case history unexploded ordnance discrimination using ...case history unexploded ordnance...

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Case History Unexploded ordnance discrimination using magnetic and electromagnetic sensors: Case study from a former military site Stephen D. Billings 1 , Leonard R. Pasion 2 , Laurens Beran 3 , Nicolas Lhomme 1 , Lin-Ping Song 3 , Douglas W. Oldenburg 3 , Kevin Kingdon 1 , David Sinex 3 , and Jon Jacobson 1 ABSTRACT 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 magnetome- ters, a time-domain electromagnetic induction EMI cart, an ar- ray of time-domain sensors, and a time-domain EMI cart with a wider measurement bandwidth. Discrimination was achieved us- ing rule-based or statistical classification of feature vectors ex- tracted 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 rela- tively 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 sig- nal-to-noise ratio, denser coverage, and more precise positioning of the EM-array data resulted in fewer false positives than the EMI cart. When depth constraints from the magnetometer data were used to constrain the EMI fits through cooperative inver- sion, discrimination performance improved considerably. The wide-band EMI sensor was deployed in a cued-interrogation mode over a subset of anomalies. This produced the highest- quality data because of collecting the densest data around each target and the additional late time-decay information available with the wide-band sensor. When the depth from the magnetome- ter was used as a constraint in the cooperative inversion process, all 4.2-inch mortars were recovered before any false positives were encountered. INTRODUCTION Unexploded ordnance UXO poses a major safety hazard in many parts of the world, including Canada, the United States, and Australia, where UXO is present at former and active military rang- es. During the past 10 years, significant research effort has been fo- cused on developing methods to discriminate between hazardous UXO and nonhazardous scrap metal, shrapnel, and geology e.g., Bell et al., 2001; Collins et al., 2001; Hart et al., 2001; Pasion and Oldenburg, 2001; Zhang et al., 2003a; Zhang et al., 2003b; Billings, 2004. The most promising discrimination methods typically pro- ceed by first recovering a set of parameters that specify a physics- based model of the object being interrogated. For example, in time- domain electromagnetic TEM data, the parameters are the object location and the polarization tensor typically two or three collocat- ed orthogonal dipoles along with their orientation and some parame- terization of the time-decay curve. For magnetics, the physics- based model is generally a static magnetic dipole. Once the parame- ters are recovered by inversion, a subset of the parameters is used as feature vectors to guide a statistical or rule-based classifier. There are significant advantages in collecting both types of data, including in- creased detection, stabilization of EM inversions by cooperative in- version of the magnetics Pasion et al., 2008, and extra dimension- ality in the feature space that may improve classification perfor- mance e.g., Zhang et al., 2003a. Manuscript received by the Editor 25 June 2009; revised manuscript received 16 September 2009; published online 20 May 2010. 1 Sky Research Inc.,Vancouver, British Columbia, Canada. E-mail: [email protected]; [email protected]; kevin.kingdon @skyresearch.com; [email protected]. 2 Sky Research Inc., Vancouver, British Columbia, Canada, and The University of British Columbia, Geophysical Inversion Facility, Vancouver, British Co- lumbia, Canada. E-mail: [email protected]. 3 The University of British Columbia — Geophysical Inversion Facility, Vancouver, British Columbia, Canada. E-mail: [email protected]; [email protected] .ca; [email protected]; [email protected]. © 2010 Society of Exploration Geophysicists. All rights reserved. GEOPHYSICS, VOL. 75, NO. 3 MAY-JUNE 2010; P. B103–B114, 9 FIGS. 10.1190/1.3377009 B103

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Page 1: Case History Unexploded ordnance discrimination using ...Case History Unexploded ordnance discrimination using magnetic and electromagnetic sensors: Case study from a former military

Case History

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GEOPHYSICS, VOL. 75, NO. 3 �MAY-JUNE 2010�; P. B103–B114, 9 FIGS.10.1190/1.3377009

nexploded ordnance discrimination using magnetic and electromagneticensors: Case study from a former military site

tephen D. Billings1, Leonard R. Pasion2, Laurens Beran3, Nicolas Lhomme1, Lin-Ping Song3,ouglas W. Oldenburg3, Kevin Kingdon1, David Sinex3, and Jon Jacobson1

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ABSTRACT

In a study at a military range with the objective to discriminatepotentially hazardous 4.2-inch mortars from nonhazardousshrapnel, range, and cultural debris, six different discriminationtechniques were tested using data from an array of magnetome-ters, a time-domain electromagnetic induction �EMI� cart, an ar-ray of time-domain sensors, and a time-domain EMI cart with awider measurement bandwidth. Discrimination was achieved us-ing rule-based or statistical classification of feature vectors ex-tracted from dipole or polarization tensor models fit to detectedanomalies. For magnetics, the ranking by moment yielded betterdiscrimination results than that of apparent remanence from rela-tively large remanent magnetizations of several of the seededitems. The magnetometer results produced very accurate depthsand fewer failed fits attributable to noisy data or model insuffi-

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iency. The EMI-based methods were more effective than theagnetometer for intrinsic discrimination ability. The higher sig-

al-to-noise ratio, denser coverage, and more precise positioningf the EM-array data resulted in fewer false positives than theMI cart. When depth constraints from the magnetometer dataere used to constrain the EMI fits through cooperative inver-

ion, discrimination performance improved considerably. Theide-band EMI sensor was deployed in a cued-interrogationode over a subset of anomalies. This produced the highest-

uality data because of collecting the densest data around eacharget and the additional late time-decay information availableith the wide-band sensor. When the depth from the magnetome-

er was used as a constraint in the cooperative inversion process,ll 4.2-inch mortars were recovered before any false positivesere encountered.

