The presence of unexploded ordnance (UXO), discarded military munitions (DMM), and munitions constituents (MC) at
both active and formerly used defense sites (FUDS) has created a necessity for production-level efforts to remove these
munitions and explosives of concern (MEC). Ordnance and explosives (OE) and UXO removal operations typically
employ electromagnetic induction (EMI) or magnetometer surveys to identify potential MEC hazards in previously
determined areas of interest. A major cost factor in these operations is the significant allocation of resources for the
excavation of harmless objects associated with fragmentation, scrap, or geological clutter. Recent advances in
classification and discrimination methodologies, as well as the development of sensor technologies that fully exploit
physics-based analysis, have demonstrated promise for significantly reducing the false alarm rate due to MEC related
clutter. This paper identifies some of the considerations for and the challenges associated with implementing these
discrimination methodologies and advanced sensor technologies in production-level surveys. Specifically, we evaluate
the implications of deploying an advanced multi-axis EMI sensor at a variety of MEC sites, the discrimination
methodologies that leverage the data produced by this sensor, and the potential for productivity increase that could be
realized by incorporating this advanced technology as part of production protocol.
Current time-domain electromagnetic induction instruments generally only utilize data acquired after the cessation of the
transmitted field. During this "off time", signals are dominated by induced eddy currents and magnetic surface modes,
but do not fully capture the magnetostatic response of permeable and conductive metallic ordnance. In this paper, we
investigate the response of EMI systems that measure signals during excitation of the primary magnetic field (the so-called
"on-time"). Our analysis shows that on-time signals have great potential to yield useful information that is not
often exploited in current EMI systems. We compare analytical models to data from state-of-the-art time-domain EM
sensors that have the capability to sample receivers during the on-time. We present modeling results that represent the
responses from different current ramps and on-time waveforms for objects and ground. We consider target and clutter
objects and grounds having a range of material properties, shapes and sizes, and configurations and investigate signal
processing and inversion methods for target detection and discrimination. Specifically, correlations between on-time
and off-time signals are shown to be a powerful tool for discriminating ferrous and non-ferrous metallic objects.
In UXO contaminated sites, there are often cases in which two or more targets are likely close together and
the electromagnetic induction sensors record overlapping signals contributed from each individual target. It is
important to develop inversion techniques that have the ability to recover parameters for each object so that
effective discrimination can be performed. The multi-object inversion problem is numerically challenging because
of the increased number of parameters to be found and because of the additional nonlinearity and non-uniqueness.
An inversion algorithm is easily trapped in a local minimum of the objective function that is being minimized.
To tackle these problems we exploit the fact that, based on an equivalent magnetic dipole model, the measured
electromagnetic induction signals are nonlinear functions of locations and orientations of equivalent dipoles and
linear functions of their polarizations. Based on these conditions, we separate model parameters into nonlinear
parts (source locations and orientations) and linear parts (source polarizations) and proceed sequentially. We
propose a selected multi-start nonlinear procedure to first localize multiple sources and then get the estimated
polarization tensor matrix for each item through a subsequent or a nested linear inverse problem. It follows that
the orientations of the objects are estimated from the computed tensor matrix. The resultant parameter set is
input to a complete nonlinear inversion where all of the dipole parameters are estimated. The overall process can
be automated and thus efficiently carried out both in terms of human interaction and numerical computation
time. We validate the technique using synthetic and field data.
Recently the SERDP/ESTCP office under the UXO Discrimination Pilot Study Program acquired high-density data over
hundreds of targets using time-domain EM-63 sensor at Camp Sibert. The data were inverted and analyzed by various
research groups using a simple dipole model approach and different classification tools. The studies demonstrated high
discrimination probability with a low false-alarm rate. However in order to further improve discrimination between
UXO and non-UXO items a better understanding is needed of the limits of current and emerging processing approaches.
In this paper, the simple dipole model and a physically complete model called the normalized surface magnetic source
(NSMS) the Camp Sibert data sets. The simple, infinitesimal dipole representation is by far the most widely employed
model for UXO modeling. In this model, one approximates a target's response when excited by a primary (transmitted)
field using an induced infinitesimal dipole (in turn described by a single magnetic polarizability matrix). The greatest
advantage of the dipole model is that it is simple and imposes low computation costs. However, researchers have
recently begun to realize the limitations of the simple dipole model as an inherently coarse description of the EMI
behavior of complex, heterogeneous targets like UXO. To address these limitations, here the NSMS is employed as a
more powerful forward model for data inversion and object discrimination. This method is extremely fast and equally
applicable to the time or frequency domains. The object's location and orientation are estimated by using a standard nonlinear
inversion-scattering approach. The discrimination performance between the dipole and NSMS models are
conducted by investigating model fidelity and data density issues, positional accuracy and geological noise effects.
