Alltem Uxo Classification and Discrmination Results from the Yuma Proving Ground Standardized Test Site
Theodore H. Asch, David L. Wright, Craig W. Moulton, Trevor P. Irons and David V. Smith
Event name: 23rd EEGS Symposium on the Application of Geophysics to Engineering and Environmental Problems
Publication date: 11 April 2010
Info: Extended abstract, PDF ( 2.1Mb )
An advanced multi-axis electromagnetic induction system, ALLTEM, has been specifically designed for detection and discrimination of unexploded ordnance (UXO). This work has been funded by ESTCP (Project MM-0809). ALLTEM uses a continuous triangle-wave excitation that measures the target step response rather than the more common impulse response. Ferrous and non-ferrous metal objects have opposite polarities. The system multiplexes through all three orthogonal (Hx, Hy, and Hz axes) transmitting loops and records a total of 19 different transmitting (Tx) and receiving (Rx) loop combinations with a spatial data sampling interval of 15 cm to 20 cm. 2008 saw improvements to the ALLTEM with a new, lighter cart made of NOMEX honeycomb with fiberglass facing and new electronics with approximately 40% higher current output. An Attitude Heading and Reference System has also been integrated into the acquisition stream and is used to generate more precise locations of sensor locations. Software improvements include development and integration of ALLTEM analyses onto the Oasis Montaj platform. This includes importing survey data, gridding, noise analysis for threshold determination, automatic selection of targets, batch inversion of selected targets using PEST (a forward model independent non-linear inversion), and automatic classification of inverted targets into clutter and targets of interest. This paper presents some of the results of a demonstration and validation survey at the Yuma Proving Ground in February 2009. The U.S. Geological Survey operated ALLTEM with a Leica 1200 GPS over the Army’s UXO Calibration and Blind Test Grids and a portion of the Open Field Area. Ongoing data analyses indicate that this new and improved version of the ALLTEM is able to detect anomalous features and to automatically classify targets as being items of interest or not and then to discriminate the munitions’ types.