Improved detection performance using a Bayesian decision-theoretic approach

The fundamental goal of target identification using geophysical methods is to achieve a high detection rate along with a low false alarm rate. While many electromagnetic (EM) and magnetic detectors achieve the first of these goals, it is often at the cost of a prohibitively high false alarm rate. In this paper, we present a Bayesian decision-theoretic approach to target identification using EM data. This approach provides both an improved detection scheme and performance evaluation in the form of ROC curves plotting probability of detection versus false alarm rate. In addition to detailing the Bayesian-based approach, we present selected case studies that utilize broadband electromagnetic data acquired with the GEM-3 sensor. We compare the survey results obtained with standard thresholding analyses to survey results obtained using statistically based signal detection theory. Our preliminary results indicate that the detector derived using signal detection theory is superior to the standard detection schemes that utilize contour plots and thresholding. The results also indicate that detection performance is dramatically improved when multi-frequency data is utilized. Furthermore, since detection performance is observed to be a function of frequency, careful selection of the EM frequencies at which data is recorded may result in further improvements.