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

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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.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


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