The need for assessing data quality in unexploded ordnance (UXO) remediation problems arises from two sources. In the planning stage it is essential that the data are acquired in sufficient numbers and with sufficient accuracy to answer the detection or discrimination problem of relevance. At the interpretation stage it is critical to objectively assess whether the data are of sufficient quality to warrant subsequent processing, inversion, and classification. Faced with this practical challenge of defining data quality we propose a Figure of Merit (FOM). FOM is a reliability indicator derived from quantities that affect the quality of data, such as anomaly coverage, line spacing, station spacing, instrument noise, survey location errors, etc. The FOM can also include informative features of the inversion, such as the variance of key model parameters, and thus it depends on the inverse model to be applied. Anomalies associated with higher values of FOM should have increased reliability in classification. Anomalies below a critical threshold will not be suitable for advanced analysis.

In this paper, we apply the FOM framework to guide the practical interpretation of field data collected at Camp Sibert as part of the Environmental Security Technology Certification Program (ESTCP) Discrimination Study Pilot Project. Using simulations of electromagnetic (EM) data for different quality of survey designs, we examine the success rate of inversions to identify key FOM parameters that can explain unreliable inversion results. In this manner, the relationship between FOM and reliability is calibrated on synthetic data before application to the interpretation of field data. A trust index for each inversion can subsequently be included into a discrimination algorithm to help establish a priority dig list. We find that incorporating the FOM in the classification procedure significantly reduces the number of non-UXO items that need to be excavated to recover all UXO.

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