INTRODUCTION

Unexploded ordnance �UXO� poses a major safety hazard inany parts of the world, including Canada, the United States, andustralia, where UXO is present at former and active military rang-

s. During the past 10 years, significant research effort has been fo-used on developing methods to discriminate between hazardousXO and nonhazardous scrap metal, shrapnel, and geology �e.g.,ell et al., 2001; Collins et al., 2001; Hart et al., 2001; Pasion andldenburg, 2001; Zhang et al., 2003a; Zhang et al., 2003b; Billings,004�. The most promising discrimination methods typically pro-eed by first recovering a set of parameters that specify a physics-ased model of the object being interrogated. For example, in time-

Manuscript received by the Editor 25 June 2009; revised manuscript receiv1Sky Research Inc., Vancouver, British Columbia, Canada. E-mail: stephenskyresearch.com; [email protected] Research Inc., Vancouver, British Columbia, Canada, and The Univ

umbia, Canada. E-mail: [email protected] University of British Columbia — Geophysical Inversion Facility, V

ca; [email protected]; [email protected] Society of Exploration Geophysicists.All rights reserved.

omain electromagnetic �TEM� data, the parameters are the objectocation and the polarization tensor �typically two or three collocat-d orthogonal dipoles along with their orientation and some parame-erization of the time-decay curve�. For magnetics, the physics-ased model is generally a static magnetic dipole. Once the parame-ers are recovered by inversion, a subset of the parameters is used aseature vectors to guide a statistical or rule-based classifier. There areignificant advantages in collecting both types of data, including in-reased detection, stabilization of EM inversions by cooperative in-ersion of the magnetics �Pasion et al., 2008�, and extra dimension-lity in the feature space that may improve classification perfor-ance �e.g., Zhang et al., 2003a�.

eptember 2009; published online 20 May [email protected]; [email protected]; kevin.kingdon

f British Columbia, Geophysical Inversion Facility, Vancouver, British Co-

er, British Columbia, Canada. E-mail: [email protected]; [email protected]

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Results are presented here for a discrimination study pilot pro-ram conducted at the former Camp Sibert, Alabama, U.S.A., spon-ored by the Environmental Security Technology Certification Pro-ram �ESTCP�. The ability to separate munitions from nonhazard-us metallic items or geology is critical to reduce the cost of remedi-ting munitions-contaminated sites. Camp Sibert is particularly welluited for such classification efforts, which require collecting high-uality geophysical data. The site is relatively flat and clear of ob-tructions from vegetation or topography that would hinder survey-ng with cart-based geophysical sensors. In addition, the history ofhe site indicates there was primarily one type of ordnance deployedhere: 4.2-in mortars. The objective at Camp Sibert was to discrimi-ate potentially hazardous 4.2-inch mortars from nonhazardoushrapnel, range, and cultural debris.

Our case study focuses on the results achieved using methodolo-ies developed by ourselves and others �e.g., Pasion and Oldenburg,001; Billings, 2004; Beran and Oldenburg, 2008; Pasion et al.,008�. We provide an overview of the methodology and point theeader to relevant papers that describe the techniques in greater de-ail. We concentrate primarily on the results achieved — in particu-ar, the comparative performance of the different sensor modalitiesnd deployment modes. All results reported here were obtained us-ng commercial off-the-shelf �COTS� sensors. These include Geon-cs EM61 and EM63 time-domain metal detectors and an array ofeometrics cesium vapor magnetometers. See Gasperikova et al.

2009� for the results of applying a multistatic EMI sensor at theame site.

Because each COTS instrument measures only one component ofvector field, a measurement at a single location provides limited in-

ormation. Consequently, relatively dense 2D measurements are re-uired for accurate recovery of relevant target parameters. Theseeasurements must be very precisely positioned and oriented for

iscrimination to be successful. Data were acquired in full-coverageurvey mode �magnetometer array, EM61 cart, and EM61 array� andn cued-interrogation mode �EM63 cart�, whereby selected prelo-ated anomalies were surveyed densely in the immediate vicinity ofhe flagged anomaly location picked from a full coverage survey. Inddition to inverting each of the individual sets of data discussed, co-perative inversions also were undertaken for the towed EM61 arraynd the EM63 cued-mode data using the magnetometer data to con-train the EM inversions.

Here, we contrast the discrimination and localization abilities ofix sensor combinations:

� Magnetometer array deployed in full-coverage mode with di-pole moment size used for discrimination

� EM61 cart deployed in full-coverage mode �no orientation in-formation recorded�

� EM61 array deployed in full-coverage mode with position andorientation information recorded

� Cooperative inversion of EM61 array and magnetometer arraydata

� EM63 cart deployed in cued-interrogation mode with positionand orientation recorded

� Cooperative inversion of EM63 cart and magnetometer arraydata

STRUCTURE OF DISCRIMINATION STUDY

Our results are from a blind test of the discrimination performancef the different techniques; the ESTCP managed the discriminationtudy and withheld ground-truth information until after discrimina-ion declarations had been made. All results reported here, unlesstherwise indicated, were for the original submissions to the ESTCP.

