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dc.contributor.authorHuang, Kou-Yuanen_US
dc.contributor.authorChen, Kai-Juen_US
dc.contributor.authorYang, Jia-Rongen_US
dc.date.accessioned2015-07-21T08:31:21Z-
dc.date.available2015-07-21T08:31:21Z-
dc.date.issued2013-01-01en_US
dc.identifier.isbn978-1-4673-6129-3; 978-1-4673-6128-6en_US
dc.identifier.issn2161-4393en_US
dc.identifier.urihttp://hdl.handle.net/11536/125034-
dc.description.abstractWe adopt genetic algorithm (GA) for velocity picking in reflection seismic data. Conventional seismic velocity picking was to pick a series of peaks in a seismic semblance image (stacking energy) by geophysicists. However, it took human efforts and time. Here, we transfer the velocity picking to a combinatorial optimization problem. The local peaks in time-velocity seismic semblance image are ordered in a sequence with time first, then velocity. We define a fitness function including the total semblance of picked points, and constraints on the number of picked points, interval velocity, and velocity slope. GA can find an individual with the highest fitness value, and the picked points form the best polyline. We use simulation data and Nankai real seismic data in the experiments. We sequentially find the best parameter settings of GA. The picking result by GA is good and close to the human picking result. The result of velocity picking by GA is used for the normal move-out (NMO) correction and stacking. The stacking result shows that the signal is enhanced. This method can improve the seismic data processing and interpretation.en_US
dc.language.isoen_USen_US
dc.titleGenetic Algorithm for Seismic Velocity Pickingen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2013 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000349557200379en_US
dc.citation.woscount0en_US
Appears in Collections:Conferences Paper