Geophysical inversion using radial basis function

• Main Authors: Arif, Agus, Asirvadam, Vijanth S., Karsiti, M. N.
• Format: Conference or Workshop Item
• Published: 2010
• Subjects: TK Electrical engineering. Electronics Nuclear engineering; QA75 Electronic computers. Computer science
• Online Access: http://eprints.utp.edu.my/4637/1/GeoPhySeabed.pdf; http://eprints.utp.edu.my/4637/

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.