Discrimination of munitions and explosives of concern
at F.E. Warren AFB using linear genetic
programming
GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation, 2, page 1999--2006. London, ACM Press, (7-11 July 2007)Abstract
Removing underground, unexploded bombs, mortars,
cannon shells and other ordnance (MEC or UXO) from
former military ranges is difficult and expensive. The
principal difficulty is discriminating intact,
underground ordnance from other metallic items such as
fragments of exploded ordnance (Clutter), magnetic
rocks, and historic items such as horseshoes,
barbed-wire, and refrigerators. This study represents
the first, large-scale, blind-test of MEC
discrimination technology on production-grade,
survey-mode data from the cleanup of a real impact
site. The results reported here significantly advance
the state-of-the-art in MEC discrimination over
alternative forward modelling/ inversion approaches to
performing MEC discrimination. We combined Linear
Genetic Programming (LGP) and statistical analysis to
process data from the cleanup of 600 acres of the
F.E.Warren Air Force Base. These data contained almost
30,000 targets of interest identified by geophysicists,
including three-hundred thirty-two 75mm projectiles
(75mm) and 37mm projectiles (37mm). A little under
one-third of the ground truth was held back by the
customer for blind-testing. Our task was to
discriminate intact 37mm and 75mm from the clutter by
ordering the targets from most-likely to be MEC to
least-likely to be MEC in what is referred to as a
prioritised dig list. We identified all 75mm by
28.2percent of the way through our prioritized dig-list
and all 37mm by 64.2percent of the way through the
prioritised dig list. Thus, depending on ordnance type,
we reduced the number of targets that had to be
excavated (false alarms) to clear the entire site by
between 35percent and 72percent.
