Genetic algorithm for seismic velocity picking

Kou-Yuan Huang*, Kai Ju Chen, Jia Rong Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


We 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.

Original languageEnglish
Title of host publication2013 International Joint Conference on Neural Networks, IJCNN 2013
StatePublished - 1 Dec 2013
Event2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, TX, United States
Duration: 4 Aug 20139 Aug 2013

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Conference2013 International Joint Conference on Neural Networks, IJCNN 2013
CountryUnited States
CityDallas, TX

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