Nicolas Lhomme

Unexploded ordnance discrimination using magnetic and electromagnetic sensors: Case study from a former military site

In a study at a military range with the objective to discriminate potentially hazardous 4.2-inch mortars from nonhazardous shrapnel, range, and cultural debris, six different discrimination techniques were tested using data from an array of magnetometers, a time-domain electromagnetic induction (EMI) cart, an array of time-domain sensors, and a time-domain EMI cart with a wider measurement bandwidth. Discrimination was achieved using rule-based or statistical classification of feature vectors extracted from dipole or polarization tensor models fit to detected anomalies. For magnetics, the ranking by moment yielded better discrimination results than that of apparent remanence from relatively large remanent magnetizations of several of the seeded items. The magnetometer results produced very accurate depths and fewer failed fits attributable to noisy data or model insuffi-ciency. The EMI-based methods were more effective than the magnetometer for intrinsic discrimination ability. The higher signal-to-noise ...

Cooperative Inversion of Time Domain Electromagnetic and Magnetometer Data for the Discrimination of Unexploded Ordnance

Magnetic and electromagnetic induction (EMI) surveys are the primary techniques used for unexploded ordnance (UXO) remediation projects. Magnetometry is a valuable geophysical tool for UXO detection because of the ease of data acquisition and its ability to detect relatively deep targets. However, magnetic data can have large false alarm rates caused by geological noise, and there is an inherent non-uniqueness when trying to determine the orientation, size and shape of a target. EMI surveys, on the other hand, are relatively immune to geologic noise and are more diagnostic for target shape and size but have a reduced depth of investigation. We aim to improve discrimination ability by developing an interpretation method that takes advantage of the strengths of both techniques. We consider cooperative inversion, where information from the inversion of one type of data is used as a constraint for inverting another. We compare the confidence with which we can discriminate UXO from non-UXO targets when inverting the data sets cooperatively, to results from individual inversions. Examples are given of the application of the methodology to time domain electromagnetic induction (TEM) and magnetic data sets collected at the Yuma Proving Ground UXO Standardized Test Site calibration grid and the Former Camp Sibert.

Assessing the Quality of Electromagnetic Data for the Discrimination of UXO Using Figures of Merit

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. A...

MPV-II: an enhanced vector man-portable EMI sensor for UXO identification

The Man-Portable Vector (MPV) electromagnetic induction sensor has proved its worth and flexibility as a tool for identification and discrimination of unexploded ordnance (UXO). TheMPV allows remediation work in treed and rough terrains where other instruments cannot be deployed; it can work in survey mode and in a static mode for close interrogation of anomalies. By measuring the three components of the secondary field at five different locations, the MPV provides diverse time-domain data of high quality. TheMPV is currently being upgraded, streamlined, and enhanced to make it more practical and serviceable. The new sensor, dubbedMPV-II, has a smaller head and lighter components for better portability. The original laser positioning system has been replaced with one that uses the transmitter coil as a beacon. The receivers have been placed in a configuration that permits experimental computation of field gradients. In this work, after introducing the new sensor, we present the results of several identification/discrimination experiments using data provided by the MPV-II and digested using a fast and accurate new implementation of the dipole model. The model performs a nonlinear search for the location of a responding target, at each step carrying out a simultaneous linear least-squares inversion for the principal polarizabilities at all time gates and for the orientation of the target. We find that the MPV-II can identify standard-issue UXO, even in cases where there are two targets in its field of view, and can discriminate them from clutter.

EMI sensor positioning using a beacon approach

Discrimination of buried exploded ordnance by inversion of electromagnetic data requires accurate sensor positioning. There are many contaminated areas were dense forest or significant topographic variation reduces accuracy or precludes use of standard geo-location methods, such as satellite-based Global Positioning System (GPS) and laser tracking systems (e.g., Robotic Total Station, RTS), as these rely on line of sight. We propose an alternative positioning system that is based on a beacon principle. The system was developed to survey with the Man-Portable Vector (MPV) EMI sensor. The magnetic moment of the MPV transmitter can be detected at a relatively large distance. The primary field is measured from a portable base station comprised of two vector receivers rigidly attached to either ends of a 1.5 meter horizontal boom. Control tests showed that relative location and orientation could be recovered with centimeter positional and one degree angular accuracy within a 3-4-meter range and 60-degree aperture (relative to boom transverse direction), which is more than sufficient to cover any UXO anomaly. This accuracy level satisfies commonly accepted positional requirement for discrimination. The beacon positioning system can facilitate classification of munitions in any man-trafficable area and was successfully deployed at a field demonstration.

Inversion of frequency domain data collected in a magnetic setting for the detection of UXO

Magnetic soils are a major source of false positives when searching for landmines or unexploded ordnance (UXO) with electromagnetic induction sensors. The viscosity effects of magnetic soil can be accurately modeled by assuming a ferrite relaxation with a log-uniform distribution of time constants. The frequency domain response of ferrite soils has a characteristic negative log-linear in-phase and constant quadrature component. After testing and validating that assumption, we process frequency domain electromagnetic data collected over UXO buried in a viscous remanent magnetic host. The first step is to estimate a spatially smooth background magnetic susceptibility model from the sensor. The response of the magnetically susceptibility background is then subtracted from the sensor data. The background removed data are then inverted to obtain estimates of the dipole polarization tensor. This technique is demonstrated for the discrimination of UXO with hand-held Geophex GEM3 data collected at a contaminated site near Denver, Colorado.

Sensor Placement via Optimal Experiment Design in EMI Sensing of Metallic Objects

This work, under the optimal experimental design framework, investigates the sensor placement problem that aims to guide electromagnetic induction (EMI) sensing of multiple objects. We use the linearized model covariance matrix as a measure of estimation error to present a sequential experimental design (SED) technique. The technique recursively minimizes data misfit to update model parameters and maximizes an information gain function for a future survey relative to previous surveys. The fundamental process of the SED seeks to increase weighted sensitivities to targets when placing sensors. The synthetic and field experiments demonstrate that SED can be used to guide the sensing process for an effective interrogation. It also can serve as a theoretic basis to improve empirical survey operation. We further study the sensitivity of the SED to the number of objects within the sensing range. The tests suggest that an appropriately overrepresented model about expected anomalies might be a feasible choice.

UXO and UXO Sensor Technology

Advanced electromagnetic induction sensors have been developed under the SERDP and ESTCP Munitions Response program to find and identify buried unexploded ordnance. These sensors consist of multiple transmit and receive coil configurations that collect sufficient data for inverting the time decaying, dipole polarizations of a buried metallic object. These polarizations can be used to identify the object as UXO or metallic debris. These sensor platforms have been deployed in both dynamic survey modes to locate and identify and stationary “cued” modes to just identify at target locations. ESTCP has sponsored a number of Live Site Demonstrations and these systems have been found to be very effective in finding UXO. An approach has been developed for advanced EMI survey data that applies a model based detection filter to locate metallic targets and a dipole inversion to identify the targets as UXO or clutter based on the inverted polarizations. Analysis of the Live Site data has found a significant fraction of the target locations have multiple target signals present. To address this, an N-dipole inversion is being applied to all target locations. This inversion inverts for a specified (N) number of targets. Fit results are returned for N = 1, 2, and 3 possible targets at each location. The problem arises that quite often all of the multi-target fits represent the data equally well. We will present these results and some strategies taken to insure that all possible targets of interest are selected from the multiple fit results. Work is also in progress to try and evaluate when the local target density is too high for valid analysis.