TY - GEN
T1 - Seismic velocity picking by genetic algorithm
AU - Huang, Kou-Yuan
AU - Chen, Kai Ju
AU - Yang, Jia Rong
PY - 2013
Y1 - 2013
N2 - We use genetic algorithm (GA) of global optimization method for velocity picking in reflection seismic data. 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 that includes 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 maximum of fitness function and get the picked points to form the best polyline. We have Nankai real seismic data in the experiments. We use sequential method to 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.
AB - We use genetic algorithm (GA) of global optimization method for velocity picking in reflection seismic data. 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 that includes 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 maximum of fitness function and get the picked points to form the best polyline. We have Nankai real seismic data in the experiments. We use sequential method to 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.
KW - common midpoint (CMP) gather
KW - genetic algorithm
KW - normal move-out (NMO) correction
KW - seismic velocity picking
KW - sequential method
UR - http://www.scopus.com/inward/record.url?scp=84894249891&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2013.6723083
DO - 10.1109/IGARSS.2013.6723083
M3 - Conference contribution
AN - SCOPUS:84894249891
SN - 9781479911141
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1548
EP - 1551
BT - 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings
T2 - 2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013
Y2 - 21 July 2013 through 26 July 2013
ER -