%R 10.1109/ICIAS.2010.5716138
%D 2010
%L scholars931
%J 2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010
%O cited By 0; Conference of 2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010 ; Conference Date: 15 June 2010 Through 17 June 2010; Conference Code:84196
%X 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.
%K Artificial Neural Network; Geophysical inverse problems; Geophysical inversion; Radial basis functions; Seabed logging; Synthetic data, Geophysics; Inverse problems; Mean square error; Models; Neural networks, Radial basis function networks
%C Kuala Lumpur
%A A. Arif
%A V.S. Asirvadam
%A M.N. Karsiti
%T Geophysical inversion using radial basis function