TY  - CONF
A1  - Arif, A.
A1  - Asirvadam, V.S.
A1  - Karsiti, M.N.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-79952757513&doi=10.1109%2fICIAS.2010.5716138&partnerID=40&md5=ba44094881dbfebaffa0fc3595ea4837
SN  - 9781424466238
Y1  - 2010///
TI  - Geophysical inversion using radial basis function
ID  - scholars931
KW  - Artificial Neural Network; Geophysical inverse problems; Geophysical inversion; Radial basis functions; Seabed logging; Synthetic data
KW  -  Geophysics; Inverse problems; Mean square error; Models; Neural networks
KW  -  Radial basis function networks
N1  - 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
N2  - 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.
AV  - none
CY  - Kuala Lumpur
ER  -