An initial magnetometer survey of the Camp Sibert site was usedo select three different areas for the discrimination study: southeast

�SE1�, southeast 2 �SE2�, and southwest �SW�. Combined, thehree areas cover approximately 15 acres. A geophysical prove-outGPO� was established immediately adjacent to the SW area; it in-luded 30 4.2-inch mortars emplaced at different depths and orienta-ions and eight partial mortars, considered as nonhazardous scrap.he ESTCP emplaced 152 4.2-inch mortars in the three areas. The

ocations and depths of 29 of these mortars, along with the locationsnd identities of 179 clutter items, were provided to each demonstra-or and used as training data.

In full-coverage survey mode, data were collected using the stan-ard cart platform of the EM61-MK2 system. The Geonics EM61-K2 is a pulse-based time-domain electromagnetic instrument that

ecords data in four time windows centered at 0.216, 0.366, 0.660,nd 1.266 ms after pulse turn-off �e.g., Bosnar, 2001�. EM61-MK2art data were acquired by an onsite contractor using a line spacingf 50 cm, sensor height of 40 cm, and positions recorded with a real-ime kinematic global position system �RTK GPS�, accurate to with-n a few centimeters. Survey-mode data also were acquired for a

agnetometer array and an EM61-MK2 array using the multisensorowed-array detection system �MTADS�. The MTADS EM arrayonsists of three overlapping EM61-MK2 sensors �each 1 m widend 0.5 m long� that have a center-to-center separation of 0.5 mNelson et al., 2003�. Data were collected with a nominal across-rack sensor spacing of 50 cm, and the array was transported 25 cmbove the ground.

Figure 1 shows the MTADS EM data over the SW area with the lo-ation and identities of the ground truth marked. The MTADS mag-etometer array is composed of a platform housing a 2-m-wide in-ine array of eight G-822 cesium vapor magnetometers spaced5 cm apart and 25 cm above the ground �Nelson et al., 2003�. Forhe MTADS EM and magnetometer arrays, position and orientationnformation were obtained through centimeter-level RTK GPS andnertial-motion-unit �IMU� measurements.

The Geonics EM63 is a pulse-based multichannel TEM inductionnstrument �e.g., McNeil and Bosnar, 2000�. The system consists of

1�1-m-square transmitter coil and three coaxial 0.5�0.5--square receiver loops mounted on a two-wheeled trailer. Mea-

ured voltages are averaged over 26 geometrically spaced timeates, spanning 180 �s to 25.14 ms. The EM63 was deployed in aued-interrogation mode on a customized air-suspension cart. Theart lowered the instrument closer to the surface �from the standard0 cm to 20 cm� and absorbed some of the effects from instrumentostle resulting from an uneven ground surface. The late-time infor-

ation available from the 26 time channels provided an extendediew of target decays. Position information was collected by a LeicaPS 1206 robotic total station with orientation effects recorded us-

ng a Crossbow AHRS 400 IMU. We estimated that positions wereccurate to within 2–4 cm and orientation to within about 2°.

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DATA PROCESSING AND INTERPRETATIONMETHODOLOGY

Three steps are required to utilize geophysical data for unex-loded ordnance discrimination. First, we create a map of the geo-hysical sensor data; this includes all actions required to estimate theeophysical quantity in question �magnetic field, amplitude of EMIesponse at a given time channel, etc.� at each visited location. Sec-nd, we select the anomaly and extract features. This includes de-ecting anomalous regions and subsequently extracting a dipolemagnetics� or polarization tensor model �TEM� for each anomaly.

here magnetic and EMI data have been collected, the magneticata are used as constraints for the EMI model via a cooperative in-ersion process. Third, we classify anomalies. The objective of theemonstration is to produce a dig sheet with a ranked list of anoma-ies. We achieve this using statistical classification, which requiresraining data to determine the attributes of the UXO and non-UXOlasses.

rocess flow

Each demonstrator provided filtered, located geophysical data.o additional preprocessing was performed on any data set. At thisoint in the process flow, there was a map of each of the geophysicaluantities measured during the survey. The next step in the processas to detect anomalous regions, followed by extracting features for

ach detected item.Anomalous regions were detected by the demon-trators who provided the easting and northing coordinates corre-ponding to the maximum geophysical signature for each anomaly.he demonstrators used an automatic target-detection algorithm that

riggered a detection each time the sensor response exceeded ahreshold. The threshold value was set to one-half of the minimummplitude recorded over any of the 4.2-inch mor-ars in the GPO.

The inversion procedure assumes that we areealing with a single target in free space. Onceata anomalies are identified, we define a mask toepresent the spatial limits of the data to be invert-d. Unlike magnetics data, an unconstrained EMInversion is very sensitive to adjacent anomaliesnd to the size of the mask used in areas withoutearby anomalies. The masking procedure helpsnsure that signals from adjacent anomalies doot affect the inversion results. In addition, from aractical standpoint, inverting the minimumumber of observations reduces computing time.

We used an automated masking procedure thatts an ellipse to contours of the anomalous target.y using an ellipse, we recover a relatively

mooth mask that mimics the shape of the anoma-y. The main challenge is to find contours that aremooth as well as close to the noise level. Includ-ng good estimates of the background noise en-ures that we choose appropriate starting contouralues that are above the baseline error and en-ompass all anomalous data. Estimates of back-round noise were calculated independently forach time channel through the statistics in a 5�5m moving window that covered the entire dataet. To estimate the standard deviation of the

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oise, the sample skewness was calculated and the largest data sam-les were iteratively discarded until the skewness value was lesshan a user-defined threshold value �we used 0.25�. This process pro-ided an effective means to estimate the spatially �and temporally�arying background noise without significant distortion from theignals of interest.

