Application of a library based method to time domain electromagnetic data for the identification of unexploded ordnance

Pasion, Leonard R.; Billings, Stephen; Oldenburg, Douglas W.; Walker, Sean E.

doi:10.1016/j.jappgeo.2006.05.006

Cited by 51 publications

(33 citation statements)

References 23 publications

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“…Classification is a matter of deciding whether the object's polarizabilities are TOI-like or clutterlike. Library matching methods employing various procedures to compare polarizabilities of unknown targets with those of targets of interest (TOIs) are commonly used for classification [5,6]. Our method exploits the fact that an object's polarizability tensor B(t) can be represented as a product of two factors: the volume V of the object and a tensor A(t) whose eigenvalues α j (t), i = 1, 2, 3 depend only on the shape and composition of the object.…”

Section: Decision Metrics For Classificationmentioning

confidence: 99%

Tools for Risk-Based UXO Remediation

Keiswetter¹,

Bell²

2014

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. The primary purpose of the process described and illustrated in this SEED study is to confirm that the detection and classification decisions made prior to and during a remedial effort remain reasonable once ground truth information is garnered and taken into account. Specific objectives were to investigate (i) a practical method for assessing target detections and (ii) procedures for determining the likelihood of false negatives given some amount of ground truth information. AbstractObjectives: The primary purpose of the process described and illustrated in this SEED study is to confirm that the detection and classification decisions made prior to and during a remedial effort remain reasonable once ground truth information is garnered and taken into account. Specific objectives were to investigate (i) a practical method for assessing target detections and (ii) procedures for determining the likelihood of false negatives given some amount of ground truth information.Technical Approach: The problem of UXO residual risk is naturally divided into the issues of target detection and, once detected, classification. With regards to the assessment of missed UXO detections, we present a practical and straight forward tool that involves inserting synthetic signatures, appropriately chosen for the UXO types actually recovered, into the reconnaissance survey data, processing using standard detection schemes and thresholds, and evaluating the results in the light of ground truth information. This approach is attractive because it naturally incorporates site-specific realistic noise levels and sampling schemes.The second issue, that of classification failures, results when TOI targets "look" more like what they are not than what they are, and are classified accordingly. In fact, because the decision metric is based on a library of expected TOI signatures, the target does not look "enough like" a TOI to be considered TOI. To investigate the possibility of classification failures, we (i) performed a probabilistic risk assessment using polarizabilities and ground truth information from Camp San Luis Obispo, Camp Butner, and Camp Beale, (ii) conducted a cluster analysis to look for groups of targets with similar signature...

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Section: Decision Metrics For Classificationmentioning

confidence: 99%

Tools for Risk-Based UXO Remediation

Keiswetter¹,

Bell²

2014

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“…A significant number of studies have been performed on the discrimination of targets. One approach is to analyse metal detector responses (e.g., Pasion et al, 2007;Shubitidze et al, 2007;Throckmorton et al, 2007). Another approach is to exploit other subsurface sensing techniques in addition to a metal detector or as a standalone detector.…”

Section: Introductionmentioning

confidence: 99%

Soil properties and performance of landmine detection by metal detector and ground-penetrating radar — Soil characterisation and its verification by a field test

Takahashi

Preetz

Igel

2011

Journal of Applied Geophysics

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“…The majority of automated algorithms that are currently being evaluated in field settings involve using sophisticated data inversion strategies coupled with phenomenological models to extract object parameters from spatially geo-located sensor data (Bell et al 2001;Pasion and Oldenburg 2001;Billings and Youmnas 2007;Pasion et al 2007;Chilaka et al 2006;Sun et al 2005;Billings and Herrmann 2003;Butler et al 2003;Rose-Pehrsson et al 1999;Lavely et al 1998;Shubitidze et al 2005;Zhang et al 2003a, b;Tantum and Collins 2001;Collins et al 2001;Tarokh et al 2004;Nelson and McDonald 2001). Several issues have historically been cited as impacting the performance of the inversion, and thus impacting the ability of the subsequent discrimination algorithm to separate UXO and clutter based on the object parameters that are determined from the inversion.…”

Section: Introductionmentioning

confidence: 99%

“…Some discrimination algorithms that have been developed specifically to address the stochastic nature of the problem directly do so within the context of a Bayesian framework (Lavely et al 1998;Zhang et al 2003a, b;Tantum and Collins 2001;Collins et al 2001;Tarokh et al 2004). Others mitigate the effects of this uncertainty associated with the extracted object parameters in other ways (Bell et al 2001;Pasion and Oldenburg 2001;Billings and Youmnas 2007;Pasion et al 2007;Chilaka et al 2006;Butler et al 2003;Rose-Pehrsson et al 1999).…”

Section: Introductionmentioning

confidence: 99%

Modeling position error probability density functions for statistical inversions using a Goff–Jordan rough surface model

Tantum

Zhu

Torrione

et al. 2008

Stoch Environ Res Risk Assess

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Buried unexploded ordnance (UXO) continues to be a difficult remediation problem from both a sensing and a discrimination point of view. Modern approaches to both the sensing and discrimination problems utilize high bandwidth electromagnetic induction (EMI) sensors to collect geo-referenced data which is then inverted, or fit, using a forward model in order to obtain features that can be directly interpreted using the physics associated with electromagnetic induction-based sensing. These features are then used in a variety of classification architectures. One aspect of this process that has captured recent interest is that uncertainty in the positions at which data was collected can degrade the inversion performance and thus the subsequent classification. Several mechanisms to address this issue have been explored that range from filtering and prediction of actual positions to exploiting Bayesian approaches for uncertainty mitigation. In the Bayesian approach, a statistical model of the position errors is used as a prior for integrating over the uncertainty in the inversion process. In this study, we demonstrate that errors in the statistical priors used in this process can negatively impact subsequent classification performance, thus highlighting the need for an accurate statistical model for the position errors. Next, we propose a mechanism by which to obtain such models. Specifically, we utilize a Goff-Jordan rough surface model and simulate the sensor data collection system motion over the simulated ground or ocean surfaces to calculate errors and generate statistical models. Our results suggest that this approach can be used to develop the statistical models necessary for mitigating uncertain position information.

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Application of a library based method to time domain electromagnetic data for the identification of unexploded ordnance

Cited by 51 publications

References 23 publications

Tools for Risk-Based UXO Remediation

Tools for Risk-Based UXO Remediation

Soil properties and performance of landmine detection by metal detector and ground-penetrating radar — Soil characterisation and its verification by a field test

Modeling position error probability density functions for statistical inversions using a Goff–Jordan rough surface model

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