ALLTEM for UXO Applications — First Field Tests

Wright, David L.; Moulton, Craig W.; Asch, Theodore H.; Brown, Philip J.; Hutton, S. Raymond; Nabighian, Misac N.; Li, Yaoguo

doi:10.4133/1.2923640

Cited by 9 publications

(6 citation statements)

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“…Viscous magnetic soils with permeabilities greater than 10 −6 MKS units cause detrimental interference to the response to targets with volumes on the order of 40 cm 3 . In cases where the spatial variation of magnetic soil properties is low (i.e., large correlation lengths), subtracting the regional background response may facilitate proper target analysis [35]. 8.…”

Section: Discussionmentioning

confidence: 99%

Combining Advances in Em Induction Instrumentation and Inversion Schemes for Uxo Characterization

Oden¹

2012

PIER B

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Abstract-Several experimental time-domain EM induction instruments have recently been developed for unexploded ordnance (UXO) detection and characterization that use multiple transmitting and receiving coil combinations. One such system, the US Geological Survey's ALLTEM system, is unique in that it measures both the electrodynamic response (i.e., induced eddy currents) and the magneto-static response (i.e., induced magnetization). This allows target characterization based on the dyadic polarizability of both responses. This paper examines the numerical response of the ALLTEM instrument due to spheroidal, conductive, and permeable UXO targets; and to conductive and optionally viscous magnetic earth. An inversion scheme is presented for spheroidal targets that incorporates fully polarimetric measurements for both magneto-static and electro-dynamic excitations. The performance of the inversion algorithm is evaluated using both simulated and surveyed data. The results are examined as a function of the number of coil combinations, number of instrument locations, and uncertainty in sensor location and orientation. Results from the specific cases tested (prolate spheroids lying horizontally) show that 1) that collecting data from more than 12 sensor locations or from more than four coil combinations reduced the chances that inversion solutions would be from a local minimum, and 2) that uncertainties in position greater than 3 cm or in orientation greater than 10 degrees cause errors in the estimated spheroid principal lengths of greater than 100%. Soil conductivities less than 1 S/m contribute negligible interference to the target response, but viscous magnetic soils with permeabilities greater than 10 −6 MKS units do cause detrimental interference.

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Section: Discussionmentioning

confidence: 99%

Combining Advances in Em Induction Instrumentation and Inversion Schemes for Uxo Characterization

Oden¹

2012

PIER B

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“…Practically, the use of a triangle‐waveform results in smaller early‐time voltages induced in the Rx loops reducing dynamic‐range demands on the receiver analogue electronics and the digitizer. Another useful consequence is that ferrous and non‐ferrous targets generate markedly different responses (Wright et al . 2005, 2006).…”

Section: Alltemmentioning

confidence: 99%

“…Practically, the use of a triangle-waveform results in smaller early-time voltages induced in the Rx loops reducing dynamic-range demands on the receiver analogue electronics and the digitizer. Another useful consequence is that ferrous and non-ferrous targets generate markedly different responses (Wright et al 2005(Wright et al , 2006. ALLTEM obtains the advantages of triangle-wave excitation in a system whose dimensions, characteristics and geometry are appropriate to UXO applications.…”

Section: Electromagnetic Inductionmentioning

confidence: 99%

“…2001; Pasion & Oldenburg 2001; Zhang et al . 2003; Wright et al . 2006, 2008) and magnetics techniques (Nelson & McDonald 2001; Billings 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Classification takes discrimination one step further by identifying the individual types of UXO that have been detected. Among the primary geophysical systems used to detect and classify UXO are time and frequency domain electromagnetic induction (McNeill & Bosnar 1996;Geng et al 1999;Carin et al 2001;Pasion & Oldenburg 2001;Zhang et al 2003;Wright et al 2006Wright et al , 2008 and magnetics techniques (Nelson & McDonald 2001;Billings 2004).…”

Section: Introductionmentioning

confidence: 99%

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Hybrid analysis of multiaxis electromagnetic data for discrimination of munitions and explosives of concern

