<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "Geophysical inversion using radial basis function"^^ . "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."^^ . "2010" . . . "2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010"^^ . . . . . . . . . . . . . . "V.S."^^ . "Asirvadam"^^ . "V.S. Asirvadam"^^ . . "M.N."^^ . "Karsiti"^^ . "M.N. Karsiti"^^ . . "A."^^ . "Arif"^^ . "A. Arif"^^ . . . . . "HTML Summary of #931 \n\nGeophysical inversion using radial basis function\n\n" . "text/html" . .