In recent years great strides have been made on the development, demonstration, production, and commercialization of advanced electromagnetic induction (EMI) systems for detection, localization, and classification of subsurface unexploded ordnances (UXO). These devices, such as commercial MM2x2, APEX, UltraTEM and others., have an unprecedented spatial resolution and a spectral range multi-static geophysical vector data set by providing arrays of transmitters and receivers in precisely known configurations. These systems, however, tend to be large and heavy and cannot be readily used in challenging terrain that does not allow vehicular access, such as marshy wetland, wooded and rocky areas. To overcome this limitation, here we introduce a new Ultra-Light Electromagnetic Array (ULEMA) for subsurface UXO detection and classification. The system consists of three 35 cm diameter small and one large transmitter loops, four receivers, weighing at about 7.5 pounds. The three small Tx coils, that produce up to 35 A-m2 moment, are designed to illuminate targets from different sites, and the large Tx coil, with up to 45 A-m2 moment, is used to enhance detection, location, and identification of deep targets. When the excitation pulse is turned off, the Rxs collect target responses at a sample rate of 500 kHz. The system operates in both static (cued) and dynamic modes, and outputs the raw transient decay measurements grouped into 30 logarithmically spaced time gates whose center times range from 20 μs to 10 ms. In the dynamic mode the system collects a series of closely spaced high quality data set whose processing with advanced classification models results in a fully classified dig list. In this presentation, detection, and classification performance of the ULEMA system will discussed and demonstrated.

Electromagnetic induction has been utilized in the past by the United States Army Corps of Engineers as a method of detecting unexploded ordinance. Recently an EMI instrument was built that extended the traditional EMI frequency range from 100 kHz to 15 MHz to aid in the detection of nonmetallic ordinance, landmines, and improvised explosive devices. Extending that research, the iFROST mapper was built to use the same HFEMI technique to characterize arctic soil and subsurface permafrost deposits. This paper details the original iFROST mapper software and hardware systems as well as a new HFEMI device that improves on the original iFROST mapper design.

The in-situ physical properties of soils are the basis of modeling, inspection, and design for many different fields with applications including agriculture, civil infrastructure, environmental investigations, engineering design, and military based forward mobility. Yet obtaining accurate in-situ physical properties through direct contact field measurements, and at a spatially meaningful distribution, can be difficult given access restrictions and the inherent lateral inhomogeneities of the near surface. Most commonly, this information has been acquired through direct physical measurements on point source data, e.g. soil sampling or cone penetrometer tests. Geoelectrical geophysical methods, such as electrical resistivity tomography (ERT) and electromagnetic induction (EMI), continue to gain traction in these communities as they provide spatially continuous information that when paired with the physical measurements often better characterizes the lateral extent of the investigation area.

Electromagnetic induction (EMI) instruments have been traditionally used to detect high electric conductivity discrete targets such as metal unexploded ordnance (UXO). The frequencies used for this EMI regime have typically been less than 100 kHz. To detect intermediate conductivity objects like carbon fiber, even less conductive saturated salts, and even voids embedded in conducting soils, higher frequencies up to the low megahertz range are required in order to capture characteristic relaxation responses. In this context, nonconducting lastic landmines can be considered a void plus small metallic parts such as the firing pin. To predict EMI phenomena at frequencies up to 15MHz, we modeled the response of conducting and nonconducting targets using the the Method of Auxiliary Sources. Our high-frequency electromagnetic induction (HFEMI) instrument is able to acquire EMI data at frequencies up to that same high limit. Modeled and measured characteristic relaxation signatures compare favorably and indicate new sensing possibilities in a variety of scenarios including the detection of voids and landmines.

Unexploded Ordnances (UXO) classification procedure consists of the following: background subtractions, data inversions and targets feature parameters estimations, and separating UXO from non-hazardous anomalies. First, each dataset is normalized by a corresponding Tx-current; then, all data files are background subtracted; third, the background corrected data are inverted and targets intrinsic (effective polarizabilities) and extrinsic (locations) are extracted; next, the extracted intrinsic and extrinsic parameters are used for generating prioritized and training targets lists; Finally, once the ground truth for training targets are provided, then prioritized targets are reclassified and final dig list is created. In this paper, the detailed steps of UXO classification procedure using the advanced EMI sensors and models are presented along with the processing and analysis approaches that are used to generate a prioritized dig list.