Friedel

Asch

Oden³

2012

Geophysical Journal International

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SUMMARY The remediation of land containing munitions and explosives of concern, otherwise known as unexploded ordnance, is an ongoing problem facing the U.S. Department of Defense and similar agencies worldwide that have used or are transferring training ranges or munitions disposal areas to civilian control. The expense associated with cleanup of land previously used for military training and war provides impetus for research towards enhanced discrimination of buried unexploded ordnance. Towards reducing that expense, a multiaxis electromagnetic induction data collection and software system, called ALLTEM, was designed and tested with support from the U.S. Department of Defense Environmental Security Technology Certification Program. ALLTEM is an on‐time time‐domain system that uses a continuous triangle‐wave excitation to measure the target‐step response rather than traditional impulse response. The system cycles through three orthogonal transmitting loops and records a total of 19 different transmitting and receiving loop combinations with a nominal spatial data sampling interval of 20 cm. Recorded data are pre‐processed and then used in a hybrid discrimination scheme involving both data‐driven and numerical classification techniques. The data‐driven classification scheme is accomplished in three steps. First, field observations are used to train a type of unsupervised artificial neural network, a self‐organizing map (SOM). Second, the SOM is used to simultaneously estimate target parameters (depth, azimuth, inclination, item type and weight) by iterative minimization of the topographic error vectors. Third, the target classification is accomplished by evaluating histograms of the estimated parameters. The numerical classification scheme is also accomplished in three steps. First, the Biot–Savart law is used to model the primary magnetic fields from the transmitter coils and the secondary magnetic fields generated by currents induced in the target materials in the ground. Second, the target response is modelled by three orthogonal dipoles from prolate, oblate and triaxial ellipsoids with one long axis and two shorter axes. Each target consists of all three dipoles. Third, unknown target parameters are determined by comparing modelled to measured target responses. By comparing the rms error among the self‐organizing map and numerical classification results, we achieved greater than 95 per cent detection and correct classification of the munitions and explosives of concern at the direct fire and indirect fire test areas at the UXO Standardized Test Site at the Aberdeen Proving Ground, Maryland in 2010.

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Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

Thomas,

Salmon,

Holloway

et al. 2024

Meas. Sci. Technol.

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This paper presents machine learning classification on simulated data of permeable conducting spheres in air and seawaterirradiated by low frequency electromagnetic pulses. Classification accuracy greater than 90% was achieved. The simulated datawere generated using an analytical model of a magnetic dipole in air and seawater placed 1.5 – 3.5 m above the center of the spherein 50 cm increments. The spheres had radii of 40 cm and 50 cm and were of permeable materials, such as steel, and non-permeablematerials, such as aluminum. A series RL circuit was analytically modeled as the transmitter coil, and an RLC circuit as the receivercoil. Additive white Gaussian noise was added to the simulated data to test the robustness of the machine learning algorithms tonoise. Multiple machine learning algorithms were used for classification including a perceptron and multiclass logistic regression,which are linear models, and a neural network, 1D convolutional neural network (CNN), and 2D CNN, which are nonlinear models.Feature maps are plotted for the CNNs and provide explainability of the salient parts of the time signature and spectrogram dataused for classification. The pulses investigated, which expand the literature, include a two-sided decaying exponential, Heavisidestep-off, triangular, Gaussian, rectangular, modulated Gaussian, raised cosine, and rectangular down-chirp. Propagation effects,including dispersion and frequency dependent attenuation, are encapsulated by the analytical model, which was verified usingfinite element modeling. The results in this paper show that machine learning methods are a viable alternative to inversion ofelectromagnetic induction (EMI) data for metallic sphere classification, with the advantage of real-time classification without theuse of a physics-based model. The nonlinear machine learning algorithms used in this work were able to accurately classify metallicspheres in seawater even with significant pulse distortion caused by dispersion and frequency dependent attenuation. This paperpresents the first effort towards the use of machine learning to classify metallic objects in seawater based on EMI sensing.

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ALLTEM for UXO Applications — First Field Tests

Cited by 9 publications

References 0 publications

Combining Advances in Em Induction Instrumentation and Inversion Schemes for Uxo Characterization

Combining Advances in Em Induction Instrumentation and Inversion Schemes for Uxo Characterization

Hybrid analysis of multiaxis electromagnetic data for discrimination of munitions and explosives of concern

Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

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