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...
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my.utp.eprints.46372017-01-19T08:24:08Z Geophysical inversion using radial basis function Arif, Agus Asirvadam, Vijanth S. Karsiti, M. N. TK Electrical engineering. Electronics Nuclear engineering QA75 Electronic computers. Computer science 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. 2010-06 Conference or Workshop Item PeerReviewed application/pdf http://eprints.utp.edu.my/4637/1/GeoPhySeabed.pdf Arif, Agus and Asirvadam, Vijanth S. and Karsiti, M. N. (2010) Geophysical inversion using radial basis function. In: Intelligent and Advanced Systems (ICIAS), 2010 International Conference on. http://eprints.utp.edu.my/4637/ |
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TK Electrical engineering. Electronics Nuclear engineering QA75 Electronic computers. Computer science Arif, Agus Asirvadam, Vijanth S. Karsiti, M. N. Geophysical inversion using radial basis function |
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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. |
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Conference or Workshop Item |
author |
Arif, Agus Asirvadam, Vijanth S. Karsiti, M. N. |
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Arif, Agus Asirvadam, Vijanth S. Karsiti, M. N. |
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Arif, Agus |
title |
Geophysical inversion using radial basis function |
title_short |
Geophysical inversion using radial basis function |
title_full |
Geophysical inversion using radial basis function |
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Geophysical inversion using radial basis function |
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Geophysical inversion using radial basis function |
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geophysical inversion using radial basis function |
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2010 |
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http://eprints.utp.edu.my/4637/1/GeoPhySeabed.pdf http://eprints.utp.edu.my/4637/ |
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