Application of Extreme Learning Machines and Echo State Networks to Seismic Multiple Removal

In seismic exploration, the collected signals not only contain the primary reflections of the subsurface structures, but also multiple reflections, which may hamper a proper and reliable analysis of the seismic image. In order to attenuate the effect of the multiple events, a predictive deconvolutio...

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Bibliographic Details
Main Authors: Carvalho, Heitor S., Shams, Farzin, Ferrari, Rafael, Boccato, Levy
Format: Conference Proceeding
Language:English
Subjects:
Deconvolution
Distortion
echo state networks
extreme learning machines
Multiple removal
predictive deconvolution
Sea surface
Sensors
Surface impedance
Task analysis
Transforms
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Summary:In seismic exploration, the collected signals not only contain the primary reflections of the subsurface structures, but also multiple reflections, which may hamper a proper and reliable analysis of the seismic image. In order to attenuate the effect of the multiple events, a predictive deconvolution approach can be used, which is particularly adequate for shallow water, and involves the design of a filter to predict the multiple events. In this work, we investigate the possibility of employing nonlinear structures in lieu of the usual linear filter. The chosen structures in this study refer to extreme learning machines and echo state networks, which offer an interesting trade-off between compu- tational cost and processing capability. The obtained results in the context of synthetic data clearly reveal the potential of this approach and motivate the continuity of this investigation.
ISSN:2161-4407
DOI:10.1109/IJCNN.2018.8489620