Browsing by Author "Chen, Kai-Ju"

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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.
Hough transform neural network for pattern detection and seismic applications
(2008-10-01)
Huang, Kou-Yuan
;
Chen, Kai-Ju
;
You, Jiun-Der
;
Tung, An-Ching
;
資訊工程學系
Hough transform neural network is adopted to detect the line pattern of direct wave and the hyperbolic pattern of reflection wave in a one-shot seismogram. We use time difference from point to hyperbola and line as the distance in the pattern detection of seismic direct and reflection waves. This distance calculation makes the parameter learning feasible. One set of parameters represents one pattern. Many sets of parameters represent many patterns. The neural network can calculate the distances from point to many patterns as total error. The parameter learning rule is derived by gradient descent method to minimize the total error. The network is applied to three kinds of data in the experiments. One is the line and hyperbolic pattern in the image data. The second is the simulated one-shot seismic data. And the last is the real one-shot seismic data. Experimental results show that lines and hyperbolas can be detected correctly in three kinds of data. The method can also tolerate certain level of noise data. The detection results in the one-shot seismogram can improve the seismic interpretation and further seismic data processing. (C) 2008 Elsevier B.V. All rights reserved.
Hough transform neural network for seismic pattern detection
(2006-01-01)
Huang, Kou-Yuan
;
You, Jiun-De
;
Chen, Kai-Ju
;
Lai, Hung-Lin
;
Dong, An-Jin
Hough transform neural network is adopted to detect line pattern of direct wave and hyperbola pattern of reflection wave in a seismogram. The distance calculation from point to hyperbola is calculated from the time difference. This calculation makes the parameter learning feasible. The neural network can calculate the total error for distance from point to patterns. The parameter learning rule is derived by gradient descent method to minimize the total error. Experimental results show that line and hyperbola can be detected in both simulated and real seismic data. The network can get a fast convergence. The detection results can automatically provide a reference and improve seismic interpretation.
Hough transform neural network for seismic pattern detection
(2006-01-01)
Huang, Kou-Yuan
;
You, Jiun-De
;
Chen, Kai-Ju
;
Lai, Hung-Lin
;
Don, An-Jin
Hough transform neural network is adopted to detect line pattern of direct wave and hyperbola pattern of reflection wave in a seismogram. The distance calculation from point to hyperbola is calculated from the time difference. This calculation makes the parameter learning feasible. The neural network can calculate the total error for distance from point to patterns. The parameter learning rule is derived by gradient descent method to minimize the total error. Experimental results show that line and hyperbola can be detected in both simulated and real seismic data. The network can get a fast convergence. The detection results can improve the seismic interpretation.
Multilayer Perceptron Learning with Particle Swarm Optimization for Well Log Data Inversion
(2012)
Huang, Kou-Yuan
;
Shen, Liang-Chi
;
Chen, Kai-Ju
;
Huang, Ming-Che
;
資訊工程學系
Well log data inversion is important for the inversion of true formation. There exists a nonlinear mapping between the measured apparent conductivity (C-a) and the true formation conductivity (C-t). We adopt the multilayer perceptron (MLP) to approximate the nonlinear input-output mapping and propose the use of particle swarm optimization with mutation (MPSO) algorithm to adjust the weights in MLP. In the supervised training step, the input of the network is the measured C-a and the desired output is the C-t. MLP with optimal size 10-9-10 is chosen as the model. We have experiment in simulation and real data application. In simulation, there are 31 sets of simulated well log data, where 25 sets are used for training, and 6 sets are used for testing. After training the MLP network, input Ca, then C-t' can be inverted in testing process. Also we apply it to the inversion of real field well log data. The result is acceptable. It shows that the proposed MPSO algorithm in MLP weight adjustments can work on the well log data inversion.