We did not always include all time channels in the inversion pro-ess. For the EM61, we kept, at a minimum, the first three time chan-els; for the EM63, we kept, at a minimum, the first 10 time chan-els. Additional channels were included if the energy within theask at that time channel were at least twice the estimated noise lev-

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eature extraction for a magnetics sensor

For magnetics, the physics-based model most commonly used is aipole:

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�r� is the distance between the center of the object and the obser-ation point.Abound-constrained optimization problem is solved toxtract feature vectors from each anomaly �Branch et al., 1999�.

The magnetic remanence metric was calculated for each dipoleoment �Billings, 2004� by using an equivalent spheroid to repre-

ent a 4.2-inch mortar. The equivalent spheroid defines a dipole fea-ibility curve �the family of moments that an object of that length and

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iameter can produce by induced magnetization in the presence ofhe earth’s magnetic field�. We calculated the orientation that causeshe minimum difference �m between the moment of the ordnancend the moment determined through the inversion m. We then esti-ated the minimum percentage of remanent magnetization required

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eature extraction for a time-domain EMI sensor

In general, TEM sensors use a step-off field to illuminate a buriedarget. The currents induced in the buried target decay with time,enerating a decaying secondary field that is measured at the sur-ace. The time-varying secondary magnetic field b�t� at a location rrom the dipole m�t� can be approximated with a dipole as per equa-ion 1 except, in this case, the moment is a function of time. The di-ole induced by the interaction of the primary field b0 and the buriedarget is given by

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here M�t� is the target’s polarization tensor �e.g., Das et al., 1990;ell et al., 2001; Pasion and Oldenburg, 2001; Smith et al., 2004�.The polarization tensor governs the decay characteristics of the

uried target and is a function of the shape, size, and material proper-ies of the target. The polarization tensor is written as

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here we use the convention that L1�t1��L2�t1��L3�t1�, so that po-arization-tensor parameters are organized from largest to smallest.he polarization-tensor components are parameterized such that the

arget response can be written as a function of a model vector con-aining components that are a function of target characteristics. Par-icular parameterizations differ, depending on the instrument �num-er of time channels, time range measured, etc.� and the group im-lementing the work. Bell et al. �2001� solve for the components ofhe polarization tensor at each time channel, and this is the proceduree use for the four-channel EM61-MK2. For the EM63, we use theasion-Oldenburg �2001� formulation:

Li�t��ki�t�� i��� i exp�t/� i �5�

or i� �1,2,3�, with the convention that k1 �k2 �k3. For a body ofevolution, L2�L3 for a rodlike object �Pasion and Oldenburg,001� and L1�L2 for a platelike object.

A bound-constrained optimization problem is solved for the threeolarization tensor components, their three Euler angles, and thehree position coordinates of the source �Branch et al., 1999; Billingst al., 2002�.

eature extraction for cooperative inversion of TEMnd magnetic data

In cooperative inversion, multiple data are inverted sequentially;he results of the first inversion constrain the second �Pasion et al.,008�. This prior information can be introduced formally into theayesian formulation through the prior p�v�, where v is a vector ofodel parameters. Commonly utilized priors include Gaussian and

niform priors �i.e., a constant probability density function �PDF�or a parameter between two limits and zero probability outsidehese limits�. The solution to the inverse problem, given observedata dobs, utilizes a Gaussian prior with hard bounds:

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We used a three-step strategy for cooperatively inverting magnet-cs and electromagnetics data. First, the magnetics data were invert-d for a best-fit dipole. Second, the dipole location was used to definej �for j�1, 2, and 3 that correspond to the easting, northing, andepth of the dipole� and the standard deviation of the parameter un-ertainties used to define j. The estimated model-parameter stan-ard deviations were obtained from the Gauss-Newton approxima-ion to the Hessian at the optimum model location �e.g., Billings etl., 2002�. Third, we inverted the EM data using the prior obtainedrom the magnetics data in step 2.

Inevitably, there were anomalies in the TEM data that did not haveorresponding magnetic fits, and vice versa. Where no constraintsrom magnetometer data were available, the TEM data were invert-d using the same procedure as for single inversion.

ata-quality considerations using a figure of meritFOM)

In all data sets, there are a number of targets whose observed fitbtained through the inversion process is unsatisfactory and must beailed. These failed fits were classified as “can’t analyze” and wouldave resulted in a significant number of excavations. We thereforenvestigated methods to reduce the number of items that needed to bexcavated, hoping to maintain the same probability of correct classi-cation.Close scrutiny of failed inversions and inversions that yielded in-

ccurate depth estimates on the GPO and ground-truth targets re-ealed that poor inversions could be tied to certain features of theata or the inversion. This motivated us to identify and establishules to define a confidence factor for a given inversion. We consid-red several criteria that we gathered under a so-called figure of mer-t �FOM�, which comprises the following data features:

Signal-to-noise ratio �S/N� should be above a given threshold forreliable inversion of each time channel; S/N should decay withtime if the sensor operates properly and noise estimates are accu-rate.Data coverage of anomaly should sample the spatial decay of the

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EM scattered field to allow recovery of orthogonal polarizations.

he data inversion features include the following:

