Abstract
Artificial Neural Networks (ANNs) are used for the interpretation of multi-frequency airborne electromagnetic (AEM) data independently of the sensor height, with one-dimensional (1-D) horizontally layered homogeneous earth structures. A divide-and-conquer strategy is applied. One ANN is trained to interpret data, which are best described by homogeneous half-space (HHS) models. A second ANN inverts data from horizontally layered half-space models with two layers (2LHS). Tests have shown that when the 2LHS ANN is applied to data, which are best, described with a HHS-like structure, interpretation errors can become large. Therefore, a third ANN is trained, which classifies the best interpretation of measurements as a HHS model or a 2LHS model. This modular ANN approach shows a good performance on synthetic data. Finally, the interpretation of data from an AEM survey over a tertiary basin structure, shows good accordance with known geological data.
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© 2003 Springer Science+Business Media Dordrecht
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Winkler, E., Seiberl, W., Ahl, A. (2003). Interpretation of Airborne Electromagnetic Data with Neural Networks. In: Sandham, W.A., Leggett, M. (eds) Geophysical Applications of Artificial Neural Networks and Fuzzy Logic. Modern Approaches in Geophysics, vol 21. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-0271-3_16
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DOI: https://doi.org/10.1007/978-94-017-0271-3_16
Publisher Name: Springer, Dordrecht
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