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2011The ortho normalized volume magnetic source technique applied to live‐site UXO data: Inversion and classification studiesAuthors: Fridon ShubitidzeBen BarrowesIrma ShamatavaJuano Pablo FernándezKevin O'NeillFridon ShubitidzeThayer School of Engineering Dartmouth College/Sky REsearch, Hanover NH, 03755Search for more papers by this author, Ben BarrowesUSA ERDC Cold Regions Research and Engineering Laboratory, Hanover, NH 03755, USASearch for more papers by this author, Irma ShamatavaSky Research Inc./Dartmouth College Etna NH, 03750Search for more papers by this author, Juano Pablo FernándezThayer School of Engineering Dartmouth College, Hanover NH, 03755Search for more papers by this author, and Kevin O'NeillUSA ERDC Cold Regions Research and Engineering Laboratory, Hanover, NH 03755, USASearch for more papers by this authorhttps://doi.org/10.1190/1.3627990 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract The orttho‐normalized volume magnetic source technique (ONMVS) [1] is applied to Camp Butner, NC, live‐site UXO MetalMapper data inversion and subsurface metallic target discrimination. The ONVMS model can be considered as a generalized surface dipole model, and in fact reverts to the point dipole model as a limiting case. The method is based on the assumption that a collection of scatterers can be replaced with a set of magnetic dipole sources, distributed over a volume. These sources mimic the eddy currents and thereby the magnetic response that are induced on the targets by the primary magnetic field, and that in turn establish the observable secondary field. In this study, twenty‐four hundred anomalies were processed. The anomaly sets included three types of UXOs: M48 Fuze, 105 mm and 37 mm projectiles. The effective total ONVMS amplitudes were used to discriminate UXO's from metallic clutter. The amplitudes of the total ONVMS were determined for each anomaly along three orthogonal axes by inverting MetalMapper data using the combined ONVMS and differential evolution algorithm. The inverted anomalies were ranked as UXO and non‐UXO targets and submitted to the Institute for Defense Analyses (IDA) for independent scoring. The independent scoring results that are presented here, suggest that the ONVMS technique has the potential to improve UXO classification.Permalink: https://doi.org/10.1190/1.3627990FiguresReferencesRelatedDetailsCited byImproved Differential Evolution Algorithm for Multi-Target Response Inversion Detected by a Portable Transient Electromagnetic SensorIEEE Access, Vol. 8Setting the stop dig point for unexploded ordnance remediationValidation of Advanced EM Models for UXO DiscriminationIEEE Transactions on Geoscience and Remote Sensing, Vol. 51, No. 7Practical strategies for classification of unexploded ordnanceLaurens Beran, Barry Zelt, Leonard Pasion, Stephen Billings, Kevin Kingdon, Nicolas Lhomme, Lin-Ping Song, and Doug Oldenburg11 December 2012 | GEOPHYSICS, Vol. 78, No. 1Regularizing dipole polarizabilities in time-domain electromagnetic inversionJournal of Applied Geophysics, Vol. 85 SEG Technical Program Expanded Abstracts 2011ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2011 Pages: 4424 Publisher:Society of Exploration Geophysicists HistoryPublished Online: 25 May 2012 CITATION INFORMATION Fridon Shubitidze, Ben Barrowes, Irma Shamatava, Juano Pablo Fernández, and Kevin O'Neill, (2011), "The ortho normalized volume magnetic source technique applied to live‐site UXO data: Inversion and classification studies," SEG Technical Program Expanded Abstracts : 3766-3770. https://doi.org/10.1190/1.3627990 Plain-Language Summary PDF DownloadLoading ...