Quality of fit — misfit, correlation coefficient.Variance of estimated depth — there can be several solutions ofthe inverse problem with similar misfits but distributed over alarge range of depth.

ull details and quantitative descriptions of each FOM metric arerovided in Lhomme et al. �2008�.

iscrimination methodology

For the magnetic data, the primary discrimination diagnostic wasased on the size of the dipole moment �with a preference to digtems with larger moments�. For test purposes, we compiled a sec-nd discrimination ranking using the apparent magnetic remanenceequation 2�. Data from the GPO and the initial release of trainingata were used to determine appropriate thresholds for dipole sizend remanence. The dipole-size threshold was selected based on theinimum moment of a 4.2-inch mortar encountered in the GPO and

raining data, with an extra safety margin built in to account for errorn estimating the depth of the dipole �which could cause an underes-imate of the size of the moment�. The remanence threshold was sett 10% larger than the maximum apparent remanence encountered inhe GPO and training data.

For the statistical classification, we used data over the GPO andhe initial training grids to determine which feature vectors and sta-istical classifier to use. A probabilistic neural network �PNN� wasrained on a size and a time-decay feature extracted from anomaliesn the GPO �thirty 4.2-inch mortars and eight cutter items� and theartial release of ground truth �29 mortars and 179 clutter items�.he PNN classifier models the underlying statistical distributions assuperposition of Gaussian kernel functions centered on each train-

ng vector, with the kernel width set to the standard deviation of theeature vectors in the UXO class. The size feature was chosen as thease-10 logarithm of the maximum instantaneous polarization atime channel 1 for the EM61 and the base-10 logarithm of the largestasion-Oldenburg k-parameter for the EM63. Relative time decaysere calculated as the ratio of the principal polarizations of the third

nd first time channels for the EM61 and as the ratio of the principalolarizations at the fifteenth and first time channels for the EM63.he size and time-decay features were then normalized so they hadero mean and unit variance. Stop-digging points were selected byisually inspecting the training data and the PNN probability sur-ace.

From the extracted feature vectors, the following five statisticallyased dig sheets were produced:

� MTADS-EM61, using statistical classification of features de-rived from the MTADS-EM61 data

� Contractor EM61, using statistical classification of features de-rived from the Contractor’s EM61 data

� EM63 cued, using statistical classification of features derivedfrom the EM63 cued-interrogation data

� MTADS-EM61 and magnetics as for dig sheet 1 but withMTADS-EM61 fits constrained by the magnetics data and add-ing features from the magnetometer data �remanence, moment,etc.�

� EM63 and magnetics as for dig sheet 4 but with the EM63.

RESULTS FROM CAMP SIBERT

urvey-mode data

Figure 2 plots the two feature vectors for the MTADS magnetics,M61 cart, MTADS-EM61, and MTADS-EM61 cooperative dataets over the decision surface, with separate plots for the training andest data. For magnetics, the training data revealed several 4.2-inch

ortars with relatively large remanence �Figure 2a�.All mortars hadoments greater than 0.17 Am2 and could therefore be considered

arge. Consequently, the size of the moment �not the remanence� wassed to prioritize the dig list. We also produced a remanence priori-ized dig list so that we could test performance using that discrimina-ion metric. Assuming a 5-cm error in the depth estimate of the di-ole moment at the surface, we anticipated that all mortars would beecovered with a threshold of 0.109 Am2, which we decreased to.1 Am2 for an extra safety margin. In other words, we recommend-d digging all anomalies with moments greater than 0.1 Am2.

Feature vectors within the UXO, partial rounds, and base-platelasses are more tightly clustered for the MTADS-EM61 than for theM61 cart, with further improvement evident in the cooperatively

nverted MTADS-EM61. This indicates higher-quality data ob-ained using the MTADS-EM61 array, which has the additional ca-ability over the EM61 cart of recording sensor orientation by incor-orating IMU and collecting orthogonal sets of survey lines. Furthermprovements are obtained when the EM61-MTADS is coopera-ively inverted because incorporating the MTADS magnetometer

easurements considerably improves the accuracy of the estimatedepths. For all three data sets in Figure 2, we used two decisionoundaries: an aggressive one for high FOM anomalies and a lessggressive one for low FOM anomalies. For each of the three dataets, all 4.2-inch mortars lay on the appropriate side of the “high-onfidence UXO” decision boundary.

Figure 3 compares the magnetometer, EM61, MTADS-EM61,nd MTADS-EM61 cooperative receiver operating characteristicROC� curves. The horizontal axis shows the number of unnecessaryxcavations required �items that had to be dug but were not 4.2-inchortars� with the vertical axis, showing the proportion of mortars in

he test set that were recovered. A solid dot represents the operatingoint �i.e., the stop-digging point� of each classification method.ome anomalies had insufficient S/N or data coverage to constrain

he TEM model parameters. This included anomalies with overlap-ing signatures that could not be isolated and inverted one at a time.ll such anomalies were labeled “can’t analyze” on the dig sheet and

xcavated as potential UXO. When including the “can’t analyze”ategory �Figure 3a�, the magnetometer data require the fewest num-er of false-positive �FP� excavations at its operating point �170ompared to 264, 344, and 275 for the EM61, MTADS-EM61, andTADS-EM61 cooperative�. For the non-UXO items, 72% can be

eft in the ground with the magnetometer data, compared to 33%,4%, and 55% for the other data sets. When excluding the “can’t an-lyze” category �Figure 3b�, it is evident that feature vectors extract-d from the MTADS-EM61 and MTADS-EM61 cooperatively in-erted data are more highly discriminatory than the magnetometer orM61.

