Hopfield neural network for seismic velocity picking

Kou-Yuan Huang; Jia Rone Yang

doi:10.1109/IJCNN.2014.6889512

Hopfield neural network for seismic velocity picking

Kou-Yuan Huang^*, Jia Rone Yang

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The Hopfield neural network (HNN) is adopted for velocity picking in the time-velocity semblance image of seismic data. A Lyapunov function in the HNN is set up from the velocity picking problem. We use the gradient descent method to decrease the Lyapunov function and derive the equation of motion. According to the equation of motion, each neuron is updated until no change. The converged network state represents the best polyline in velocity picking. We have experiments on simulated and real seismic data. The picking results are good and close to the human picking results.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1146-1153
Number of pages	8
ISBN (Electronic)	9781479914845
DOIs	https://doi.org/10.1109/IJCNN.2014.6889512
State	Published - 3 Sep 2014
Event	2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	2014 International Joint Conference on Neural Networks, IJCNN 2014
Country	China
City	Beijing
Period	6/07/14 → 11/07/14

Keywords

Hopfield neural network
Lyapunov function
equation of motion
seismic velocity picking
semblance image

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10.1109/IJCNN.2014.6889512

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Cite this

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title = "Hopfield neural network for seismic velocity picking",

abstract = "The Hopfield neural network (HNN) is adopted for velocity picking in the time-velocity semblance image of seismic data. A Lyapunov function in the HNN is set up from the velocity picking problem. We use the gradient descent method to decrease the Lyapunov function and derive the equation of motion. According to the equation of motion, each neuron is updated until no change. The converged network state represents the best polyline in velocity picking. We have experiments on simulated and real seismic data. The picking results are good and close to the human picking results.",

keywords = "Hopfield neural network, Lyapunov function, equation of motion, seismic velocity picking, semblance image",

author = "Kou-Yuan Huang and Yang, {Jia Rone}",

year = "2014",

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day = "3",

doi = "10.1109/IJCNN.2014.6889512",

language = "English",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1146--1153",

booktitle = "Proceedings of the International Joint Conference on Neural Networks",

address = "United States",

note = "null ; Conference date: 06-07-2014 Through 11-07-2014",

}

Huang, K-Y & Yang, JR 2014, Hopfield neural network for seismic velocity picking. in Proceedings of the International Joint Conference on Neural Networks., 6889512, Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., pp. 1146-1153, 2014 International Joint Conference on Neural Networks, IJCNN 2014, Beijing, China, 6/07/14. https://doi.org/10.1109/IJCNN.2014.6889512

Hopfield neural network for seismic velocity picking. / Huang, Kou-Yuan; Yang, Jia Rone.

Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1146-1153 6889512 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The Hopfield neural network (HNN) is adopted for velocity picking in the time-velocity semblance image of seismic data. A Lyapunov function in the HNN is set up from the velocity picking problem. We use the gradient descent method to decrease the Lyapunov function and derive the equation of motion. According to the equation of motion, each neuron is updated until no change. The converged network state represents the best polyline in velocity picking. We have experiments on simulated and real seismic data. The picking results are good and close to the human picking results.

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