periodicals; Information storage and retrieval systems -- Periodicals; 004
• DOI: 10.1016/j.aci.2018.10.001
• Languages: English
• ISSNs: 2210-8327

Abstract

Abstract : Ground Penetrating Radar is a multidisciplinary Nondestructive Evaluation technique that requires knowledge of electromagnetic wave propagation, material properties and antenna theory. Under some circumstances this tool may require auxiliary algorithms to improve the interpretation of the collected data. Detection, location and definition of target's geometrical and physical properties with a low false alarm rate are the objectives of these signal post-processing methods. Basic approaches are focused in the first two objectives while more robust and complex techniques deal with all objectives at once. This work reviews the use of Artificial Neural Networks and Machine Learning for data interpretation of Ground Penetrating Radar surveys. We show that these computational techniques have progressed GPR forward from locating and testing to imaging and diagnosis approaches.