Camp Butner UXO Data Inversion and Classification Using Advanced EMI Models

Abstract : Advanced (non-simple-dipole) EMI models' inversion and classification performance is presented for the ESTCP Live-site UXO Discrimination Study at former Camp Butner, NC. The advanced models combine: (1) the joint diagonalization (JD) algorithm for estimating number of potential anomalies from the measured data without inversion, (2) the orthonormalized volume magnetic source (ONVMS) for representing targets' EMI responses and extracting targets' intrinsic parameters feature vector, and (3) the Gaussian Mixture algorithm and probability neural network, that utilizes the extracted discrimination features for classifying buried objects as targets of interest or not. Namely, the studies were conducted for the next generation sensor data: Time-domain Electromagnetic Multi-sensor Towed Array Detection System (TEMTADS) and Metal Mapper (MM) sensors' cued data sets collected at the Camp Bunter, live UXO site. These sensors provide the measured multi-static response (MSR) data matrix. Eigenvalues versus time, which are determined using the JD from the MSR data matrix provide information about the number of targets contributing to the signal and their initial classification features. Once the number of targets is known, then data are inverted and intrinsic parameters, such as the total ONVMS that is a function of target's geometry and material composition, are determined for each potential target. These intrinsic parameters are grouped using the unsupervised Gaussian mixture approach. For each group an anomaly is identified and ground truth is requested. Once the requested ground truth data are obtained, then each of the groups is classified. In this presentation, the advanced EMI methods data inversion, processing and discrimination scheme will be reviewed, and the classification results scored by the Institute for Defense Analyses (IDA) will be presented for both the TEMTADS and MM sensors Camp Butner, NC cued data sets.