Neural networks for determination of subsurface targets in multi layer soil structure

dc.contributor.author	Dharrnadasa, IT
dc.contributor.author	Lucas, JR
dc.contributor.author	Udawatta, UKDL
dc.contributor.author	Wijayapala, WDAS
dc.date.accessioned	2013-10-21T02:12:35Z
dc.date.available	2013-10-21T02:12:35Z
dc.date.issued	2008
dc.date.issued	2008
dc.identifier.uri	http://dl.lib.mrt.ac.lk/handle/123/8142
dc.description.abstract	This paper results the research carried on the 2D resistivity inversion problem, where resistivity distribution is determined from the apparent resistivity measurements using the Artificial Neural Networks. Neural Network (NN) is trained with the synthetic data generated using a 56 multi electrode Wenner array with 1 m electrode spacing. The geoelectrical model studied show encouraging results for the applicability of the well trained NN 's as a fast 2D resistivity inversion tool for field resistivity measurements
dc.language	en
dc.title	Neural networks for determination of subsurface targets in multi layer soil structure
dc.type	Conference-Abstract
dc.identifier.year	2008
dc.identifier.conference	Research for Industry
dc.identifier.place	Faculty of Engineering, University of Moratuwa
dc.identifier.pgnos	99-101
dc.identifier.proceeding	14th Annual Symposium on Research and Industry