@inproceedings{scholars931,
           title = {Geophysical inversion using radial basis function},
         address = {Kuala Lumpur},
            note = {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},
             doi = {10.1109/ICIAS.2010.5716138},
            year = {2010},
         journal = {2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010},
        abstract = {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.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79952757513&doi=10.1109\%2fICIAS.2010.5716138&partnerID=40&md5=ba44094881dbfebaffa0fc3595ea4837},
            isbn = {9781424466238},
        keywords = {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},
          author = {Arif, A. and Asirvadam, V. S. and Karsiti, M. N.}
}