: SERDP project MR-1664 entitled Isolating and Discriminating Overlapping Signatures in Cluttered Environments is approximately halfway complete. Significant progress has been made in working toward the original objectives of the project. Three new methods for localizing multiple sources in close proximity using EMI data have been developed and tested. Specifically, these methods are: A multiple dipole search method based on a gradient search algorithm utilizing an analytical Jacobian (see Sec. 4.2) A combined Joint Diagonalization (JD) and Orthonormalized Volume Magnetic Source (ONVMS) method (see Sec. 4.3) Source localization based MUSIC algorithm applied to EMI data (see [1]) Canonical targets of various shapes, sizes and material parameters have been fabricated (see Sec. 4.1. Data acquired from these targets as well as standard UXO targets has been acquired by the TEMTADS and MPV2 instruments in many multitarget configurations (see Sec. 4.1). The methods developed to date under this MR-1664 have been able to isolate and discriminate up to six targets simultaneously in the case of lab data (see Sec. 5). The JD method is able to almost instantaneously provide a good estimate for the number of distinct targets in the EMI data. After this estimate is obtained, the first or second methods delineated above (and described below) are used to invert for the parameters of the N identified targets. Benjamin Barrowes,

The underlying physics of low frequency EMI scattering phenomena in underwater environments from highly conducting and permeable metallic objects is analyzed using an approach that combines the method of auxiliary sources and a surface impedance boundary condition. The combined algorithm solves EMI boundary-value problems by representing the electromagnetic fields in each domain of the structure under investigation by a finite linear combination of analytical solutions of the relevant field equations, corresponding to elementary sources situated a small distance away from the boundaries of each domain. Numerical experiments are conducted for homogeneous and multilayer targets of canonical (spheroidal) shapes subject to frequency- or time-domain illumination, as well as for heterogeneous UXO like targets, to demonstrate: (a) how marine environments change EMI sensor performance and associated processing approaches for detecting highly conducting and permeable metallic objects underwater, and (b) what are the EMI sensors detectability limits. Near and far EMI field and induced eddy-current distributions are presented to help gain insight into underwater EMI scattering phenomena. Particularly, the results illustrate coupling effects between the object and its surrounding conductive medium, especially at high frequencies (early times for time-domain sensors). The results also suggest that this coupling depends on the object's material properties, the conductivity of the medium, and the distance between the sensor and the object's center.

Recently, new generation, relatively sophisticated, ultra wideband EMI sensors with novel waveforms and multi-axis or vector receivers, have been developed which operate either in the time domain or in the frequency domain. Among these emerging technologies is the Time-domain Electromagnetic Multi-sensor Tower Array Detection System (TEMTADS). The system consists of 25 transmit/receive pairs arranged in a 5 × 5 grid, each with a square 35-cm diameter transmitter coil and a concentric square 25-cm receiver coil. The sensor activates the transmitter loops in sequence, and for each transmitter all receivers receive, measuring the complete transient response over a wide dynamic time range going approximately from 100 μs to 25 ms and distributed in 123 time gates. Thus it provides 625 data points at each location, without the need for a relative positioning system due to its fixed geometry. The combination of spatial diversity in the measurements and well-located sensor positions offers unprecedented data quality for discrimination processing algorithms. To take advantage of the data diversity that this instrument provides, we will use both of the following in an analysis of data acquired with the TEMTADS at Aberdeen Proving Ground (APG) in 2008: (1) advanced, physically complete EMI forward models such as the normalized surface magnetic source (NSMS) model and (2) a data-inversion scheme that uses the newly developed HAP method to estimate the location of a target. Initially the applicability of the NSMS and HAP algorithms to TEMTADS data sets are demonstrated by comparing the modeled data to test-stand and calibration data, and then the APG blind discrimination studies are conducted using as discrimination parameters the total NSMS and principal axes of the induced magnetic polarizability tensor for each target. The classification is done on the extracted feature vector via statistical classification tools.

In this paper a physically complete model called the Normalized Surface Magnetic Source (NSMS) model is applied to data collected using the Berkeley UXO Discriminator time-domain sensor. The sensor has three pairs of rectangular transmitters and eight pairs of receivers that measure gradients of scattered fields. The system is cart-based and produces well-located EMI data sets. In order to take advantage of this high quality data the NSMS technique is utilized for the BUD instrument. The NSMS is a very simple and robust technique for predicting the EMI responses of various objects. The technique is applicable to any combination of magnetic or electromagnetic induction data for any arbitrary homogeneous or heterogeneous 3D object or set of objects. The NSMS approach uses magnetic dipoles, distributed on a fictitious closed surface, as responding sources for predicting an object's EMI response. The amplitudes of the NSMS sources are determined from actual measured data and the resulting total NSMS is used as a discriminant. To demonstrate the applicability of the NSMS technique, we compare actual and predicted data for various UXO. The data were collected at Yuma Proving Ground UXO sites by personnel from the University of California, Berkeley.

