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.


Article metrics loading...

Loading full text...

Full text loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error