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dc.contributor.authorHuang, Kou-Yuanen_US
dc.contributor.authorYang, Jia-Rongen_US
dc.date.accessioned2017-04-21T06:48:52Z-
dc.date.available2017-04-21T06:48:52Z-
dc.date.issued2014en_US
dc.identifier.isbn978-1-4799-1484-5en_US
dc.identifier.issn2161-4393en_US
dc.identifier.urihttp://hdl.handle.net/11536/135056-
dc.description.abstractThe 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.en_US
dc.language.isoen_USen_US
dc.subjectHopfield neural networken_US
dc.subjectseismic velocity pickingen_US
dc.subjectsemblance imageen_US
dc.subjectLyapunov functionen_US
dc.subjectequation of motionen_US
dc.titleHopfield Neural Network for Seismic Velocity Pickingen_US
dc.typeProceedings Paperen_US
dc.identifier.journalPROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)en_US
dc.citation.spage1146en_US
dc.citation.epage1153en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000371465701034en_US
dc.citation.woscount0en_US
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