Hopfield neural network for seismic velocity picking

Kou-Yuan Huang*, Jia Rone Yang

*Corresponding author for this work

研究成果: Conference contribution同行評審

摘要

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.

原文English
主出版物標題Proceedings of the International Joint Conference on Neural Networks
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1146-1153
頁數8
ISBN(電子)9781479914845
DOIs
出版狀態Published - 3 九月 2014
事件2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
持續時間: 6 七月 201411 七月 2014

出版系列

名字Proceedings of the International Joint Conference on Neural Networks

Conference

Conference2014 International Joint Conference on Neural Networks, IJCNN 2014
國家China
城市Beijing
期間6/07/1411/07/14

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