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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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

Well log data inversion is important for the inversion of true formation. There exists a nonlinear mapping between the measured apparent conductivity (Ca) and the true formation conductivity (Ct). 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 Ct. 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 Ct' 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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

A nonlinear mapping exists between the measured apparent conductivity (Ca) and the true formation conductivity (Ct). 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 Ct. 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 Ca, then Ct' 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. [ABSTRACT FROM PUBLISHER]