MULTILAYER PERCEPTRON WITH GENETIC ALGORITHM FOR WELL LOG DATA INVERSION
(2013-01-01)
Huang, Kou-Yuan
;
Shen, Liang-Chi
;
Chen, Kai-Ju
;
Huang, Ming-Che
;
資訊工程學系
Two-layer multilayer perceptron (MLP) learning by genetic algorithm (GA) is used to approximate the nonlinear mapping between the input and the desired output. The GA is a global optimization method that can avoid the local minimum during the training in MLP and is implemented in binary and real number calculations. We have experiments on 31 simulated well log data and real data application. In the supervised training step, the input of the network is the apparent conductivity (Ca) and the desired output is the true formation conductivity (Ct). The best size of two-layer MLP is chosen as 10-9-10 by theorem and experiments. And we get the best parameters of binary GA and real number GA by sequential method. After getting the best MLP network in training, the corresponding true formation conductivity can be inverted for each input Ca pattern in testing process. From comparison of errors in experiments of simulated data, the real number GA has less error than that of binary GA. That is because the bit string in binary GA limits the range of weighting coefficient and has higher error. We also apply the best 10-9-10 MLP model to the inversion of real field well log data. It shows that this method can work on well log data inversion and is feasible.
MULTILAYER PERCEPTRON WITH PARTICLE SWARM OPTIMIZATION FOR WELL LOG DATA INVERSION
(2012)
Huang, Kou-Yuan
;
Chen, Kai-Ju
;
Huang, Ming-Che
;
Shen, Liang-Chi
;
資訊工程學系
A nonlinear mapping exists between the measured apparent conductivity (C-a) and the true formation conductivity (C-t). We adopt the multilayer perceptron (MLP) to approximate the nonlinear input-output mapping and propose the use of particle swarm optimization with mutation (MPSO) algorithm to adjust the weights in MLP. In the supervised training step, the input of the network is the measured Ca and the desired output is the C-t. MLP with optimal size 10-9-10 is chosen as the model. We have experiments in simulation and real data application. In simulation, there are 31 sets of simulated well log data, where 25 sets are used for training, and 6 sets are used for testing. After training the MLP network, input C-a, then C-t' can be inverted in testing process. Compared with radial basis function (RBF) networks and particle swarm optimization (PSO) method, the error of MPSO is the smallest. Also we apply it to the inversion of real field well log data. The result is acceptable. It shows that the proposed MPSO algorithm in MLP weight adjustments can perform the well log data inversion.
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 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.
Simulated annealing for pattern detection and seismic application
(2007)
Chen, Kai-Ju
;
Huang, Kou-Yuan
;
資訊工程學系
;
Department of Computer Science
Simulated annealing algorithm is adopted to detect the parameters of lines, circles, ellipses, and hyperbolic patterns. We define the distance from a point to a pattern such that the detection becomes feasible, especially in hyperbola. The proposed simulated annealing parameter detection system has the capability to find a set of parameter vectors with global minimal error to the input data. Using average minimum distance, we propose a method to determine the number of patterns automatically. Experiments on the detection of lines, circles, ellipses, and hyperbolas in images are quite successful. The detection system is also applied to detect the line pattern of direct wave and the hyperbolic pattern of reflection wave in the simulated one-shot seismogram. The results are good and can improve seismic interpretations and further seismic data processing.
Simulated Annealing for Pattern Detection and Seismic Applications
(2009-05-01)
Huang, Kou-Yuan
;
Chen, Kai-Ju
;
資訊工程學系
;
Department of Computer Science
Simulated annealing (SA) is adopted to detect the parameters of circles, ellipses, hyperbolas, and to treat lines as asymptotes of hyperbola in image, Also, the algorithm is applied to seismic pattern detection. We use the general equation for ellipses and hyperbolas in detection and define the distance from a point to a pattern such that the detection becomes feasible. The system error between N points and K patients is defined. The proposed simulated annealing parameter detection system has the capability of searching a set of parameter vectors with global minimal error with respect to the input data. Experiments oil the detection of circles, ellipses, hyperbolas, and lines in images are quite successful. The detection system is also applied to detect the line pattern of direct wave and the hyperbolic pattern of reflection wave in the simulated and real one-shot seismogram. The results Can improve seismic interpretations and further seismic data processing