Seismic velocity picking by Hopfield neural network

Kou-Yuan Huang, Jia Rong Yang

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面3190-3193
頁數4
ISBN(電子)9781509033324
DOIs
出版狀態Published - 1 十一月 2016
事件36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China
持續時間: 10 七月 201615 七月 2016

出版系列

名字International Geoscience and Remote Sensing Symposium (IGARSS)
2016-November

Conference

Conference36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
國家China
城市Beijing
期間10/07/1615/07/16

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