Browsing by Author "Yang, Jia-Rong"

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Genetic Algorithm for Seismic Velocity Picking
(2013-01-01)
Huang, Kou-Yuan
;
Chen, Kai-Ju
;
Yang, Jia-Rong
;
資訊工程學系
;
Department of Computer Science
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.
Hopfield Neural Network for Seismic Velocity Picking
(2014)
Huang, Kou-Yuan
;
Yang, Jia-Rong
;
資訊工程學系
;
Department of Computer Science
The 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.
SEISMIC VELOCITY PICKING BY GENETIC ALGORITHM
(2013-01-01)
Huang, Kou-Yuan
;
Chen, Kai-Ju
;
Yang, Jia-Rong
;
資訊工程學系
;
Department of Computer Science
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.
SEISMIC VELOCITY PICKING BY HOPFIELD NEURAL NETWORK
(2016)
Huang, Kou-Yuan
;
Yang, Jia-Rong
;
資訊工程學系
;
Department of Computer Science
The Hopfield neural network (HNN) is adopted for velocity picking in the time-velocity semblance image of seismic data. A Lyapunov function is generated from the velocity picking problem. We use the gradient descent method to decrease the Lyapunov function and derive the equation of motion. The Lyapunov function can reach the minimum. According to the equation of motion, each neuron is updated until no change. The linking of the converged network neurons represents the best polyline in velocity picking. We have experiments on simulated seismic data. The picking results are good. It can improve the seismic data processing and interpretation.
SEISMIC VELOCITY PICKING BY SIMULATED ANNEALING
(2012)
Huang, Kou-Yuan
;
Chen, Kai-Ju
;
Yang, Jia-Rong
;
資訊工程學系
;
Department of Computer Science
Velocity picking is to pick several peaks on seismic semblance image (stacking energy) that is very important in seismic data processing. In time-velocity semblance image, these chosen peaks are representing the relation of stacking velocity and time. In this study, we apply simulated annealing (SA) on seismic velocity picking problem and use two constraints in the energy function of SA algorithm for searching a best peak point combination. After obtaining the best polyline, we use the picking result to perform further seismic data processing including normal move-out (NMO) correction and common midpoint stacking. This method automates the velocity picking procedure with short period of time and improves the further seismic data interpretation.