ddressing the problem of the large number of “can’tnalyze” anomalies

The MTADS-EM61 and MTADS-EM61 cooperative algorithmsad to deal with many geologic detections that could not be fit by the

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dipole model and hence had to be listed as “can’tanalyze.” These extra false detections caused theMTADS classifiers to have poorer performancethan the magnetometer and EM61 cart when“can’t analyze” anomalies were included. Mostof these geologic alarms were caused by thebouncing movement of the cart as it traversed fur-rows in the plowed field on the southwest sectionof the site. When the distance between the cartand the ground decreased, there was an increasein the amplitude of the response from the soil,which often was large enough to trigger a falsedetection.Ascatter plot of the total energy of timechannel t1 of the east-west versus north-southtransects �Figure 4a� shows that most of the geo-logic anomalies have 18–23 dB of energy north-south �perpendicular to furrows� compared to5–18 dB east-west �parallel to furrows�.

To reduce the number of geological anomalies,we decided to try to develop a metal/soil discrimi-nator based on the relative energy in the two di-rections. This was to be applied as a prescreenerbefore submitting the anomaly to the polarizationtensor-fitting routines. We used the GPO and ini-tial training data to investigate different potentialclassifiers and used two classes: one comprisingthe geologic anomalies and the other comprisingall metallic anomalies minus fragmentation �theS/N of anomalies resulting from fragmentationwas generally very low, and there often were sig-nificant differences between the north-south andeast-west transects�. After initial experimenta-tion, we settled on a quadratic discriminant analy-sis classifier because it produced the most intu-itively reasonable discrimination boundary. Wethen plotted the cumulative distributions of thedifferent classes against the metal probabilityPmetal �Figure 4c�. Approximately 50% of geolog-ic anomalies had Pmetal � 0.3, with the first UXOoccurring at Pmetal�0.4. We therefore selected anoperating point of Pmetal � 0.3.

Figure 4b shows a feature vector plot of theblind-test data, and Figure 4d shows cumulativedistributions of the different classes.At the select-ed operating point, 55% of geologic anomalieswould be rejected, along with 14% of shrapnel,8% of scrap metal, and less than 3% of partialrounds and base plates. Most importantly, noUXO would be rejected by the prescreener. Itwould have excluded 119 geologic anomaliesfrom further analysis: 116 of these were in the“can’t analyze” category and had to be excavated.In total, the prescreener could have reduced thenumber of “can’t analyze” anomalies by 130�from 285 to 155�. For the MTADS-EM61 coop-erative inversion, the reduction would havedropped from 226 to 116.

Note that this prescreener was applied retro-spectively and was not used for the initial submis-sion of dig sheets.

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) d)

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igure 2. Scatter plot of moment versus remanence for training data �left colata �right column�. �a, b� MTADS magnetometer. Vector plots for �c,M61, �e, f� MTADS-EM61, and �g, h� MTADS-EM61 cooperative data slassifier probability is shown as a grayscale image. Black represents featurequal probability of being in either class. The color scale grades to white foron-UXO classes. For the magnetics methods, stop-digging thresholds areashed lines �blue for moment, green for remanence�, with the minimum vaered in the training data delineated by solid lines.

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UXO discrimination at a military site B109

ued-interrogation-mode data

The EM63 was deployed in a cued-interrogation mode over a sub-et of anomalies; the blind-test data comprised 150 items that con-ained 34 UXO. Statistical classification results are presented forM63 data and EM63 cooperatively inverted data in Figures 5 and 6.s with the EM61 data sets, discrimination was based on a PNN

lassifier with a size-based and a time-based feature vector. The size-ased feature vector was the k1-parameter from the primary polariza-ion, with the ratio of primary polarizations at the fifteenth and firstime channels used as the time-decay parameter. The UXO, partialounds, and base-plate classes are tightly clustered in both data sets,ith fewer variation in the cued-interrogation data �Figure 5�. TheXO class for the EM63 data contain two outliers, one of which

auses the false negative evident in Figure 5b.At the selected operat-ng point, the EM63 statistical classification required 38 false posi-ives �30 of these were “can’t analyze”�, but one 4.2-inch mortar was

issed.The EM63 false negative was from a 4.2-inch mortar buried at

0 cm that was predicted to be at 5 cm. The shallower solution depthnderestimated the size of the item and resulted in the feature vectorying outside the main cluster of the 4.2-inch mortar class. Inspec-ion of the observed data and model fit reveals a very poor fit to onef the transverse lines in the cued-interrogation data �Figure 7�. Thisine of bad data should have been identified and removed so that thealse negative would have been a consequence of a quality-controlQC� failure. A misfit versus depth plot �Figure 8� reveals two mini-a: one at the shallow solution and another close to the true depth of

0 cm. The shallower solution has a slightly lower misfit and hences selected by the optimization algorithm.After removing the corruptine of data, the misfit of the shallow solution increases and the deep-r solution is preferred �Figure 8�. A better size estimate results, andhe feature vector for the anomaly lies within the same tight clusters the rest of the mortars. When the depth constraints from the mag-etometer were used, the deeper solution was also preferred and thealse negative was eliminated. This example demonstrates the im-ortance of careful QC and the benefits of the cooperative inversionrocedure. Even a simple automated QC process that flags residualsbove a certain threshold would have identified this anomaly.

