Abstract
The process of generating multiple reflections in the earth is generally non-linear. We present a technique for identifying and attenuating multiple reflections in seismic data using non-linear filters i.e. multilayer perceptron neural networks. The aim is to separate the multiple wavefield from the primary wavefield and suppress it in order to create a multiple-free seismic section. A neural network is trained with modeled data that are generated from well-log information and then applied to the areas between the boreholes. Input to the neural network is either the seismic trace itself, or a number of selected, representative attributes computed from the seismic trace. The method is demonstrated on synthetic data and is validated by comparing it to the linear Wiener-filter technique.
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© 2003 Springer Science+Business Media Dordrecht
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Essenreiter, R., Karrenbach, M., Treitel, S. (2003). Identification and Suppression of Multiple Reflections in Marine Seismic 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_6
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DOI: https://doi.org/10.1007/978-94-017-0271-3_6
Publisher Name: Springer, Dordrecht
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