Recently, several sensor technologies, such as magnetometers (total-field and gradiometers) and various types of timedomain and frequency-domain electromagnetic induction (EMI) sensors have been developed and applied successfully to land-based subsurface unexploded ordnance (UXO) detection and mapping. Current researchers of underwater UXO detection commonly apply land-based UXO detection technologies directly to underwater scenarios. Since the electric conductivity of water is much higher than that of soil, an object's EMI response underwater should be different than in a dry environment because inside the conducting water low-frequency electromagnetic signals change both in magnitude and phase, particularly at high frequencies where induction numbers (i.e., wavenumbers) are significantly high. In order to fully explore the capabilities and limitations of land-based EMI sensors for underwater UXO detection and discrimination, in this paper we assess the applicability of current EMI forward models by investigating how the electromagnetic parameters of seawater affect the performance of state-of-the-art EMI sensors. The studies are conducted using the Generalized Standardized Excitation Approach. Objects' locations are inverted for using a reduced version of the HAP technique that combines the magnetic field and its gradient. Particular attention is given to understanding how seawater EM parameters or a multilayer conductive background change objects' EMI responses and affect the UXO discrimination process.

A new physics-based expression is presented for determining a buried object's location, orientation and magnetic polarizibility. The approach assumes the target exhibits a dipolar response and requires only three global Values: a magnetic field vector H. a vector potential A and a scalar magnetic potential, all at it single location in space. Among these Values. only the scattered magnetic field, H. is measurable with current electromagnetic induction sensors. Therefore, in order to estimate the scattered magnetic scalar and vector potentials from data, a numerical technique called the normalized surface magnetic Source (NSMS) method is employed. Originally, in the NSMS model, the scattered magnetic field Outside the object is reproduced mathematically by equivalent magnetic charges distributed on a three-dimensional (3-D) closed surface. Here, a two-dimensional (2-D) implementation of the NSMS that uses elementary magnetic dipoles, instead of magnetic charges distributed on a planar Surface placed under the measurement grid, is utilized. These sources are used to estimate the scattered magnetic field's vector potential A and scalar magnetic potential 11 Without a prion knowledge of the object's location and orientation. The amplitudes of the NSMS are determined by matching the measured magnetic field with the NSMS modeled field. Once the NSMS amplitudes are determined, H, A, and are simulated on or above the measurement grid. The theoretical basis of the new approach, as well as the practical realization of the 2-D NSMS algorithm used to estimate H, A and above the measurement grid from actual data. Is illustrated. Several numerical and experimental tests for actual EMI sensors are presented.

The prohibitive costs of excavating all geophysical anomalies are well known and are one of the greatest impediments to efficient clean-up of unexploded ordnance (UXO)-contaminated lands at Department of Defense (DoD) and Department of Energy (DOE) sites. Innovative discrimination techniques that can reliably distinguish between hazardous UXO and non-hazardous metallic items are required. The key element to overcoming these difficulties lies in the development of advanced processing techniques that can treat complex data sets to maximize the probability of accurate classification and minimize the false alarm rate. To address these issues, this paper uses a new approach that combines a physically complete EMI forward model called the Generalized Standardized Excitation Approach (GSEA) with a statistical signal processing approach named Mixed Modeling (MM). UXO discrimination requires the inversion of digital geophysical data, which could be divided into two pars: 1) linear - estimating model parameters such as the amplitudes of the responding GSEA sources and 2) non-linear - inverting an object's location and orientation. Usually the data inversion is an ill-posed problem that requires regularization. Determining the regularization parameter is not straightforward, and in many cases depends on personal experience. To overcome this issue, in this paper we employ the statistical approach to estimate regularization parameters from actual data using the un-surprised mixed model approach. In addition, once the non-linear inverse scattering parameters are estimated then for UXO discrimination a covariance matrix and confidence interval are derived. The theoretical basis and practical realization of the combined GSEA-Mixed Model algorithm are demonstrated. Discrimination studies are done for ATC-UXO sets of time-domain EMI data collected at the ERDC UXO test stand site in Vicksburg, Mississippi.