Irma Shamatava

Fast data-derived fundamental spheroidal excitation models with application to UXO discrimination

Current idealized forward models for electromagnetic induction (EMI) response can be defeated by the characteristic material and geometrical heterogeneity of realistic unexploded ordnance (UXO). A new, physically complete modeling system was developed that includes all effects of these heterogeneities and their interactions within the object, in both near and far fields. The model is fast enough for implementation in inversion processing algorithms. A method is demonstrated for extracting the model parameters by straightforward processing of data from a defined measurement protocol. Depending on the EMI sensor used for measurements, the process of inferring model parameters is more or less ill-posed. More complete data can alleviate the problem. For a given set of data, special numerical treatment is introduced to take the best advantage of the data and obtain reliable model parameters. The resulting fast model is implemented in a pattern matching treatment of measurements by which signals from a UXO are identified within a series of those from unknown targets. Preliminary results show that this fast model is promising for use in processing of this kind. The inherent difficulties of target identification are examined, and solutions for resolving these difficulties are discussed.

Fast and accurate calculation of physically complete EMI response by a heterogeneous metallic object

In this paper, the coupling and close-proximity effects arising between highly conducting and permeable metallic objects are exposed and analyzed, for the electromagnetic induction (EMI) frequency range (from tens of hertz up to several hundreds of kilohertz). To understand the physics of the interaction phenomena, a numerical technique is applied, consisting of the full method of auxiliary sources (MAS) at low frequencies and a combination of the MAS with thin-skin approximation (TSA) at high frequencies. Both numerical MAS-MAS/TSA and experimental studies have shown that the scattered field from a heterogeneous target generated as a simple superposition of independent responses from each part can be very different from the field determined from whole object with full internal interaction. A new numerical technique for fast and accurate representation of EMI responses for heterogeneous objects is pursued here, applicable to any three-dimensional heterogeneous object placed in an arbitrary time-varying EMI field. First, any primary magnetic field input is decomposed into the spheroidal modes over a fictitious surface surrounding the object. Then, for each input spheroidal mode, the full EMI problem including all interaction is solved using the MAS-MAS/TSA technique, and each modal response is reproduced using a compact reduced set of sources (RSS). Finally, the total response from the given target for any other excitation can be synthesized simply by calculating that primary fields constituent spheroidal modes and combining their stored responses. Several numerical examples are designed to show how an objects electromagnetic parameters, geometry, distance between objects, antenna positions, and orientations relative to the object affect the coupling. Comparisons between numerical and measured data for a machined composite object and for an actual unexploded ordnance demonstrate the superior accuracy and applicability of the MAS-MAS/TSA RSS model over simple dipole approximations, for certain classes of heterogeneous objects.

Theoretical analysis and range of validity of TSA formulation for application to UXO discrimination

Operating in the magnetoquasistatic regime (a few hertz to perhaps a few 100 kHz), electromagnetic induction (EMI) sensing has recently emerged as one of the most promising avenues for discrimination of subsurface metallic objects, e.g., unexploded ordnance. The technique of thin-skin approximation (TSA) was devised to deal with numerical problems caused by the rapid decay of fields beneath the scatterers surface. The rather nonintuitively broad applicability and specific error patterns of the TSA formulation are explained here by theoretical analysis based on analytical solutions and approximate Monte Carlo simulation. In the limiting case of infinitesimal skin depth (EMI perfect reflection), the scatterer aspect ratio (AR) is inferred without regard to metal type. Alternatively, the AR of some homogeneous magnetic objects is inferred from the pattern of transverse to axial response ratio over the entire EMI ultrawideband. Use of the method in inversions for electromagnetic parameters reveals fundamental nonuniqueness problems and shows their basis, which is not dependent on the method of forward solution.

Applying a volume dipole distribution model to next-generation sensor data for multi-object data inversion and discrimination

Discrimination between UXO and harmless objects is particularly difficult in highly contaminated sites where two or more objects are simultaneously present in the field of view of the sensor and produce overlapping signals. The first step in overcoming this problem is estimating the number of targets. In this work an orthonormalized volume magnetic source (ONVMS) approach is introduced for estimating the number of targets, along with their locations and orientations. The technique is based on the discrete dipole approximation, which distributes dipoles inside the computational volume. First, a set of orthogonal functions are constructed using fundamental solutions of the Helmholtz equations (i.e., Greens functions). Then, the scattered magnetic field is approximated as a series of these orthogonal functions. The magnitudes of the expansion coefficients are determined directly from the measurement data without solving an ill-posed inverse-scattering problem. The expansion coefficients are then used to determine the amplitudes of the responding volume magnetic dipoles. The algorithms superior performance and applicability to live UXO sites are illustrated by applying it to the bi-static TEMTADS multi-target data sets collected by NRL personnel at the Aberdeen Proving Ground UXO teststand site.

Evaluation of approximate analytical solutions for EMI scattering from finite objects of different shapes and properties

UWB electromagnetic induction (EMI) sensing, from 10s of Hz up to 100s of kHz, is emerging as one of the most promising remote sensing technologies for discrimination of subsurface metallic objects. Progress is urgently needed to distinguish dangerous objects, such as unexploded ordnance, from innocuous clutter. Development of EMI signal calibration, interpretation, processing, and inversion have all been impeded by the lack of rigorous, reliable analytical solutions for any scatterer shape other than the sphere. Here we test a number of new approximate solutions and determine that most canonical geometries and common, homogeneous material compositions can be treated adequately by the simple formulations proposed.

