Identification of unexploded ordnance from clutter using neural networks

Anna Szidarovszky, Mary M Poulton, Scott MacInnes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The largest costs associated with subsurface Unexploded Ordnance (UXO) remediation are associated with removing non-UXO debris. Discrimination between UXO and non-UXO is important for both cost and safety reasons. A neural network was developed to distinguish between UXO and non-UXO clutter using Time Domain Electromagnetic Method (TEM) data. There are two stages for the learning process of neural network: training and testing. A synthetic dataset was created using actual acquisition configurations, with varying amounts of random noise. This dataset included 934 UXO targets representing 7 different UXO types, and 789 clutter objects based on four templates with varying size and random asymmetry. The results show 97% accuracy for correctly classifying clutter, and 97% accuracy for correctly classifying UXO.

Original languageEnglish (US)
Title of host publication78th Society of Exploration Geophysicists International Exposition and Annual Meeting, SEG 2008
PublisherSociety of Exploration Geophysicists
Pages2912-2916
Number of pages5
ISBN (Print)9781605607856
StatePublished - Jan 1 2018
Event78th Society of Exploration Geophysicists International Exposition and Annual Meeting, SEG 2008 - Las Vegas, United States
Duration: Nov 9 2008Nov 14 2008

Other

Other78th Society of Exploration Geophysicists International Exposition and Annual Meeting, SEG 2008
CountryUnited States
CityLas Vegas
Period11/9/0811/14/08

ASJC Scopus subject areas

  • Geophysics

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  • Cite this

    Szidarovszky, A., Poulton, M. M., & MacInnes, S. (2018). Identification of unexploded ordnance from clutter using neural networks. In 78th Society of Exploration Geophysicists International Exposition and Annual Meeting, SEG 2008 (pp. 2912-2916). Society of Exploration Geophysicists.