When the depth from the magnetometer was used as a constraintn the cooperative inversion process, the false negative did not occur.n fact, the EM63 cooperative produced a perfect ROC curve, withll 34 UXO recovered and zero false alarms. At the selected operat-ng point, 21 false positives were required, with 16 of these in thecan’t analyze” category. In Figure 6, we also show an ROC curveor the MTADS-EM61 cooperative when restricted to the same 150ued-interrogation anomalies. It results in a perfect ROC curve butequires 34 FP at the operating point, with six of those in the “can’tnalyze” category.

ccuracy of inverted depths

Scatter plots of the predicted versus ground-truth depths for eachf the six different sensor combinations are shown in Figure 9. Theres excellent agreement between estimated and actual depths for the

agnetometer, with 85% within 10 cm and 96% within 20 cm. TheM61 cart and MTADS-EM61 display a much larger scatter in actu-l and predicted depths — 74% and 76%, respectively, within0 cm. For the EM61, the estimated depths of the deeper 4.2-inchortars are particularly poor and contribute to the relatively wide

ange of estimated size observed for that class. The EM61 cart andTADS-EM61 have a tendency to predict deeper depths for small,

hallow items.Cooperative inversion considerably improves the accuracy of the

stimated depths: 75% of MTADS-EM61 depths are within 10 cmompared to 53% when inverted without magnetometer depth con-traints. The performance gain is from 75% to 87% within 10 cmhen the EM63 data are cooperatively inverted.

DISCUSSION

At the Camp Sibert site, the objective was to discriminate poten-ially hazardous 4.2-inch mortars from nonhazardous shrapnel,ange, and cultural debris. We have described the performance of sixifferent discrimination techniques that use data from the MTADSagnetometer array, a Geonics EM61 cart, the MTADS-EM61 ar-

ay, and the Geonics EM63. From the extracted feature vectors, sev-

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igure 3. Comparision of ROC curves for the magnetics �moment�,ontractor EM61, MTADS-EM61, and MTADS-EM61 cooperativeata sets: �a� including “can’t analyze” category; �b� excludingcan’t analyze” anomalies. The values for probability of detectionnd false positives are discriminatory �disc�.

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igure 4. Reduction in geologic false alarms in theTADS-EM61 data using the difference in the en-

rgy in the north-south versus east-west lines. �a�raining-data feature vectors �energy inside mask�verlying classifier decision surface. �b� The sames �a� but for test data. �c� Cumulative distributionsn the training data for different categories whenanked by metal probability. �d� The same as �c� butor test data.

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n different prioritized dig lists were created: magnetics ranked byoment, magnetics ranked by remanence, EM61 statistical classifi-

ation, MTADS-EM61 statistical classification, MTADS-EM61 co-perative inversion and statistical classification, EM63 statisticallassification, and EM63 cooperative inversion and statistical classi-cation.In the blind-test data set, 119 seeded 4.2-inch mortars were left.

ach of the seven methods successfully discriminated the mortarsrom nonhazardous items. For magnetics, the ranking by momentas better than that of remanence because of relatively large rema-ent magnetization of several of the seeded items.At the selected op-rating point, 76% of nonhazardous items were left in the ground andll mortars were recovered. Statistical classification of the contrac-or-collected EM61 data resulted in recovering all 4.2-inch mortars,ith 59% of nonhazardous items left in the ground. For the MTADS-M61 and MTADS-EM61 cooperative processes, all mortars were

ecovered, with 52% and 72%, respectively, of nonhazardous itemsnexcavated. The MTADS-EM61 results were degraded by theany “can’t analyze” anomalies that had to be excavated as suspect-

d UXO. Most of these were geologic artifacts caused by cart bounces the MTADS-EM61 array traversed perpendicular furrows in a re-

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ently plowed field. Retrospective analysis revealed that many ofhese “can’t analyze” anomalies could have been eliminated usingn FOM or a geologic prescreener. The prescreener was based on atatistical classification of feature vectors related to the energy in therst time channel in the east-west versus north-south transects of theTADS-EM61 array. More than 55% of the geologic anomalies

ould be rejected because they had significantly more energy north-outh �perpendicular to the furrows� than east-west �parallel to theurrows�.

By excluding the “can’t analyze” category, we revealed the excel-ent intrinsic discrimination ability of the EM methods, particularlyhe MTADS-EM61 cooperative inversion. All 119 mortars were re-overed with just three false positives, compared to 75, 25, and 8alse positives for the magnetometer, EM61, and MTADS-EM61,espectively. Stop-digging points were very conservative, with 103,15, 59, and 70 false positives for the magnetometer, EM61,TADS-EM61, and MTADS-EM61 cooperative, respectively.The EM63 was deployed in a cued-interrogation mode over a sub-

et of anomalies; the blind-test data comprised 150 items that con-ained 34 UXO. At the selected operating point, the EM63 statisticallassification required 38 false positives, but one 4.2-inch mortaras missed. The failure was caused by a corrupt line of data that re-

igure 6. Comparision of ROC curves for the EM63 and EM63 co-perative data sets: �a� including the “can’t analyze” category; �b�xcluding the “can’t analyze” anomalies. MTADS-EM61 coopera-ive results are also shown for comparison. The values for probabili-y of detection and false positives are discriminatory �disc�.

