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

Oden, Charles P.

doi:10.2528/pierb11112607

Cited by 5 publications

(4 citation statements)

References 36 publications

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“…Induced magnetization can add to the non‐ferrous or a ferrous eddy current response. The demagnetization can also have a large effect (Oden 2012 and Smith & Morrison 2006) as well as viscous magnetization at locations such as Hawaii and Montana where basalts and associated weathered soil products contain significant amounts of iron.…”

Section: Methodsmentioning

confidence: 99%

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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Section: Methodsmentioning

confidence: 99%

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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“…A majority of the objects can be approximated as spheroids, and the axial and transverse responses of the spheroid can be approximated as spheres response [37]. Therefore, the magnetic polarization model of the spheroid object in different directions can be expressed as [38]:…”

Section: B Spheroid Modelmentioning

confidence: 99%

Sensitivity Analysis and Classification Algorithms Comparison for Underground Target Detection

Duan

Wan

et al. 2019

IEEE Access

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Underground target detection technology has been widely used in urban construction and resource exploration. With the development of industrial modernization, the demand for underground target detection is becoming more specific, such as the material and shape of underground targets. Therefore, it is necessary to classify the properties of underground targets. In this paper, sensitivity analysis was performed on the spheroid model and the approximate forward model at first, and the influence of the target properties on the model output is obtained. Secondly, we utilized the fitting algorithm to obtain the model parameters of the simulation data (model response of targets with varying shapes and materials), and analyzed the influence of the fitting algorithm on the classification results at different SNR. Finally, eight machine learning algorithms: support vector machine(SVM), neural network(NN), quadratic discriminant analysis (QDA), Gaussian process (GP), decision tree (DT), random forest (RF) and AdaBoost were used in this study to compare the obtained results. From the above analysis, we found that the shape (radius) have a greater influence on the model than the material (permeability) in the spheroid model. According to the approximate forward model, we found that it is not feasible to classify targets when the orientation is unknown. The influence of the fitting algorithm on the classification performances is related to the noise level. The obtained results using neural network demonstrated that the proposed method outperformed in material-based classification and shape-based classification. In the material-based classification, the classifier generally has a weaker ability to distinguish between permeable materials.

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“…A new modeling and inversion program called MMTargets was created for this project. Derived from an existing program that was written for the USGS ALLTEM system to invert dynamic data [1], MMTargets can be used to generate synthetic datasets, or invert data to estimate target parameters. The program has a large number of user-configurable settings that control how synthetic data are generated, or how the program processes and inverts a dataset.…”

Section: Mmtargetsmentioning

confidence: 99%

“…In this section, we attempt to provide answers to these questions using a new single-source target parameterization program, MMTargets [1] and other codes.…”

Section: Optimal+arrays+study+objectives+mentioning

confidence: 99%

Expanded Processing Techniques for EMI Systems

Prouty¹,

Snyder²,

George³

et al. 2012

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

Cited by 5 publications

References 36 publications

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

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

Sensitivity Analysis and Classification Algorithms Comparison for Underground Target Detection

Expanded Processing Techniques for EMI Systems

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