Investigating the effect of data quality on time domain electromagnetic discrimination
Using field data and numerical simulations we investigate the effect of data quality on time domain electromagnetic discrimination. Data quality decreases when measurements contain responses not accounted for by our mathematical modelling. This can include instrument noise, inaccurately reported position and orientation information, geologic contributions to the signal, and loss of validity of the forward modelling. Survey design is critical to data quality in order to have sufficient sampling of data anomalies, and also to ensure that each target is illuminated such that both the axial and transverse components of the polarization can be excited and measured. For dipole model based discrimination algorithms, success is contingent upon the accuracy with which the components of the polarization tensor can be estimated. Field data from different survey modes are analysed to identify noise sources and provide quantitative estimates of the noise in each survey. Inversion results show that increased noise levels lead to greater spread in recovered parameters. Monte Carlo simulations are performed in order to investigate the importance of other data quality factors. Analysis of inversion results from the simulations show that anomaly size, signal to noise ratio, positioning error, line spacing and station spacing all play a role in the spread of recovered parameters. Through the analysis of our simulation results we propose a figure of merit as a means of quantifying different data quality factors with a single number and relate this number to the accuracy with which parameters can be estimated.
Clutter Identification Using Electromagnetic Survey Data, ESTCP MR-201001 Cost and Performance Report
Abstract : The demonstration was part of the ESTCP Live Site Demonstration at the former Spencer Artillery Range, TN, during May 2012. The dynamic test area covered 0.5 Ha of open field. We report the classification performance results for 339 unknown anomalies detected within the Dynamic Area. Approximately 70% of the detected anomalies could be classified from the dynamic data alone. Using a combination of results from dynamic data and cued data, 100% of the identified UXO were correctly classified and the number of necessary digs could be reduced by at least 75%.
A Feasibility Study on Using Physics-Based Modeler Outputs to Train Probabilistic Neural Networks for UXO Classification
Abstract : A probabilistic neural network (PNN) has been applied to the detection and classification of unexploded ordnance (UXO) measured using magnetometry data collected using the Multi-sensor Towed Array Detection System (MTADS). Physical parameters obtained from a physics based modeler were used to describe the UXO and scrap targets found at three sites: Badlands Bombing Range (BBR) Target 1 and 2 and the Former Buckley Field. The PNN was trained and tested using cross validation (CV) software developed at NRL. The PNN was able to correctly identify between 84% to 94% of the targets. By adjusting the probability threshold, further improvements in the discrimination of UXO were possible: 96% of the UXO were correctly identified for BBR Target 1, 100% for BBR Target 2, and 94% for the former Buckley Field. The ability to train using one site (BBR target 2) and predict another (BBR Target 1) was successful with 95% of the UXO correctly identified and a false alarm rate of 35%.