Soils that exhibit strong Viscous Remanent Magnetization (VRM) have a major effect on time- and frequency-domain
data collected by electromagnetic induction (EMI) sensors. Small scale topography in the form of
bumps or troughs will also distort the EM signal due to UXO. If these components of "geologic noise" are not
adequately accounted for in the inversion process, then the ability to carry out discrimination will be
marginalized. Our long-term goal is to include these effects into the inversion but the chosen methodology
depends upon some crucial issues. Foremost, we need to be certain that we can numerically compute the effects
of complex magnetic susceptibility and topography that would be encountered in field surveys. Second, we
need to investigate whether there is significant electromagnetic interaction between the UXO and its host
material or whether the signals are additive. If the total signal can be adequately represented by the
superposition of the two individual signals (ie the field of a UXO in free space, and the effect of a conductive
host with topography and complex magnetic susceptibility) then there are many avenues by which data can be
preprocessed to remove contaminating effects, or by which joint inversion of UXO and host parameters can be
carried out. In this paper we concentrate upon the issues of modeling and the possibility of additivity. We first
validate our EM numerical modeling code for halfspaces having VRM. We then show that EM interaction
between the host and a compact metallic object is minimal for a specific example which is typical of a buried
ordnance in a highly magnetic soil such as on Kaho'olawe, Hawaii. We also model soil responses for simple
variations of surface roughness including both a single bump and a single trench and compare those results with
field data acquired over similar environments.
Magnetic soils are a major source of false positives when searching for unexploded ordnance with electromagnetic
induction sensors. In adverse areas up to 30% of identified electromagnetic induction anomalies have been
attributed to geology. In the presence of magnetic soil, sensor movement and surface topography can cause
anomalies in the data that have similar size and shape to those from compact metallic targets. In areas where
the background geological response is small relative to the response of metallic targets, electromagnetic induction
data can be inverted for the dipole polarization tensor. However, spatially correlated noise from the presence
of a geologic background greatly reduces the accuracy of dipole polarization estimates. In this presentation we
examine the effects of sensor movement on the measured EM response of a magnetic background signal. We
demonstrate how sensor position and orientation information can be used to model the background soil response
and improve estimates of a target's dipole polarization tensor.
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.
Magnetic soils are a major source of false positives when searching for landmines or unexploded ordnance (UXO)
with electromagnetic induction sensors. In adverse areas up to 30% of identified electromagnetic (EM) anomalies
are attributed to geology. The main source of the electromagnetic response is the magnetic viscosity of
the ferrimagnetic minerals magnetite and maghaemite. The EM phenomena that give rise to the response of
magnetically viscous soil and metal are fundamentally different. The viscosity effects of magnetic soil can be
accurately modelled by assuming a ferrite relaxation with a log-uniform distribution of time constants. The
EM response of a metallic target is due to eddy currents induced in the target and is a function of the target's
size, shape, conductivity and magnetic susceptibility. In this presentation, we consider different soil compensation
techniques for time domain and frequency domain EM data. For both types of data we exploit the EM
characteristics of viscous remnantly magnetized soil. These techniques will be demonstrated with time domain
and frequency domain data collected on Kaho'olawe Island, Hawaii. A frequency domain technique based on
modeling a negative log-linear in-phase and constant quadrature component was found to be very effective at
suppressing false-alarms due to magnetic soils.
KEYWORDS: Data modeling, Magnetism, Data acquisition, Electromagnetism, Sensors, Unexploded object detection, Roads, Signal to noise ratio, Algorithm development, Target acquisition
Approximately 75% of buried UXO cleanup costs are expended excavating false alarm anomalies (i.e., digging on the locations of geophysical anomalies that are not caused by UXO). Although probabilities of detection at documented UXO test sites are commonly >90%, there is little documented discrimination capability. This lack of discrimination capability leads to excessively high false alarm rates for both test site and live site surveys. Despite considerable advances in quantitative interpretation methods for discrimination, the state of practice is qualitative or empirical. The UXO thrust of the Army Engineer Research and Development Center's (ERDC) Environmental Quality Technology Program seeks to develop enhanced detection and discrimination capability for survey data from total field magnetometry, time-domain electromagnetic induction, and frequency-domain electromagnetic induction methods. Enhanced discrimination capability by formal geophysical inversion is demonstrated at documented test sites and live sites. A current emphasis is the development of formal inversion procedures that utilize the information content in multiple geophysical datasets. Two approaches are considered: (1) cooperative or constrained inversion; and (2) joint inversion. Cooperative inversion is the process of using inversion parameters from one dataset to constrain the inversion of other data. In true joint inversion, the target model parameters common to the forward models for each type of data are identified and the procedure seeks to recover the model parameters from all the survey data simultaneously. High-quality datasets acquired at seeded test sites at Former Fort Ord, California, demonstrate the confidence in applying these two approaches to discrimination of UXO from non-UXO targets.
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