Seismic velocity picking by Hopfield neural network

Kou-Yuan Huang, Jia Rong Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Hopfield neural network (HNN) is adopted for velocity picking in the time-velocity semblance image of seismic data. A Lyapunov function is generated from the velocity picking problem. We use the gradient descent method to decrease the Lyapunov function and derive the equation of motion. The Lyapunov function can reach the minimum. According to the equation of motion, each neuron is updated until no change. The linking of the converged network neurons represents the best polyline in velocity picking. We have experiments on simulated seismic data. The picking results are good. It can improve the seismic data processing and interpretation.

Original languageEnglish
Title of host publication2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3190-3193
Number of pages4
ISBN (Electronic)9781509033324
DOIs
StatePublished - 1 Nov 2016
Event36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China
Duration: 10 Jul 201615 Jul 2016

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2016-November

Conference

Conference36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
CountryChina
CityBeijing
Period10/07/1615/07/16

Keywords

  • Hopfield neural network
  • Lyapunov function
  • seismic velocity picking

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    Huang, K-Y., & Yang, J. R. (2016). Seismic velocity picking by Hopfield neural network. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings (pp. 3190-3193). [7729825] (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2016-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2016.7729825