Identification of unexploded ordnance from clutter using neural networks

Anna Szidarovszky, Mary 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 - 2018
Event78th Society of Exploration Geophysicists International Exposition and Annual Meeting, SEG 2008 - Las Vegas, United States
Duration: Nov 9 2008Nov 14 2008

Publication series

Name78th Society of Exploration Geophysicists International Exposition and Annual Meeting, SEG 2008

Other

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

ASJC Scopus subject areas

  • Geophysics

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