New technologies for the detection and classification of buried unexploded ordnance (UXO) have the potential to significantly reduce the cost of munitions response efforts. In particular, electromagnetic (EM) sensors developed specifically for this problem can reliably discriminate between ordnance and non-hazardous metallic clutter. ESTCP demonstration projects have shown that advanced classification with next generation EM sensors consistently outperforms conventional detection arrays. To further the adoption of advanced sensors and processing by industry, this project examined how to best deploy available technologies for a particular remediation problem and how to ensure with high confidence that all targets of interest are identified following remediation efforts. This presentation provided an overview of key project results including the calculation of detection thresholds for next generation sensors, optimization of the detection channel, delineation of high density clutter areas, and prediction of classification performance using data and model metrics.
Dr. Laurens Beran is a research geophysicist with Black Tusk Geophysics in Princeton, NJ. He specializes in development and application of statistical algorithms for classification of unexploded ordnance. Dr. Beran has served as the principal investigator of multiple SERDP projects including MR-2226 and MR-1629. His current work focuses on detection and classification of deep UXO and underwater UXO. Dr. Beran completed his master’s and doctoral degrees in geophysics at the University of British Columbia.