relation: https://khub.utp.edu.my/scholars/931/
title: Geophysical inversion using radial basis function
creator: Arif, A.
creator: Asirvadam, V.S.
creator: Karsiti, M.N.
description: This paper is a continuation report of a series of research on seabed logging (SBL). In this paper, it was shown that a certain geophysical inverse problem (such as one posed by SBL) can be solved using an important class of artificial neural networks, which is a radial basis function (RBF). To show this, several sets of synthetic data has been generated using some assumed models of a physical property (such as seabed resistivity) distribution. Then, these pairs of data and models were used to train a RBF with a certain architecture. Finally, the trained RBF was tested to do inversion with new data and produced a predicted model. The predicted model was reasonably close to the true model and the mean square error (MSE) between them was 0.065.
date: 2010
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Arif, A. and Asirvadam, V.S. and Karsiti, M.N.  (2010) Geophysical inversion using radial basis function.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-79952757513&doi=10.1109%2fICIAS.2010.5716138&partnerID=40&md5=ba44094881dbfebaffa0fc3595ea4837
relation: 10.1109/ICIAS.2010.5716138
identifier: 10.1109/ICIAS.2010.5716138