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Application of Artificial Neural Networks to Seismic Waveform Inversion

  • Qiaodeng He
  • Hui Zhou
Part of the Modern Approaches in Geophysics book series (MAGE, volume 21)

Abstract

A three-layer feedforward neural network is described, which has been applied to geophysical parameter inversion. The number of elements in the input layer is equal to the number of recorded samples. During training, the global minimum of the energy function is determined using a decimal-encoded genetic algorithm. It is necessary to use weights and thresholds of neurons as a gene group. To determine the weights and thresholds corresponding to the global minimum of the energy function, a large search range is used initially, which is then progressively reduced in order to accelerate convergence. The network was trained using both 5 and 10 numerically modeled records of the vertical component. After convergence, the network was tested using a randomly generated three-layer transverse isotropic model. The inversion results are very encouraging.

Keywords

Genetic Algorithm Artificial Neural Network Hide Layer Energy Function Waveform Inversion 
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

  • Qiaodeng He
    • 1
  • Hui Zhou
    • 1
  1. 1.Department of GeophysicsChangchun University of Science and TechnologyChangchunPeoples Republic of China

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