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Abstract

A neural network was used to predict the mass of shallow subsurface conductive objects. The field data was from<br>an unexploded ordnance (UXO) survey conducted at Camp Simms, Washington DC. The survey instrument was the<br>Geonics EM-6 1 pulsed induction sensor. The purpose of the study was to develop a neural network architecture<br>that could be fielded at numerous sites under the US Army Corps of Engineer’s Ordnance and Explosives<br>Knowledgebase (OE-KB) program. The neural network was successful in predicting masses and depths. This<br>paper presents the results of a single training session and is also intended as a short tutorial in how to prepare and<br>present geophysical field data for analysis by a neural network.

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/content/papers/10.3997/2214-4609-pdb.205.1996_077
1996-04-28
2020-03-30
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609-pdb.205.1996_077
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