Identification of unexploded ordnance from clutter using neural networks

Anna Szidarovszky, Mary Poulton, Scott MacInnes

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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)
Pages (from-to)2912-2916
Number of pages5
JournalSEG Technical Program Expanded Abstracts
Issue number1
StatePublished - Jan 2008


  • Electromagnetic

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Geophysics


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