Adaptive and Iterative Processing Techniques for Overlapping Signatures

摘要

A primary goal of the UXO research community is to develop technologies that detect and localize buried UXO, and that discriminate them from clutter. Electromagnetic sensors such as EMI systems operate by detecting the presence of an anomalous electromagnetic field that could be caused by buried UXO. Physics-based modeling and analysis procedures, developed under previous SERDP and ESTCP funding for electromagnetic induction and magnetometer sensor data of isolated targets, have been shown to discriminate UXO from clutter based on the derived source parameters for spatially discrete target signatures. However, many real world UXO remediation sites contain highly- contaminated regions with high density of anomalies, both UXO and clutter. In these cases, where the signatures from multiple targets overlap, whether or not they are UXO, the standard procedures do not work well. The primary problem with overlapping signatures is that conventional inversion procedures assume a single source whose signature is spatially separated from other signatures. Currently, analysts attempt to isolate anomalies by carefully selecting the data to be inverted (usually by manually inspecting a two dimensional plot of the anomaly and carving out two separate regions of data) and assuming that the selected data reflect the signature caused by a single source. The goal of this project was to develop advanced iterative techniques for inverting magnetic and electromagnetic data for situations in which the signatures from two targets overlap. After developing the methodology, the algorithm(s) would be tested first on synthetic data without any added noise. The synthetic data would be used to systematically vary the parameters of the two targets by changing their depths, the distance between them and their relative orientations. Later, controlled test data would be used to further validate and test the algorithms in real-life situations.