@inproceedings{e3bfbbc1c9c24758bcea938ebda249c8,

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",

month = sep,

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",

}