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 language | English (US) |
---|---|
Title of host publication | 78th Society of Exploration Geophysicists International Exposition and Annual Meeting, SEG 2008 |
Publisher | Society of Exploration Geophysicists |
Pages | 2912-2916 |
Number of pages | 5 |
ISBN (Print) | 9781605607856 |
State | Published - Jan 1 2018 |
Event | 78th Society of Exploration Geophysicists International Exposition and Annual Meeting, SEG 2008 - Las Vegas, United States Duration: Nov 9 2008 → Nov 14 2008 |
Other
Other | 78th Society of Exploration Geophysicists International Exposition and Annual Meeting, SEG 2008 |
---|---|
Country | United States |
City | Las Vegas |
Period | 11/9/08 → 11/14/08 |
ASJC Scopus subject areas
- Geophysics