SEISMIC VELOCITY PICKING BY GENETIC ALGORITHM

Abstract

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.