Title: SEISMIC VELOCITY PICKING BY HOPFIELD NEURAL NETWORK
Authors: Huang, Kou-Yuan
Yang, Jia-Rong
資訊工程學系
Department of Computer Science
Keywords: Hopfield neural network;Lyapunov function;seismic velocity picking
Issue Date: 2016
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.
URI: http://hdl.handle.net/11536/135252
ISBN: 978-1-5090-3332-4
ISSN: 2153-6996
Journal: 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
Begin Page: 3190
End Page: 3193
Appears in Collections:Conferences Paper