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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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://eprints.utp.edu.my/4637/1/GeoPhySeabed.pdf http://eprints.utp.edu.my/4637/ |
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| Summary: | 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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