UXO target detection using magnetometry and EM survey data

摘要

Abstract: Digital filtering, principal component analysis (PCA), and an automated anomaly picker have been used to improve and automate target selection of unexploded ordnance (UXO). This is the first step in a three part program to develop new data analysis methods to automate target selection and improve discrimination of UXO from clutter and ordnance explosive waste (OEW) using magnetometry (Mag) and electromagnetic induction (EM) survey data. Traditionally, target detection has been accomplished by a time-consuming manual interactive data analysis approach. Experts screen the magnetometer data and select potential UXO targets based on their intuitive experience. EM data has been used in a secondary role in this process and the anomaly picking included classification and operator bias. In this program, the target detection step will use all of the data available and a separate classifier process will be used for identification and discrimination. Digital filtering is being used to enhance important features and reduce noise, while principal component analysis is being used to fuse three channels of data and reduce noise. Seven 50 meter-square data sets from two test sites were used to investigate these techniques. Features of interest are enhanced using filtering techniques. Inspection of the first- principal component suggests that data fusion of the magnetometer and EM data can be successfully accomplished. The new image consisting of circular features of varying diameters and intensities represent significant features present in all three data channels. Data with strong magnetometer and EM signals have the greatest intensity and in most cases noise is reduced. An automated anomaly picker has been designed to select targets from Mag, EM and PCA images. The method is fast and efficient as well as providing user options to control pick criteria. !12