Foett

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ulted in a small, shallow solution with a lower misfit than an alterna-ive second solution closer to the true depth of burial. When theepth from the magnetometer was used as a constraint in the cooper-tive inversion process, the false negative did not occur. In fact, theM63 cooperative produced a perfect ROC curve, with all 34 UXO

ecovered with zero false alarms. At the selected operating point, 21alse positives were required, with 16 of these in the “can’t analyze”ategory.

The depth predictions of the magnetometer data are very accurate,ith more than 85% within 10 cm and 96% within 20 cm of the trueepths. The EM61 and MTADS-EM61 display a much larger scattern actual and predicted depths, with 74% and 76%, respectively,

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igure 7. �a� Data, �b� model, and �c� residual of therst time-channel dipole model fit to anomaly 649

n the EM63 data. �d� Profile of the actual and fittedata in time channel 1 along the white line in viewsa�–�c�.

igure 8. Misfit versus depth curve for the EM63 Pasion-Oldenburgodel fit to anomaly 649. Two cases are considered: using all data

hat produce a shallow minimum close to the surface and after re-oving a “bad” line of data wherein the minimum occurs very close

o the true depth of 40 cm �black dashed line�.

ithin 20 cm. For the EM61, the estimated depths of the deeper 4.2inch mortars are particularly poor and contribute to the relativelyide range of estimated size observed for that class. The EM61 andTADS-EM61 tend to predict deeper depths for small, shallow

tems.Cooperative inversion considerably improves the accuracy of the

stimated depths: 75% of MTADS-EM61 depths are within 10 cm,ompared to 53% when inverted without magnetometer depth con-traints. The performance gain is 75%–87% within 10 cm when theM63 data are cooperatively inverted. The better-constrainedepths of the cooperatively inverted EM models result in less scattern the polarizabilities and improved discrimination ability.

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CONCLUSION

It is feasible to use magnetic and EMI data to reduce the number ofonhazardous items requiring excavation when attempting to clear aite contaminated with unexploded 4.2-inch mortars. The muchicher information content of the EMI data sets compared to magne-ometry results in better intrinsic discrimination performance, al-hough some of the EMI data sets suffer from many “can’t analyze”nomalies. Many of these were geologic artifacts caused by cartounce as the sensor system traversed across furrows in a plowedeld. Discrimination performance improved significantly when theepths of the EMI polarization models were constrained using mag-etometry and/or as EMI data quality and information content in-reased �denser coverage, longer decay time measured, more accu-ate positions�.

REFERENCES

ell, T. H., B. J. Barrow, and J. T. Miller, 2001, Subsurface discrimination us-ing electromagnetic induction sensor: IEEE Transactions on Geoscienceand Remote Sensing, 39, 1286–1293.

eran, L., and D. W. Oldenburg, 2008, Selecting a discrimination algorithmfor unexploded ordnance remediation: IEEE Transactions on Geoscienceand Remote Sensing, 46, 2547–2557.

illings, S. D., 2004, Discrimination and classification of buried unexplodedordnance using magnetometry: IEEE Transactions on Geoscience and Re-

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Fitteddepth(cm)

Fitteddepth(cm)

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mote Sensing, 42, 1241–1251.illings, S. D., L. R. Pasion, and D. W. Oldenburg, 2002, Discrimination andidentification of UXO by geophysical inversion of total-field magneticdata: U. S. Army Engineer Research and Development Center ReportERDC/GSL TR-02-16, http://el.erdc.usace.army.mil/uxo/pdfs/trg02-17.pdf, accessed 14 February 2010.

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ollins, L., Y. Zhang, J. Li, H. Wang, L. Carin, S. Hart, S. Rose-Pehrsson, H.Nelson, and J. R. McDonald, 2001, A comparison of the performance ofstatistical and fuzzy algorithms for unexploded ordnance detection: IEEETransactions on Fuzzy Systems, 9, 17–30.

as, Y., J. E. McFee, J. Toews, and G. C. Stuart, 1990, Analysis of an electro-magnetic induction detector for real-time location of buried objects: IEEETransactions on Geoscience and Remote Sensing, 28, 278–287.

asperikova, E., T. J. Smith, H. F. Morrison, A. Becker, and K. Kappler,2009, UXO detection and identification based on intrinsic target polariz-abilities —Acase history: Geophysics, 74, no. 1, B1–B8.

art, S. J., R. E. Shaffer, S. L. Rose-Pehrsson, and J. R. McDonald, 2001, Us-ing physics based modeler outputs to train probabilistic neural networksfor unexploded ordnance classification in magnetometry surveys: IEEETransactions on Geoscience and Remote Sensing, 39, 797–804.

homme, N., D. W. Oldenburg, L. R. Pasion, D. B. Sinex, and S. D. Billings,2008, Assessing the quality of electromagnetic data for the discriminationof UXO using figures of merit: Journal of Engineering and EnvironmentalGeophysics, 13, 165–176.cNeil, J. D., and M. Bosnar, 2000, Geonics Ltd. Technical Note TN-32,http://geonics.com/pdfs/technicalnotes/tn32.pdf, accessed 14 February2010.

elson, H. H., T. H. Bell, J. R. McDonald, and B. Barrows, 2003, Advanced

0 100 120

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Figure 9. Scatter plot of fitted versus ground-truthdepths for �a� magnetometer, �b� EM61, �c�MTADS-EM61, �d� MTADS-EM61 cooperative,�e� EM63, and �f� EM63 cooperative. The dashedlines represent errors of �15 cm.

60 8

0 8

h depth

60 8

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