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Identification and Suppression of Multiple Reflections in Marine Seismic Data with Neural Networks

  • Robert Essenreiter
  • Martin Karrenbach
  • Sven Treitel
Part of the Modern Approaches in Geophysics book series (MAGE, volume 21)

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.

Keywords

Seismic Data Multiple Reflection Wiener Filter Deep Reflector Seismic Trace 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • Robert Essenreiter
    • 1
  • Martin Karrenbach
    • 2
  • Sven Treitel
    • 3
  1. 1.PGS Seres ASLysakerNorway
  2. 2.Geophysical InstituteUniversity of KarlsruheKarlsruheGermany
  3. 3.TriDekon, Inc.TulsaUSA

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