The primary objectives of this project were to:
UXO classification procedures consist of the following sequential steps: background corrections, target detection/picking, data inversion and target feature parameter estimation, ranking, training, and finally classification, i.e., separating UXO from non-hazardous anomalies. Under this project the project team has developed and tested a user-friendly software package for advanced EMI sensor data preprocessing, inversion and classification. The software package, which supports both cued and dynamic survey datasets, allows the efficient execution of the following procedures:
During the course of this project, the project team processed multiple datasets collected at eleven ESTCP UXO Live Site demonstrations, including:
The advanced EMI data inversion and classification technology was demonstrated at these eleven UXO live sites for identifying all TOI and eliminating more than 75% of the clutter. The technology has applied to cued and dynamic data sets collected by the next generation EMI sensors: such as MetalMapper, 5x5 and 2x2 Time Domain Electromagnetic Towed Array Detection System, man portable vector, Berkeley UXO Discriminator and one pass transient electromagnetic array. The demonstrations have showed that for most sites the advanced classification technology identified all TOI while correctly classifying 75% to 92% of the clutter at specified Stop-Dig points.
However, there were few sites where the algorithm did not correctly classify one or more TOI due to insufficient data quality, magnetic soil and inaccurately documenting the intrusive results. These illustrated the importance of a well-defined data collection procedure and accounting magnetic soil responses during intrusive investigations. A comparison of the classification results for different sensors showed that they perform equally well when data are analyzed using the advanced EMI models. The project team comes to a similar conclusion when compared between classification results for cued and survey data sets. The choice of which sensor in with mode to deploy on site can therefore be driven by cost and which system can most efficiently survey the terrain.