A New Physics-Based Approach for Estimating a Buried Object's Location, Orientation and Magnetic Polarization from EMI Data

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 ψ, all at a 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...

Realistic subsurface anomaly discrimination using electromagnetic induction and an SVM classifier

The environmental research program of the United States military has set up blind tests for detection and discrimination of unexploded ordnance. One such test consists of measurements taken with the EM-63 sensor at Camp Sibert, AL. We review the performance on the test of a procedure that combines a field-potential (HAP) method to locate targets, the normalized surface magnetic source (NSMS) model to characterize them, and a support vector machine (SVM) to classify them. The HAP method infers location from the scattered magnetic field and its associated scalar potential, the latter reconstructed using equivalent sources. NSMS replaces the target with an enclosing spheroid of equivalent radial magnetization whose integral it uses as a discriminator. SVM generalizes from empirical evidence and can be adapted for multiclass discrimination using a voting system. Our method identifies all potentially dangerous targets correctly and has a false-alarm rate of about 5%.

Inferring the location of buried UXO using a support vector machine

The identification of unexploded ordnance (UXO) using electromagnetic-induction (EMI) sensors involves two essentially independent steps: Each anomaly detected by the sensor has to be located fairly accurately, and its orientation determined, before one can try to find size/shape/composition properties that identify the object uniquely. The dependence on the latter parameters is linear, and can be solved for efficiently using for example the Normalized Surface Magnetic Charge model. The location and orientation, on the other hand, have a nonlinear effect on the measurable scattered field, making their determination much more time-consuming and thus hampering the ability to carry out discrimination in real time. In particular, it is difficult to resolve for depth when one has measurements taken at only one instrument elevation. In view of the difficulties posed by direct inversion, we propose using a Support Vector Machine (SVM) to infer the location and orientation of buried UXO. SVMs are a method of supervised machine learning: the user can train a computer program by feeding it features of representative examples, and the machine, in turn, can generalize this information by finding underlying patterns and using them to classify or regress unseen instances. In this work we train an SVM using measured-field information, for both synthetic and experimental data, and evaluate its ability to predict the location of different buried objects to reasonable accuracy. We explore various combinations of input data and learning parameters in search of an optimal predictive configuration.

Analyzing multi-axis data versus scalar data for UXO discrimination

The objective of this paper is to study the advantage of multi-axis (vector) data over scalar one-dimensional data in the electromagnetic induction (diffusion) regime in both frequency and time domains for discriminating unexploded ordnance (UXO). Particular attention is given to the time domain. Traditional magnetometers and coil-based electromagnetic induction sensors measure only one component of the scattered magnetic field. They provide high sensitivity, but one-component magnetic field measurements provide limited information about the electromagnetic signatures of buried items, particularly for target localization and determination of target parameters. Recently much effort has been directed at developing next-generation electromagnetic geophysical sensors to collect vector data; for example, Geophex has built a new 3D GEM-3 sensor, with one transmitter and three (all Hx, Hy, Hz) receiver coils, and similar capabilities exist in the time domain. In this paper a surface magnetic charge (SMC) model, in conjunction with a differential evolution (DE) algorithm, is used to treat multi-axis data to advance, motivated by potential application to discrimination of buried UXO’s. In the SMC model the scattered magnetic field is produced by a set of magnetic charges distributed mathematically around the target location. The amplitudes of these charges is determined by matching to measured magnetic fields at a selected set of points. When the charge amplitudes are normalized by the corresponding normal component of the primary field at each location, their sum is regarded as an indication of the magnetic capacity of the object and is used as a discriminant. Once the amplitude of this normalized source set is found for each object, it can be stored for subsequent use in a discrimination algorithm. Time domain SMCs are developed for highly permeable and metallic objects buried inside a magnetic half-space. Air/magnetic ground interface effects are taken into account using image theory. Examples of synthetic electromagnetic induction data sets in the time domain are designed to show the advantage of vector over scalar data. The numerical tests for inversion of an object’s location and position from the multi-axis data and single component data will are discussed and analyzed in detail.

Simple magnetic charge model for representation of emi responses from a buried UXO

Low frequency electromagnetic induction (EMI) sensing, operating from tens of Hertz up to several hundreds of kHz, has been identified as one of most promising technologies for detection and discrimination of subsurface metallic objects, particularly unexploded ordnance (UXOs). In EMI sensing, which is a pure EM diffusion rather than wave phenomena, displacement currents within an object and its surrounding medium are negligible. Therefore the scattered field outside the object can be represented as a sum of quasi-static magnetic fields radiated by magnetic charges placed on a fictitious surface. This paper presents a simple surface magnetic charge model for fast and accurate representation of EMI signal for any metallic scatterer of interest. The magnetic charges and Greens function associated with them are much simpler than magnetic dipoles and their dyadic Greens functions. The simplicity and computational accuracy of the magnetic charge model makes it an alternative candidate to the simple dipole model for buried metallic object discrimination in EMI frequency range. In addition, for a given object the amplitudes of the magnetic charges are unique and they can be used to discriminate an object of interest from innocuous items. The amplitudes of the responding magnetic charges are determined from measured data. Several numerical results are presented to demonstrate the accuracy and superior computational speed of the proposed method. I. Introduction