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The prevalence of unexploded ordnance (UXO), discarded military munitions (DMM), and munitions constituents (MC) at both active and formerly used defense sites (FUDS) has created a necessity for remediation efforts to mitigate the potential environmental and public health hazards posed by these munitions and explosives of concern (MEC). UXO remediation operations typically incorporate electromagnetic induction (EMI) or magnetometer surveys to identify potential MEC hazards located throughout cleanup sites. Often, significant costs are allocated for the intrusive investigation by dig teams of magnetic field anomalies associated with harmless objects such as fragmentation, scrap, or geological clutter at these sites. Recent developments in advanced EMI sensor technologies, i.e., those that employ multi-axis transmitter and receiver configurations, have enabled classification of a vast majority of these non-hazardous objects prior to excavation. One of the key requirements for successfully implementing MEC classification is the acquisition of high quality EMI data prior to analysis. Factors such as improper sensor positioning, low signal-to-noise ratio, or insufficient data sampling can lead to poor performance of classification algorithms. We present results from recent field evaluations of an approach for incorporating an in-field analysis of data quality metrics as part of the EMI survey process. Specifically, this approach applies a dipole inversion routine to the EMI data immediately after acquisition is complete. Data and model parameters are subsequently used to extract quality metrics, which are supplied to the operator in the form of a quality decision. This process provides the operator with high confidence that the data will yield effective classification results.
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